Hi Team,

We’re practically in October… so close to BFCM I can smell it.

I read a post this week from Sean Frank, Ridge’s CEO, that stuck with me. He said revenue would fall 70% if he took his foot off the gas. That’s a hell of a dependency to have.

His point is that selling a wallet today doesn’t automatically help sell the next one. You still have to find and convince another customer, without the network effects you get in some other businesses.

I wouldn’t pretend a better support team could make that dependency disappear. But it did make me wonder how much we spend acquiring customers we then leave to figure out the product themselves. By the time they need something else, we’re back in the auction trying to convince them again.

With BFCM coming, I think that deserves a little attention.

Let’s get into it.

This week’s newsie is brought to you by my friends at Horatio!

Seasonal hiring is a lot more expensive than it looks on paper.

If you’ve ever built out a temporary team for peak season, you know the drill: recruiting on a tight timeline, background checks, training people up fast, and then someone on your team taking on the extra work of managing it all.

And just as everyone is fully ready? Peak season’s over, the team’s gone, and you’re starting from scratch next year.

Once you add up the real cost per agent (recruiting, training, management, churn, and all the little hidden costs) that “budget-friendly” seasonal team isn’t looking so budget-friendly anymore.

Wondering how your setup stacks up? See exactly where you stand with Horatio’s side-by-side comparison chart. And if you’re curious what a hybrid model could look like for your specific order volume, their team’s happy to help you map it out.

👉 See the Real Difference

The first order should help earn the next one

Ridge has built a huge business by getting very good at finding the next customer. Sean’s point is that the work keeps coming: selling a wallet today doesn’t create a network effect that brings tomorrow’s buyer along.

A wallet that lasts for years also shouldn’t need replacing every few months. The next sale might be a gift, another product, or a friend buying because someone they trust recommended it.

We spend a lot of money getting someone to believe the product page. Once they buy, we seem considerably less interested in whether they still believe it.

If you want the wallet customer to consider your other categories, the first purchase needs to give them a reason to trust you with another one. Knowing how to use it, getting a question answered, and finding out whether the guarantee is any use when something breaks all contribute to that decision.

Those moments can also give someone a reason to look elsewhere, without ever opening a support ticket.

The questions your support team never hears

Imagine a customer buying a $160 pair of boots. The ad says they get better the longer you wear them. The product page says they’re built to be resoled instead of replaced.

They arrive in a plastic bag inside a plain box, with no information about break-in or care. He wears them Monday, and by Wednesday they’re hurting his feet. He doesn’t know whether they need time, whether he bought the wrong size, or whether these particular boots just aren’t for him.

So Thursday night he types “do leather boots hurt this much at first” into his phone and spends twenty minutes on a subreddit where four strangers explain his own purchase to him. Two of them recommend a different brand, which is a generous little acquisition service for your competitor.

He had a question about your product, and somebody else was there to answer it.

Later, when the heels wear down, he remembers the resole promise. The link is in the footer and opens a form asking for proof of purchase he can’t find. Shipping is on him, and the turnaround is six weeks.

Six weeks without the boots he wears every day is a fairly compelling reason to buy another pair. He closes the tab and keeps wearing them until he’s ready to replace them.

When that day comes, he opens three tabs. One of them is the brand from that thread.

Nobody escalated anything, nobody asked for a refund, and your CSAT for the quarter is lovely. He never created a ticket, so your support report has nothing to say about any of this. You paid to acquire him, then left him to work out the ownership part with strangers.

A care guide could have explained break-in and when to ask for help with the fit. An order lookup could have spared him the receipt hunt. And if resoling is a reason to buy the boots, I’d want the cost and turnaround visible before checkout, with a process worth using when the time comes.

Nobody puts a car fire in the annual plan

The first purchase can also give people something worth sharing. In her reply to Sean, Alex points to Crocs and Stanley as examples of the brand heat Ridge could try to build, while acknowledging how much riskier that is than a familiar, data-driven acquisition playbook.

You probably remember the Stanley video: a woman’s car burns, her Quencher is still in the cupholder, and there’s still ice inside it. The president responds on TikTok and offers to replace her car.

The product got an absurdly good demonstration of its durability, entirely by accident. Then the company gave people a second reason to talk about it. Replacing the cup would have been understandable. Offering to replace the car made the response part of the story.

It’s a maddening example if you’re trying to plan for it. You can put a media budget in a spreadsheet and argue about the assumptions. Nobody can promise you a Stanley moment in next year’s plan.

I would, however, want to know whether my company could respond to one before the internet moved on. If replacing a damaged order requires three approvals, I have some concerns about our readiness to buy someone a car.

Someone has to notice, someone has to be able to make a call, and the response has to arrive while people still care. Oh, and it doesn’t always need to be a goddamn car. There are much quicker, cheaper ways to do this.

Most brands will never get a Stanley moment. They will get a customer trying to use a repair promise, a gift that arrives damaged, or a question the product page never answered. Those are less spectacular opportunities to give someone a story worth repeating, and they turn up without requiring anyone’s car to catch fire.

I’d happily keep funding the acquisition machine while working on this. I’d also want someone responsible for making sure the experience we’re selling holds up after delivery. Otherwise we’re paying to bring more people into the same disappointing process and wondering why nobody recommends us.

What I’d actually fix before BFCM

I’d work through four things before sending another wave of first-time customers into the same experience:

• Buy and return your own product. Use a new email, pay normally, and give nobody an internal heads-up. See what the customer has to figure out without the benefit of working at your company.

• Find three unanswered first-month questions. Ask your support team, read reviews, then search the questions yourself. If Reddit explains how to use your product better than you do, you have some fairly obvious homework.

• Compare early reviews with later ones. Someone who opened the box yesterday can tell you about delivery and first impressions. Someone who’s used the product for months can tell you what held up, what became annoying, and what they wish they’d known.

• Try using your guarantee. Follow the actual steps. Check the cost, turnaround, and information required. If the customer has to find a receipt your team could look up in thirty seconds, fix that before writing another paragraph about how much you care.

Then I’d check what the rep can do when one of these problems reaches them. If the customer clearly qualifies for a repair, can the rep arrange it? If the case needs judgment, is there someone available to make the call? A good policy is considerably less useful when everyone is waiting for the one person who’s allowed to apply it.

Before BFCM, I’d put the product page next to the experience of actually owning the thing and work through every place they disagree, from the care instructions and repair process to the person who can make a decision when something goes wrong.

You’ve already paid to persuade this customer once. When someone asks them where to buy boots, I’d like them to recommend the pair they own, with a better reason than “the ads were good.”

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,
Eli 💛

Hi team,

In case you missed it, the normies are getting AI agents. Maybe not your mom or my mom just yet, but this is getting considerably closer to people whose weekend plans don’t involve the words GitHub commit.

Between Instinct, Grok Bot, and Meta launching Muse, more people will have software that can actually handle something for them while they go live their lives.

If you think “using ai” is asking ChatGPT to rewrite an email, the world is moving fast. These agents can work across connected accounts and browsers, keep going after you step away, and, depending on the product, communicate through text or WhatsApp. Muse and Instinct can even make purchases through Stripe’s Link.

I’ve been using instinct for a few weeks, but in the last week or so, I have had a few experiences that made me wonder how drastically CX will change in the short term.

“Have your agent talk to my agent” is becoming a fairly reasonable household plan.

Let’s dive in.

This week’s newsie is brought to you by my friends at Horatio!

Customer retention isn’t won when things go perfectly. It’s won in the 48 hours before a disruption hits.

We all know the math: a site outage, an inventory glitch, or a shipping delay is statistically going to happen in Q4. But customer sentiment doesn’t drop because of the error; it drops exponentially because of silent errors. If you wait for the tickets to explode before you say something, you’ve already lost the LTV. Winning brands use proactive, omnichannel customer warnings to head off the chaos before it starts.

Horatio’s Human + AI 24/7 Coverage natively plugs into your existing tech stack (Gorgias, Zendesk, Gladly, Kustomer, Fin…you name it!) to flag issues and communicate with customers before your inbox drowns.

Check out their dedicated BFCM hub to map out your proactive strategy today.

👉 Secure Your Trust Window

My dryer has representation now

Our dryer broke, and we had a warranty through Asurion. I gave Instinct the task of getting the claim handled, along with a photo of the appliance sticker.

It found last year’s receipt in my email, went to the warranty website, and encountered the requirement to file claims over the phone.

I have children and a job, so discovering that an appliance requires a scheduled conversation is generally where my enthusiasm for owning a warranty starts to deteriorate.

Instinct went into live chat and worked through it until the claim was accepted, the repair scheduled, and written confirmation arrived that there would be no service fee.

It even attached a screenshot of chat.

As a customer, I was delighted. As someone in CX, I started thinking about all the requests people never bother making.

Brands forecast support demand using behavior that already includes people giving up.

The small charge you never disputed, and the return you abandoned after discovering you needed a printer never became tickets. Those problems existed; your willingness to spend an afternoon on them did not.

Naturally, I started looking for other things I could hand over.

Save 15% or More on Your Car Insurance

I gave Instinct access to my Geico account, and it pulled my coverage, shopped around for the same coverage, and sent me a ready-to-pay quote at Allstate for same coverage that was priced 24% lower. That will save me $1,005 per year.

I understand that shopping around for insurance has always been possible. I also understand that I have spent years being technically capable of doing it while finding almost anything else to do instead.

There’s a meaningful amount of money sitting inside tasks people keep postponing. Once someone else can do the comparison shopping, more of those tasks become worth pursuing.

Agent, Pls Plan Date with My Wife’s Agent

I know, this is bleak… but Instinct also announced a Trusted Person network, where your agent can coordinate with the agents of people you’ve connected with. One possible use is having my agent talk to my partner’s agent to arrange a date night.

Having two virtual assistants negotiate when we can eat dinner together feels like peak Silicon Valley, although I would like to hear their position on finding a babysitter before dismissing the concept.

That feature is restricted to a trusted network. It doesn’t mean my agent can already contact every brand’s agent. But the coordination itself is becoming a product feature, which makes “have your agent talk to my agent” considerably less of a bit.

Check This Flight Every Morning

I was looking at flights to TLV for a wedding in November, and the round trip was $2,000. I asked Instinct to check daily and book if the exact same itinerary dropped below $1,400.

A week later, I received a United confirmation for $1,350.

Now my bot is checking my hotel reservation’s price every night, with instructions to cancel and rebook if a lower rate appears before my free-cancellation window closes.

I used to have time for this kind of thing. These days, remembering that I booked the hotel feels like a reasonable contribution.

From the hotel’s side, though, one reservation could now produce several changes without the guest spending their evenings refreshing a booking page.

Jevons Had a Point

In 1865, economist William Stanley Jevons argued that more efficient steam engines could increase Britain’s coal consumption. Getting more work from each ton made steam power cheaper and useful in more places, expanding demand enough to overwhelm the savings per engine. His original explanation is considerably older than anyone’s AI strategy.

AI creates the same possibility: as individual tasks become cheaper and easier, we find more things worth asking it to do. Checking a flight once becomes checking it every morning. Shopping for insurance graduates from something I should probably do to something that actually happens.

My bet is that support will experience its own version of this.

Think about the small charge you never disputed or the return you abandoned after discovering you needed a printer. Those problems existed, even though your willingness to pursue them expired somewhere between the instructions and dinner.

Brands forecast support demand using behavior that already includes people giving up.

An agent lowers the effort required to pursue those requests. Meanwhile, the brand is (hopefully) using at least some AI to make each interaction cheaper. Your cost per contact could fall while the number of contacts makes the original forecast look rather optimistic.

That doesn’t mean every queue explodes. A hotel rebooking may never generate a support ticket, and a properly resolved claim could eliminate several follow-ups.

It does mean I’d be deeply suspicious of a savings forecast that automates the brand’s side while leaving customer behavior conveniently frozen.

They’ll Still Need Permission to Do Something

My warranty experience involved Instinct working through the support chat. A future version could involve my agent contacting your agent with the receipt, order history, and relevant policy already available.

If your system can check eligibility and complete the appropriate action, that could be a great experience.

If it can only explain the policy and request patience, your team could end up overseeing two pieces of software exchanging increasingly polished messages about a refund neither has permission to issue. A tremendous moment for everyone selling tokens.

Someone still has to decide what can be approved and when a human needs to get involved. A vague response that previously ended the conversation because the customer got busy may now receive a follow-up.

What I Would Do Today

If I were running CX today, I’d test a few controlled requests against our own support experience using a personal agent. I’d want to see whether an eligible request reaches a resolution, whether an ineligible request gets an accurate explanation, and where either one gets stuck waiting for permission.

Then I’d watch completed resolutions and repeat contacts alongside cost per conversation. Handling messages cheaply won’t tell me whether the underlying work is getting finished.

I’m genuinely excited about handing over more of this admin. As a customer, I’ve already found several reasons to keep going.

If I were on the other side of those conversations, I’d leave some room in the forecast for everyone else discovering the same thing.

Have you handed an agent a customer service problem yet? I’d love to hear what happened, particularly if it got further than you expected.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,
Eli 💛

Hi team,

I started tinkering with AI (then fondly called artificial intelligence, its full government name) in 2019 after my friend Zee pushed me to try it. I managed to make my computer sound like it was about to take off, took a break, and got pulled back in when ChatGPT launched in 2022.

I’m a normie. I don’t have a Mac Mini farm, Hermès, etc. I have kids, a calendar I do not fully control, and an alarming number of personal projects in various stages of “almost done.”

I even recently joined letsgo.co as a mentor and have had some genuinely fun conversations helping people automate the most monotonous parts of their day. Once you start looking closely at the work you keep repeating, you find plenty to experiment with.

A lot of the CX folks I speak with still feel behind, though. They’re unsure where to start, and much of what they’ve seen involves AI answering customer tickets, often poorly. I get why that hasn’t made them particularly excited to hand over more of their day.

So I put together four downloadable skills for problems you already understand well enough to have an opinion about the answer.

I think they’ll be genuinely helpful, and would love to hear your pov if you use them!!

Let’s jump into it.

This week’s newsie is brought to you by Kim.cc

I was on a webinar with the Kim team recently, and we spent a fair amount of time talking about where AI should probably stop making decisions on its own.

I’m obviously not anti-AI. I’m about to give you four AI skills to download.

But customer support gets messy very quickly.

A tracking question is one thing. A customer who has ordered six times, received the wrong product, wants a refund that technically falls outside policy, and is now threatening to leave is something else entirely.

Those are the conversations where “the AI followed the policy” is not necessarily the same thing as “the customer got the right outcome.”

And BFCM is a particularly unpleasant time to discover where those gaps are.

You probably aren’t going to replace your helpdesk, rebuild your workflows, retrain your team, and introduce an entirely new support operation a few weeks before peak. I wouldn’t recommend adding that to anyone’s November calendar either.

So Kim is doing something a little different for readers of this newsletter.

They’re picking 25 brands and helping them predict where their current support operation is most likely to break during BFCM.

They’ll look at things like your expected ticket volume, team capacity, current automation, common ticket types, and the places where human judgment still matters, then walk you through where they think the cracks are likely to show up once volume increases.

No requirement to change your stack. Just a chance to stress-test the setup you already have before your customers do it for you.

If you want them to take a look, you can book a 20-minute BFCM Support Stress Test here:

Stress-test my BFCM support →

Skill #1: CX Site Auditor

TL;DR: Reviews your customer-facing pages and messages for conflicting promises and unanswered questions, then suggests fixes and likely owners.

Use when: You want a “stranger” to do a deep dive on your site and audit where you perhaps are making promises you can not keep, or saying different things at different parts of the journey that don’t align.

Start with your store URL, then add emails or pages it can’t access. I’d include the order confirmation, since the customer reading that one has already given you money and would quite reasonably like to know what happens next.

When you get the findings, check the actual passages. “Ships in three days” and “arrives in three days” are different promises, and customers shouldn’t need a working knowledge of your fulfillment operation to decode them.

I’d pick one clear issue, figure out which promise the business can keep, and check everywhere else it appears before changing it. Otherwise, you fix the FAQ and leave the confirmation email confidently telling a different story.

I used this on a beauty brand and it found massive discrepancies on shipping timelines and promises, charging for returns in one place and on FAQs saying returns are free, and much more. It found all of this in under 60 seconds on ChatGPT using this skill.

Download CX Site Auditor

Skill #2: Ticket Second Opinion

TL;DR: Reads a customer conversation alongside your policy, recommends the next action, drafts the reply, and prepares an approval request if needed.

Use when: A ticket has gone back and forth and someone is about to ask for the photo the customer already sent twice.

Give it the conversation and the relevant policy. Then read the recommendation before getting distracted by how nicely it wrote the email.

Did it catch what the customer already provided? Does the proposed action address what they’re asking for now? Is it promising something the rep can actually do?

I’d be perfectly happy agreeing with its recommendation and rewriting the reply myself. Sometimes another read of the thread is useful. You can keep your own way of talking to customers, particularly when the customer has already spent enough time wondering whether anyone is reading what they send.

Download Ticket Second Opinion

Skill #3: Macro Reality Check

TL;DR: Reviews your macros for what the customer still won’t know after reading them, separating wording edits from decisions your team needs to make.

Use when: “Keep an eye on tracking” has somehow become your entire response to a package that hasn’t moved.

Try a macro you use constantly and one you think is already good. I’d be interested in whether it can leave the good one alone. We have enough work without a tool discovering that every sentence needs its personal attention.

For each finding, ask which customer question the suggested change answers. Perhaps the next step is buried, or the timeline needs explaining. Perhaps nobody has decided when the rep can offer a replacement, and the macro has been politely avoiding that subject.

That last finding belongs with whoever owns the policy. Get the decision made before putting a new promise into a reply your team sends all day.

Download Macro Reality Check

Skill #4: Escalation Auditor

TL;DR: Reviews escalation records against your frontline authority policy and identifies patterns worth discussing with your team.

Use when: You suspect reps spend too much time asking permission and want evidence before changing what they can approve.

It starts with the columns you supply and should tell you where missing information limits the analysis. You’ll also need the policy that explains what frontline reps are currently allowed to do.

One thing I’d watch closely: a manager agreeing with the rep’s recommendation doesn’t automatically mean the approval was unnecessary. They may have checked something the export doesn’t capture.

Pull a few of those cases and ask what happened. What did the rep already know? What did the manager verify? Could that information have been available earlier?

You might find a permission worth changing, guidance worth clarifying, or an approval step worth keeping. I’d want that conversation before announcing to the team that a spreadsheet has reorganized their responsibilities.

Download Escalation Auditor

How to use them

For conversation-based use in Claude or ChatGPT:

1. Download the skill you want to try.

2. Attach SKILL.md to a new conversation.

3. Send: “Follow the attached skill instructions. Tell me what information you need from me before starting.”

4. Supply the relevant material: your store URL, macros, a redacted ticket and policy, or escalation records and your frontline authority policy.

Start with something manageable that you know well enough to judge. Your public site or a macro without customer information is an easy place to begin. Before uploading company material, remove customer information locally and follow your company’s AI policy.

Read the output before acting on it, please. These tools recommend and draft, and your team still makes the decisions.

I’d genuinely love to hear which one you try, what it catches, and where it misses something obvious. If you have to explain a detail it should have asked about, tell me that too. That’s useful feedback for making these better.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

The 20% Coupon Made It Worse

Hey team,

For starters, this is newsletter #200, which feels insane??? 200 weeks of writing this thing, and it means the world that some of you have been around since day one.

Some of my favorite newsletters over the years have come from random customer experiences I’ve had. The good, the ugly, and the lesson that I learned from them.

This week gave me both: one genuinely impressive recovery experience, and one AT&T experience that made me want to toss my computer through a brick wall.

Let’s jump into it.

This week’s newsie is brought to you by my friends at Horatio!

You-seriously-have-to-read-it BFCM Playbook

You guessed it: revenue-focused. Most brands approach BFCM with a defensive mindset: SURVIVE the queue, and that’s a massive missed revenue opportunity.

When a shopper reaches out during the biggest shopping weekend of the year, you know they are a high-intent buyer holding their wallet (not just a ticket). If your team is playing whack-a-mole with macros, you’re losing money.

I’ve partnered with the team at Horatio to compile real operational frameworks from top-tier scaling brands.

We’ve turned them into an actionable, plug-and-play manual to shift your support from a cost center to a revenue-generating engine.

Don’t try to win BFCM alone. Download the ultimate playbook today, steal the exact operational frameworks top e-commerce brands use, and see if you qualify for a complimentary 15-minute operational capacity audit with their team (give my name and they’ll prioritize you).

👉 Download The Free BFCM CX Playbook

The Tree Saga

I ordered two trees in the mail this week, which is apparently where I am in life now.

A prairie crabapple and a kousa dogwood. Both were shipped in one box, which showed up a few days late, and the prairie crabapple arrived snapped in half.

Generally not the preferred condition for a tree.

Honestly, I get that things can break during shipping; carriers do carrier things. The annoying part was that the entire convenience argument disappeared immediately. I paid to avoid going to the local Lowe’s, waited longer than expected, received a broken tree, called support, and then will have to wait another week and a half for the thing I should have received the first time correctly.

They had phone support, which already put them ahead of half the internet. A person answered, “activated the warranty,” told me the tree would be reshipped, and said someone would follow up.

Operationally, that was the correct first answer. The replacement was moving, the warranty process was triggered, and nobody made me explain the concept of “snapped in half” like I was presenting evidence in a small claims case.

I was still annoyed because the ticket action and the customer frustration were not the same thing. The rep solved the mechanical part of the issue, while the bigger frustration was still sitting there next to my broken tree.

I had lost time, trust, and the entire convenience argument that made me order from them in the first place. How do I even dispose of this neutered tree youngin??

Then came the make-good: 20% off my next order.

That would have landed better if the homepage didn’t already have 20% off sitewide (and ofc, you can’t combine discounts.)

So I asked to escalate, which felt ridiculous and also completely reasonable. The tree was snapped in half.

The Weekened Call Nobody Was Contractually Obligated to Make

The next morning, on a weekend, someone from the CX leadership team called me. They had already refunded 30% of the order. They apologized like a person. They acknowledged the packing issue. They talked through what they could learn from it. They said they would follow up again once the replacement arrived.

That changed the experience.

The original order still failed, and I still had to wait for the replacement. The company did not magically un-snap the tree. The difference was that the escalation finally matched the inconvenience. It added ownership, speed, a real concession, and some accountability for what happened next.

Then, because the CX gods apparently wanted to make the lesson extremely obvious, I had the opposite experience with AT&T.

Page 4, under “News You Can Use”

AT&T still remains undefeated.

My bill went up $15 a month. I found out the way most people find out these things, by looking at a number that felt wrong and going to check. I called, and the rep’s entire explanation was “all plans went up.”

So I filed a BBB complaint, because apparently that’s still a lever that works in 2026, and it escalated all the way to the president’s office.

I want you to sit with that for a second. It took filing a formal complaint with a consumer protection nonprofit to get anyone at AT&T to engage with a $15 pricing question.

And the final answer, from the president’s office, delivered like it was a gotcha, was to check page 4 of my bill, under a section literally titled “news you can use.”

“News you can use” is doing an enormous amount of unpaid legal labor in that document. It is a section designed to be technically true and practically invisible, four pages into a bill nobody reads cover to cover, in a font size that assumes you have both excellent eyesight and nothing better to do on a Tuesday.

Escalation is where the company shows its hand

A lot of brands think escalation means the customer got moved to someone with a better title.

Manager, senior specialist, executive office, retention team. Whatever internal label makes everyone feel like the situation is being handled.

That part barely matters if the person on the other end is only there to repeat the first answer with cleaner grammar.

The useful version of escalation starts with a better question: what did the first answer fail to fix?

In the tree situation, the first rep handled the warranty. That needed to happen. The replacement had to get moving. The escalation handled the part the warranty did not touch: the lost time, the bad packing, the inconvenience, and the fact that I paid for a simpler experience and ended up with a broken product plus another week-plus of waiting.

AT&T did the opposite. The first rep said there was nothing they could do. The executive office called to explain why there was still nothing they were going to do, now with a page number. 🙃

That is a more expensive and high-touch way to annoy someone.

What the complaint was evidence of

The tree company treated the snapped trunk as evidence that something in their process failed. The packaging was wrong, the shipment arrived late, the product showed up broken, and the customer was now doing work he paid to avoid.

They did not treat my annoyance like a mood to manage, rather a signal that the recovery needed to be bigger than a warranty replacement.

AT&T treated my complaint like a customer education problem.

The subtext of “check page 4” was pretty clear: you should have read this already. The price increase existed somewhere in the bill, which meant the company felt covered, and the rest of the call was basically a guided tour through why I had no case.

That is the difference I’d care about heading into BFCM.

Support teams are going to get a lot of complaints over the next few months that are technically messy:

• Promo codes that worked exactly as designed and still confused people

• Shipping cutoffs that were listed somewhere and still missed

• AI answers that technically resolved the ticket and still created a second, angrier ticket

• Customers who were warned in the terms and still feel misled because nobody normal reads terms like bedtime material

The question is what your company thinks those complaints are evidence of.

If every complaint is treated like a customer misunderstanding, your team will spend peak season explaining, defending, and pointing people to the place where the company technically said the thing.

If the complaint is treated like a signal, your team can actually learn from it.

• The promo language was confusing

• The shipping promise created the wrong expectation

• The AI flow closed tickets too early

• The make-good did not match the miss

• The escalation path had no authority behind it

Before BFCM, I’d pull the moments where your team is technically right and customers are still annoyed. Reopened tickets, escalations, promo confusion, AI-resolved conversations that came back, and high-value customers who got the same answer as everyone else.

Then ask what those complaints are being treated as.

• A nuisance to close

• A misunderstanding to correct

• A signal to fix the system

Those three paths create very different customers.

Being right on page 4 does not beat being useful on day one.

And lastly, download the BFCM CX Playbook and review your experience one more time.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,
Eli 💛

Hey team,

We are back in the US after a week of gallivanting through Sicily and Malta, which was beautiful, chaotic, and a helpful reminder that vacation with kids is mostly parenting with extra carbs.

Feeling inspired, slightly tired, and very ready for childcare so I can catch a breather.

Over the last few weeks, I’ve gotten some version of the same question a few times:

Where should I use AI in CX, and where should I avoid?

Most teams start with “what can we automate?” which sounds practical until order tracking, refund exceptions, angry VIP customers, damaged birthday gifts, and cancellation saves all get dumped into the same giant bucket called “support.”

That is how brands get into trouble. AI is great at reading the pile, spotting patterns, pulling context, routing work, and drafting the boring stuff. The wheels come off when it starts making promises, denying refunds, apologizing to angry customers, or deciding whether a long-time customer deserves an exception.

BFCM makes this worse because volume hides bad judgment. A weak AI resolution in August is annoying. The same weak resolution in November gets multiplied across thousands of tickets while everyone is staring at response time, deflection, and queue size like those numbers tell the whole story.

They don’t.

So this week: five CX jobs AI should probably own already, and five I’d keep it far away from for now.

Let’s get into it.

This week’s newsie is brought to you by Kim.cc

BFCM doesn’t create new support problems. It exposes the ones you already had.

The tickets your AI half-resolves in October come back at 2-3x volume in November, on the most expensive week of your year.

A closed ticket isn’t necessarily a resolved ticket, and when the first answer doesn’t land, the brand pays twice: once for the AI interaction, again for the human cleanup. Tickets closed by AI carry an average re-open rate of 50%+. Going into peak, that’s a bill you haven’t seen yet.

Here is an interesting approach that 200+ Shopify brands are taking for this BFCM season: Kim.cc. An AI-native customer support service for ecommerce.

AI handles the volume and Sentinels, trained CX, QA, and prompt experts, own the outcome: they implement the system, step in where judgment is needed, and teach the AI how to handle the hard ones via proper prompt engineering so the same ticket doesn’t come back next time.

This way more issues are resolved the first time, fewer customers coming back (because suboptimal resolutions received by standard AI tools in the market), and an operation that gets sharper as volume climbs instead of buckling under it.

This BFCM brands should really try to look at the problem differently, i.e, the number that matters isn’t how many tickets close. It’s how many stay closed.

You can now book a pre-BFCM re-open rate teardown. This team is pulling a sample of your AI-resolved tickets and show you your real resolution rate — and where peak volume will hurt — before the rush hits.

You can book a 20-min call with their CX solutions expert to get your analysis done:

Learn More About Kim.cc

First, stop worshipping deflection rate

Deflection rate is one of those metrics that looks great in a board deck and gets much weirder the second you read the actual conversations.

Leadership loves it because it is clean “cost savings.” A ticket did not reach a human, the customer got some kind of answer, and the dashboard moved in the right direction. Everyone gets to feel like the operation got more efficient, which is a fun feeling until you remember the customer may have hated every second of it.

A ticket can close cleanly while the customer decides they are done with you.

I remember reading a story about Air Canada slop a few years back. Its chatbot gave a grieving customer bad information about bereavement fares. The company tried to argue the chatbot was responsible for its own actions, which is an incredible attempt at putting a name tag on the Roomba and sending it to court. The tribunal did not go for it.

Then there was the Chevrolet dealership chatbot that got prompt-injected into agreeing to a 2024 Tahoe for $1. Obviously nobody got a Tahoe for a dollar, but the point is still useful. The bot was allowed to speak in a place where the words had financial and reputational weight, and nobody had built a strong enough fence around what it was allowed to say.

AI should help your team see more, move faster, and spend less time doing soul-crushing admin work. It should not be handed the moments where money, emotion, judgment, or public embarrassment are on the table.

1. AI should tag and categorize tickets

This is boring, which is usually a good sign.

Most helpdesks have tags that look like they were built by five teams, two agencies, one intern, and someone who left the company in 2021 but apparently had very strong opinions about capitalization.

AI should clean that up. It should tag tickets by issue type, product, urgency, sentiment, customer type, subscription status, and root cause. It should turn the inbox from “general chaos with search functionality” into something your team can learn from.

No customer sees this or gets offended. Nobody screenshots your bot being weird. Your team just gets better data and fewer tags named “misc-urgent-final-v2.”

Good AI job.

2. AI should summarize messy ticket history

A customer writes in after three previous conversations, two replacements, one refund, and a subscription change nobody fully understands.

The agent opens the ticket and gets to choose between spending seven minutes spelunking through the helpdesk or answering with the confidence of someone who absolutely did not read the file.

AI should solve that.

Give the agent the customer history, previous issues, order timeline, subscription status, prior concessions, and the reason this ticket probably matters. Do not make the human dig through fourteen messages to learn that the brand already messed this up twice.

This is one of the best uses of AI because it makes the rep sharper before they reply. The customer gets a better answer, the agent gets the full picture, and nobody has to pretend that opening six tabs is a customer experience strategy.

3. AI should find patterns across tickets

This is the one I wish more brands cared about.

If ten customers mention leaking caps, someone should know. If five customers say the new formula tastes weird, someone should know. If subscription customers keep saying they have too much product, someone should know before the next retention meeting where everyone pretends churn is mysterious.

AI should be reading across tickets, reviews, surveys, and social comments to flag patterns your team might miss while they are buried in the queue. Broken zippers, melted product, weird smell, missing scoop, late shipments from the same warehouse, confusing onboarding, broken discount code, whatever.

A good CX person can catch some of this manually. They should not have to rely on memory and vibes while 300 tickets are waiting.

Let AI find the smoke and put it in front of the people who know what it means.

4. AI should route work to the right place

A lot of bad CX happens because the ticket takes a scenic tour through the wrong queue before anyone useful sees it.

AI should route billing issues to billing, product-quality issues to the right escalation path, angry subscription issues to the retention team, VIP issues to someone who understands why they are VIP, and low-risk questions to automation.

This will not make a sexy LinkedIn post. It will make the operation better, which is usually more useful.

Good routing means faster replies, cleaner ownership, fewer missed escalations, and less “who owns this?” theater in Slack. It also means your best people spend more time on the tickets that actually need them, instead of being human traffic cops for a helpdesk that looks like it was configured during a fire drill.

5. AI should draft low-risk replies

Drafting is great. Sending is where brands start lighting candles around the helpdesk.

AI should draft the boring replies: order status, return instructions, product FAQs, warranty eligibility, subscription instructions, basic policy explanations. Nobody needs to handwrite “you can start your return here” for the 4,000th time. That is how good agents slowly become houseplants.

The risk level should decide how far the draft goes.

If the customer is calm, the issue is simple, and the answer is low-stakes, let AI help. If the message includes a refund denial, an apology, a dollar amount, a health concern, a damaged gift, a public complaint, or someone who is clearly already angry, do not let the bot auto-send its little masterpiece.

That is how you create screenshots.

Now, the five things I’d keep far away from AI

The simplest rule I’d use: if the moment requires judgment, keep a human close.

1. Refund and return exceptions

If the policy cleanly says yes, automate it.

If the customer is asking for an exception, do not let the bot freestyle.

The policy might say no while the context says obviously yes. A customer outside the return window might be unreasonable, or they might be a great customer with one weird issue your policy did not account for. They might be technically wrong and still worth taking care of.

AI can show the order history, LTV, prior issues, and suggested options. Great. The actual exception should belong to a person until you are extremely confident the logic reflects how your best CX people would make the call.

Most brands are not there. They just have a refund threshold someone invented in a meeting.

2. High-LTV customers

Your top 1% customers should not get the same bot treatment as someone who bought once with a 40% off code and is now trying to return a final-sale item from the Obama administration.

This sounds obvious, and yet plenty of automations do exactly that because the ticket type is the same.

AI should identify the VIP. It should surface customer value, order history, subscription status, prior issues, and anything else that changes how the brand should handle the moment.

It should not quietly decide that your best customer deserves the cheapest workflow because the workflow technically works.

3. Angry customers

AI is very bad at knowing when it is making the situation worse.

It keeps the same tone while the customer gets more annoyed. It apologizes in that weird smooth way where every sentence sounds like it was dipped in customer-service lotion. It says “I understand how frustrating this must be” and then sends a help-center link, which is how brands create screenshots with captions.

Angry customers do not need perfect grammar. They need to feel like someone competent is actually handling the issue.

AI can summarize the problem and suggest next steps. A human should own the reply.

4. Product-quality judgment

AI should flag product-quality issues. It should not be the final judge of whether they matter.

A bot sees the ticket in front of it. A good CX person remembers the last ten and knows when the pattern feels too familiar to ignore.

Let AI group the signals. Let humans decide whether product, ops, quality, or leadership needs to hear about it before Reddit does.

5. Generic cancellation saves

Cancellation is where brands love pretending every problem is a discount problem.

Too much product gets 20% off. Too expensive gets 20% off. Did not see results gets 20% off. Going on vacation somehow also gets 20% off, because apparently every subscription issue can be solved with the same sad coupon.

Too much product needs a pause, skip, or better frequency. Price might need a smaller bundle. Product confusion needs education. Poor fit needs a swap. Travel needs a delay. A customer who is actually done needs a clean cancellation, because making cancellation painful is how you turn “maybe later” into “never again.”

AI can help here when it is designed around the reason, the customer context, and the right save path. It should not treat every cancellation like a discount negotiation with a pulse.

The actual rule

The Extinguisher Rule is the simplest way I can think about this.

AI can help you find the fire. It can read more tickets than your team can read, spot the pattern, summarize the context, route the issue, draft the boring reply, and make the human faster.

When the customer is standing there watching to see if the brand actually cares, keep a person holding the extinguisher.

Not because humans are inherently always magical. Plenty of humans are also bad at this. But your best CX people understand context in a way most AI workflows still do not. They know when the policy answer is technically correct and still expensive. They know when the customer is asking for information versus fairness. They know when a ticket is the first visible sign of something upstream breaking.

Before you buy another AI tool or expand the one you already have, run a simple audit. Pull the last 20 tickets AI resolved end to end. Read them like a customer would. Ask whether you would be comfortable with each exchange being posted publicly. Check whether any of them involved an apology, refund denial, dollar amount, angry customer, VIP, health concern, damaged gift, or product-quality issue.

If more than two make you wince, your deflection rate has probably been eating your judgment while everyone admired the silly little dashboard.

Find the fire with AI. Hand the extinguisher to a person.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

If you know me, you know two things take up an unreasonable amount of space in my brain: customer experience and travel.

I’ve written 198 newsletters about the first one. Meanwhile, I’ve been to 40 countries, spent far too much of my life figuring out points, and somehow never really written about the second.

I want to change that.

Every once in a while, I’m going to write about a place we stayed, ate, wandered into, or couldn’t stop thinking about. There will probably be some CX in here because apparently I’m incapable of going on vacation without noticing this stuff, but these are travel stories first.

This is Trip Report #001: Modica.

We got to Modica around 9 pm on Sunday night, which is late enough to arrive at a small hotel and immediately feel guilty about it.

A few hours earlier, Casa Talía had messaged us to say we were going to miss check-in. I apologized, assuming we were about to ruin the evening of some individual working reception.

When we finally pulled up, there was a man in his 60s standing down an alley waving us toward the parking spot.

Polo shirt, chinos, gray hair, small beard. Extremely Italian without making a production out of being extremely Italian.

His name was Marco.

He helped us with our bags and started walking us down the alley toward the hotel, except there wasn’t really a hotel.

There was no lobby or big entrance, and no reception desk with someone waiting to take a credit card for incidentals. Casa Talía is a collection of old little homes built into a hillside overlooking Modica, connected by narrow paths, gardens, stone walls, and lots and lots of stairs.

It reminded me a little of the hidden residential alleys in Jerusalem. You have the feeling that you’ve somehow wandered into somebody else’s neighborhood, except someone has left a very nice bed for you.

We followed Marco to the two-floor house we’d booked for three nights, which cost us 38,000 Hyatt points a night. Cash rates were around €500 a night, and I obviously chose the fake-money option because I have spent an embarrassing portion of my adult life making sure I never have to pay for a hotel.

The house was beautiful. White walls, cave-like ceilings, old stone, restored furniture and almost nothing unnecessary, with a little terrace outside covered in grapevines. You can sit at the table, reach above your head and pull off a bunch of grapes while looking across Modica, which is the sort of thing a hotel could spend an enormous amount of money trying to manufacture and still somehow f$ck up.

It also had a staircase apparently designed for a family with no children and no plans to ever encounter one.

Instead of a banister, ropes hang from the ceiling every foot or so. Architecturally, very cool. As the father of two kids, I have spent considerably less time admiring the architecture.

This was our first introduction to what I ended up loving about Casa Talía. It is beautiful and high-end, but nobody seems particularly interested in sanding away every strange edge until it resembles every other expensive hotel in the world.

And then we found out the guy standing outside at 9 pm waving us into a parking spot was the owner.

Marco Giunta and his wife, Viviana Haddad, were architects living in Milan when they came to Sicily for a summer holiday in 2001. They ended up in Modica almost by accident and stayed for 15 days.

Then they bought a room.

They went back to Milan, returned to Modica, and bought another. Then another. Within eight months they had somehow acquired 12 little properties on the hillside, which is apparently what happens when two architects fall in love with a Sicilian town, and nobody stops them.

Every one of those rooms had originally been somebody’s home. Marco and Viviana restored them, kept the original exterior walls, and connected everything through the garden rather than turning the whole thing into one conventional hotel building.

Which explains why, more than twenty years later, you arrive at Casa Talía and spend the first few minutes trying to figure out where the hotel actually is.

It still feels more like someone’s slightly ridiculous collection of beautiful Sicilian homes than a hotel.

Marco also behaves more like the person whose house you’re staying in than the owner of a fancy boutique property.

He’ll tell you where to eat and, just as confidently, where he wouldn’t bother. He says buongiorno every time he sees you. If you need something, somebody appears. He tells the story of how he ended up here with the enthusiasm of someone who still can’t quite believe this became his life.

And he’s there in the morning.

Breakfast happens outside under olive trees overlooking the old city of Modica. The view is stupid.

The churches and old stone buildings climb up the opposite hillside, framed by branches from the trees above you. Breakfast comes out slowly: bread, yogurt, juice, tomatoes, grilled zucchini, cheese, figs, fruit, jams. Much of it is grown locally or comes from whatever is actually in season nearby.

There isn’t a buffet with 11 kinds of cereal sitting under fluorescent lights. You sit down and eat what Sicily has that morning.

Marco hangs around nearby, talking to guests, checking in on people, and generally holding court.

Then there’s Riz.

I’m not entirely sure what Riz’s official job title is, and after a few days here I’m not convinced Casa Talía believes very strongly in official job titles. He checked us in, serves breakfast, handles the concierge stuff, fixes whatever breaks, and somehow finds time to play with our kids.

He also has an unnerving ability to appear.

At one point, our key wouldn’t open the door to the pool. I sent Riz a WhatsApp, looked back at the door, and maybe 15 seconds later he was standing behind me smiling with a new key.

I have no idea where he came from.

It’s like having a concierge who can apparate, except Hogwarts apparently has worse response times.

That’s basically how it works with Riz. You text him and within 30 seconds there’s a decent chance he’s standing in front of you, smiling, already fixing whatever you were about to ask him to fix.

He is a f$cking legend.

One morning while we were eating breakfast, I noticed that the housekeeping room overlooks the patio.

This means the staff knows when you’ve left your room because they can literally see you sitting under an olive tree eating breakfast. By the time you finish, they’ve usually been upstairs and cleaned it.

I’ve spent a decade around companies buying increasingly complicated technology to figure out where a customer is and what they might need next.

Casa Talía has a window.

There are little things like this everywhere once you start paying attention.

Our terrace has actual grapes hanging over the table. There are fig trees around the property. Old stone paths run between the rooms because the rooms were once separate homes. There are corners of the property where it’s hard to tell where Casa Talía ends and Modica begins.

Very little feels designed around what a hotel is supposed to have. It feels like they started with what was already here and figured out how to let people stay in it.

That applies to the people too.

I’ve stayed at hotels that know an absurd amount about me. They know what floor I prefer, when I last stayed, what status I have, probably what I ordered from room service in 2022. Some of them are incredible. Others manage to know everything about you while making you feel like room 416.

Casa Talía knew almost nothing about us when we arrived. Marco knew we were late, so he waited outside. Riz got to know the kids. Someone saw us eating breakfast, so they cleaned the room.

You start to wonder whether we occasionally make this stuff more complicated than it needs to be.

The WiFi password at Casa Talía is slowliving.

Of course it is.

I initially assumed this was a cute hotel thing, but Marco has been talking about slow living for years. He once described it as forgetting your phone, drinking a glass of wine, looking across at the Baroque buildings and sitting under the olive tree with a book.

There is one problem with this philosophy.

Marco also says he works 24 hours a day.

He said that years ago when asked what running Casa Talía had done to his life. In the same answer, he said his life was much better than it had been before. He gets to live near the sea, see blue sky and sun every day, and spend his time around people who are generally pretty happy to be there.

I love that answer because it makes slowliving slightly harder to explain.

Marco didn’t escape work. If anything, he seems to have created considerably more of it. He just built a life around work he actually wanted to be doing, in a place he didn’t want to leave.

Maybe that’s why nothing here feels particularly slow when you actually need something. When our fuse went out, Riz was there almost immediately. Breakfast appears, rooms get cleaned, bags get carried, kids get entertained.

The slowness seems to be somewhere else.

Most of my professional life has been spent around companies trying to make experiences scalable. You document things, build processes, create roles with clear responsibilities, automate the repetitive parts, and try to make sure the customer gets roughly the same experience regardless of who happens to be working that day.

There are very good reasons for doing all of that. It also removes some of what makes places like Casa Talía special.

If you asked me what the best part of the hotel is, I wouldn’t say the room. The room is beautiful, but I’ve stayed in nicer rooms. I wouldn’t say the pool either. It’s tiny. I wouldn’t even say the view, although the view at breakfast makes it very easy to lose an hour doing absolutely nothing.

I’d probably say Marco.

Which is a terrible answer if your job is figuring out how to scale Casa Talía to 200 locations.

How do you scale the owner standing in an alley at 9pm because a family he has never met is arriving late? How do you operationalize a sixty-something Italian architect telling you where he thinks you should eat dinner?

You could put his recommendations into an app. You could write down exactly what he says when guests arrive. You could create a breakfast SOP, photograph the way the tables are set and train everyone to say buongiorno with precisely the right amount of enthusiasm.

I’m sure you could recreate a lot of it.

I’m not sure you’d end up with Casa Talía.

We spend so much time in business trying to remove dependency on individual people. I understand why. If the company only works because one person is there every day, eventually you have a problem.

Travel has made me wonder whether we occasionally take that idea too far.

Some of the places I remember most are memorable precisely because of one particular person. The restaurant where the owner decides what you should order. The tiny store where someone spends 45 minutes helping you find something that costs $20. The hotel where the guy who owns it is standing outside waiting because he knows you’re arriving late.

Those businesses may be less scalable. They may also be considerably more difficult to forget.

Casa Talía is interesting because Marco isn’t the only one.

Riz isn’t doing an impression of him. He has his own personality and his own relationship with the guests. His job seems to be whatever needs doing at that particular moment.

There’s no obvious attempt to make every person sound or behave exactly the same. They just seem to care about the same place.

Casa Talía doesn’t feel luxurious in the traditional sense. Nobody is polishing marble in the lobby because there is no lobby. The pool is tiny. The stairs remain an ongoing threat to the Weiss bloodline.

But sitting under an olive tree eating tomatoes and cheese while Modica wakes up across the valley, you don’t find yourself wondering what’s missing.

Marco and Viviana started buying these little houses twenty-five years ago. Somehow, after all that time, it still feels like the people here are paying attention.

Maybe that’s what slowliving actually means.

Or maybe I’ve spent too many years working in CX and should be capable of eating a fig without turning it into a business philosophy.

Either way, I’m listening to Marco.

Hi Team!

It’s been a hot minute; hope you are well!

This Saturday night, we head out on Air France business class to Sicily with a layover in CDG, spend the week at hotels that run roughly $700+ a night, take a boat to Malta for two days, then head back to NYC.

ALL WITH POINTS!!

Malta will be country #40, and I genuinely can’t remember the last time I paid cash for a hotel…

If you needed to hear it one more time… go get yourself some signup bonuses and see the world. PLEASE.

Now, back to regularly scheduled programming. In the last few weeks, I’ve seen several CX issues on social handled publicly by the CEO, and it got me thinking.

I’ve heard this story a million times, and even experienced it myself (I am a nightmare customer 🙃)

A customer spends two days arguing with support, gets increasingly pissed, asks for a manager, gets passed around a few times, and eventually lands with someone in the C-suite.

That person reads the thread, apologizes like an actual human, makes a decision, and the customer immediately calms down.

More often than not, the C-level exec does not even offer a discount or a refund; they validate and talk like a human.

So why does this happen? And what can brands do to avoid it?

Let’s get into it.

This week’s newsie is brought to you by my friends at Kim.cc.

Tickets closed by AI have an average re-open rate of 50%+

A closed ticket doesn’t necessarily mean a resolved issue.

When a customer has to come back because the first answer didn’t solve their problem, the brand pays for the same issue twice: once for the AI interaction, and again for the human cleanup.

That makes re-open rate one of the clearest signals of whether support AI is actually working.

The opportunity isn’t to automate more tickets. It’s to resolve more of them the first time.

But resolution requires more than retrieving the right answer from a knowledge base. It requires judgment — knowing how to handle edge cases, exceptions, ambiguity, and situations where the documented answer isn’t enough.

That’s the layer most AI support tools lack.

Kim.cc adds it.

Kim is an AI-native service for ecommerce customer support. AI handles the volume, while Sentinels, trained CX, QA, and prompt experts, implement, train, and continuously improve the system, stepping in where human judgment is needed.

And when a ticket goes wrong, Sentinels don’t just correct that one interaction. They teach the AI how to handle that situation the next time.

That’s how Kim works across 200+ ecommerce brands: continuously improving how tickets are handled so more issues are resolved the first time, and fewer customers have to come back.

Because the real measure of support AI isn’t how many tickets it closes.

It’s how many stay closed.

Check out the Customer Study report

Learn more about Kim.cc

Sometimes they just want you to give a sh!t

I think companies misunderstand what an angry customer is actually asking for more often than we realize.

Sometimes they absolutely want their money back. If the product showed up broken or you charged them for something you shouldn’t have, you certainly should do the right thing.

But plenty of customers aren’t primarily fighting you over $20 or the 20% discount on the “next order.”

They’re fighting because something went wrong, they took the time to explain it, and every response since has made them feel like nobody at the company actually understands why they’re upset.

Someone writes four paragraphs explaining what happened and gets a $5 credit with “we apologize for any inconvenience this may have caused.” Depending on the situation, the $5 almost makes it worse. It’s insulting.

The customer wanted somebody to acknowledge that the experience sucked, and the response feels like you assigned a very small dollar value to making them go away.

Then the thread reaches somebody senior, who writes, “I read through everything, and I’m really sorry we handled this the way we did. I completely understand why you’re frustrated.”

The customer finally feels heard.

Maybe the resolution stays the same. Maybe they don’t get another dollar. Somehow the temperature of the entire conversation changes.

We spend a lot of time training CX teams on rules and policies, and considerably less time teaching people how to recognize what the customer is actually asking for.

Sometimes the right answer is money. Sometimes it’s fixing the product. Sometimes it’s simply having one person at the company say, credibly, “Yeah, I see why you’re pissed.”

We need to hire folks with EQ and let them cook.

You trained everyone to escalate

Escalations are useful when something genuinely requires more authority or expertise. Every company will have situations where the stakes are high enough, the request is unusual enough, or the risk is significant enough that somebody senior should make the call.

The problem starts when escalation becomes the default destination for anything that doesn’t fit perfectly inside the playbook.

An agent gets a request that feels reasonable but falls slightly outside policy, so they ask a manager. The manager has more authority, but the safest decision is still to follow the existing process.

The customer gets another version of the same answer, gets angrier, and by the time someone senior enters the thread they’ve spent two days trying to convince the company that the issue deserves an actual decision.

I’d pay very close attention to what changes once the executive responds.

In a lot of cases, the executive actually has less context than the original agent. They didn’t spend the last day inside the thread, and they probably know less about the day-to-day mechanics of the policy.

They simply feel comfortable deciding that this particular situation deserves an exception, or admitting that the company handled something badly without worrying about whether the wording is approved.

The agent closest to the problem can have the most context while holding the least authority to act on it.

As the issue moves upward, the person making the decision often knows less about the conversation but feels far more comfortable making the call.

That is how you end up with a CEO who looks weirdly brilliant at customer support.

And it should infuriate you.

You threw AI at every problem

AI is genuinely good at a lot of support work now. WISMO, return status, address changes, subscription updates, basic product questions, and all the repetitive work that used to eat huge chunks of a team’s day should increasingly be handled without a human.

I’ve said this in the past… but that changes what the human job looks like.

Once the easiest tickets disappear, a larger share of the conversations reaching your team are messy. Something went wrong twice. The customer already tried the obvious fix. The normal policy doesn’t quite fit. There’s history involved.

The technically correct answer can still make the situation dramatically worse.

Those conversations require better judgment than the average support ticket did a few years ago.

A lot of companies upgraded the technology while leaving everything around the human team basically untouched. They kept the same hiring profile, the same QA process, the same approval tree, the same bullshit macros, and the same fear of somebody making an exception leadership didn’t explicitly approve.

So software takes the questions that barely require judgment, while people inherit more of the conversations where judgment is the entire job.

Then we give those people even less room to use it.

I’m pretty sure we have that backwards.

Your whole CX team should not sound the same

I can’t say I hate a macro or canned response per sè. Nobody should spend their Monday typing the same return instructions 80 times because someone decided handcrafted support emails are more authentic.

But somewhere between “we should have a consistent brand voice” and “we should save agents some typing,” many companies decided that every person on the CX team should sound exactly the same.

I don’t think that’s a good thing.

You hired seven different humans. They have different personalities, different ways of explaining things, different senses of humor, and different instincts for dealing with people. Then you handed all seven the same paragraph and told them not to touch it too much.

Customers can feel that.

If I write three paragraphs about a very specific situation and get a reply that could have been sent to 5,000 other people that morning, I have no evidence anybody actually read what I wrote.

The macro should get the agent 80% of the way there. Let them do the remaining 20% themselves.

There should still be guardrails. I don’t need every agent improvising your warranty policy based on their mood that afternoon.

But there is a huge amount of space between letting everyone say whatever they want and making every person at the company sound like the same damn macro.

AI handling more of the routine work makes this even more important. If a customer has reached a human because the situation is complicated enough to need one, sounding like a human seems like a reasonable place to start.

Sometimes they have permission and still don’t give a sh!t

I also don’t want “empower your team” to become the explanation for every bad CX organization, because I’ve seen brands give people plenty of freedom and still get painfully mediocre support.

The team can make exceptions. There’s budget for surprise and delight. Leadership has explicitly told them to waive fees, send replacements, spend a little extra, and use their judgment when the situation deserves it.

Then you read the tickets, and everyone is still sending the same macro, nobody follows up, and a customer who has ordered 14 times gets treated exactly like someone placing their first order.

At some point, that stops being a permission problem.

Sometimes you hired people who are good at working a queue but don’t naturally care enough to look beyond the ticket in front of them.

The same goes for CX leadership. Someone can keep response times down, maintain the macros, run the weekly meeting, and send you a perfectly nice CSAT report while having almost no POV on whether the actual customer experience feels good.

That matters even more as AI takes over the repetitive work. The human job is increasingly made up of messy situations where the policy won’t tell you exactly what to do.

I’d rather hire someone who is curious enough to understand what happened, cares enough to want to fix it, and has the judgment to know what a reasonable fix looks like.

You can teach someone your policy and give them a bigger budget. Teaching them to actually give a shit is a lot harder.

Your customer has no idea who any of you are

Companies underestimate how absurd their internal processes can feel from the outside.

Inside the company, the ticket moved to Operations because Operations owns that workflow. Finance has to approve something, the manager is out today, the warehouse hasn’t responded yet, and the original agent went off shift.

To the customer, they emailed one company on Monday and have spent the rest of the week explaining the same problem to that company.

They don’t know which department owns replacements, and they definitely don’t care why somebody in Finance needs to approve $60. They want one person who knows what happened and stays attached to it until it’s over.

Executives tend to be good at this because they collapse all of that internal complexity. They take responsibility for the company as a whole and tell the customer what happens next.

Your frontline team should be able to create that same feeling without personally running Finance, Operations, and the warehouse.

Here are the 8 things I’d change

If escalations keep landing with leadership, I’d start here:

• Pull your last 20 to 30 executive escalations and compare the first replies with whatever finally resolved them. Look at what materially changed once someone senior got involved. Sometimes the remedy will be different. In other cases, the biggest change will be tone, clarity, ownership, or somebody finally feeling comfortable making the decision.

• Find the exact response where the customer became meaningfully more upset. Most ugly threads have a moment where the original problem becomes secondary and the customer starts getting angry about how the company is handling it. Figure out what your team said right before that happened.

• Stop automatically reaching for discounts when somebody gets angry. Figure out what they actually want first. If you owe them money, give them the money. If they’re looking for accountability, a tiny coupon in place of a real acknowledgment can feel more insulting than useful.

• Write down what agents can actually do without asking permission. Refund limits, replacements, shipping upgrades, fee waivers, credits, phone calls, and common policy exceptions should have clear boundaries. If your team has to guess where their authority ends, they’re going to send anything remotely unusual upstairs.

• Look for decisions managers approve almost every time. If managers keep rubber-stamping the same type of $50 or $75 exception, move the decision closer to the person who already has the customer context. Your approval history is giving you a pretty good map of authority you no longer need to centralize.

• Let people personalize the macros. Every meaningful response should contain enough of the agent’s own language that the customer knows somebody actually read the conversation. Consistency matters, but if every person on your team sounds interchangeable, you’ve probably taken it too far.

• QA judgment alongside compliance. An agent can follow every policy, use every approved phrase, and still make the customer much angrier. Review whether the response moved the situation forward, whether the agent understood what the customer needed, and whether a reasonable decision earlier in the thread could have prevented the escalation.

• Turn good judgment into training. When an agent handles something beautifully or an executive saves a difficult conversation, break down what worked. Show the team how the person thought about the situation, which details mattered, what they were willing to do, and why. The same type of issue should require less seniority the next time it happens.

I think senior leaders should keep reading customer emails. They should answer some too. There are few better ways to understand what your company actually feels like from the outside.

But customers should feel those standards long before anyone with “Chief” in their title has to enter the thread.

If the apology gets warmer, the ownership gets stronger, and people become much more willing to make a reasonable decision every time another title gets added to the email chain, I’d look pretty hard at what the organization taught the first person who answered.

Your customer probably didn’t need the CEO. They needed somebody who was allowed to solve the problem.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

Hi Team!

Welcome to the summer chaos where pickup, drop-off, and camp laundry somehow fill half my spare time.

This is newsletter #197, and while I’ve covered more topics than I can count, there’s one topic I get more emails and consulting requests about than almost anything else:

Increasing subscription LTV.

More brands than ever, especially in CPG and supplements, are pushing subscription as the default and doing whatever little hacks they can come up with to juice recurring revenue. Some of it works, but some of it feels like it was brainstormed in a room where nobody ever tried to cancel anything online.

Most subscription retention problems are expectation problems dressed up as billing problems.

Brands treat retention like a dunning issue or a cancellation flow project. Delay the churn event, add a discount, make the portal a little more annoying, force the customer through three screens and hope they get tired…

By that point, the customer already decided.

They’ve been annoyed for weeks because the product timing, quantity, or frequency stopped matching their life. The shipment shows up and they already have three bottles in the cabinet. Or they ran out two weeks ago and forgot to order. Or they didn’t see results and assumed the product wasn’t for them.

At that point, the retention moment has passed, and the cancellation is more of a formality

This week, I want to talk about five levers that actually move subscription LTV, and why most brands are optimizing the wrong part of the funnel.

Let’s get into it.

This week’s newsie is brought to you by my friends at Hoop!

(Hoop is one of the newest tools I found and one I highly recommend checking out)

Brands pour a huge chunk of their budget into acquiring new subscribers. On average, 50 to 70% of those subscribers churn before month 2.

Stop and consider that for a moment: you paid an expensive CAC for that revenue, and most of it walks out through a cancellation ticket or self serve flow that gets closed with a polite confirmation. You don’t know exactly why each subscriber cancelled, and you didn’t make a reason based offer to help them stay.

So what can you actually do today to keep more of those customers for longer? That’s exactly what Hoop does, out of the box. Built by the team behind Trello, it runs the save conversation on every cancellation:

- Asks why before offering anything: then matches the offer to the specific reason (product education, price, a product swap instead of a cancel, and fully customizable based on your brand)

- Reads full subscriber context: from systems of record like Shopify and Recharge or Skio

- Works inside Gorgias, Zendesk and Kustomer: No new tools, no migration.

MoonBrew, a scaling sleep supplement brand, went live in about a week and hit 24/7 coverage within a month, for roughly the cost of one agent instead of five.

Their team stays on the complex, high-touch conversations while Hoop handles cancellation requests and boosts save rate.

Since launch, not one customer has asked to “talk to a human.”

Want to see how much revenue you could be keeping?

Take the free audit: Hoop reads 90 days of your helpdesk tickets and hands you your baseline save rate, why subscribers actually leave, and the annual revenue you’re leaving on the table.

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The first purchase doesn’t need to be a subscription

Most brands are obsessed with converting the first purchase into a subscription because it makes the dashboard look clean. Higher attach rate, prettier LTV projection, easier forecasting.

The problem is that forcing a subscription on the first purchase can suppress AOV, attract discount-chasers, and set the wrong expectation from day one. Someone who says yes because you offered 20% off isn’t the same as someone who says yes because they already know they want the product every month.

Sometimes the better long-term customer is the person who buys a larger one-time bundle, uses the product for six weeks, understands the value, and subscribes later with actual intent.

The subscription funnel gets optimized to death while the OTP path is treated like a waiting room. Brands spend all their retention energy on cancel flows and almost none of it on turning high-intent one-time buyers into subscribers after they’ve proven they like the product.

That customer often has better intent and fewer “wait, why am I being charged again?” vibes.

Save offers should match the cancellation reason

A lot of cancellation flows are basically Mad Libs. Customer clicks cancel, brand says, “Sorry to see you go, here is 20% off.”

Doesn’t matter if the reason was price, quantity, timing, product fit, travel, moving, or the customer having six tubs of greens powder in the pantry. Same sad offer every time.

A discount only helps when price is the actual problem. If the customer has too much product, 20% off is just a cheaper way to make the problem worse.

If someone has too much, offer pause or skip. If price is the issue, offer a smaller bundle or lower frequency. If they didn’t like the product, offer a swap. If they’re traveling, delay the shipment. If they didn’t see results, reset expectations or send usage content.

The save offer should solve the reason they’re leaving. Otherwise you’re just bribing them into one more billing cycle they’ll resent.

Frequency is retention

This is probably the most underrated lever in subscription retention, and the one that kills LTV for consumable brands.

Most subscription churn is less of “I hate this brand” and more of “I have too much of this product sitting in my cabinet and I can’t keep up.”

Funny enough, this was our #1 churn reason at OLIPOP way back.

This is huge for supplements, coffee, skincare, pet, household, beverage, pantry, basically anything that gets used up.

If you ship a 30-day supply every 30 days and the customer only uses 22 days’ worth, they’re building backstock after every shipment. After six months, they have a small museum of your product in the cabinet. That’s when they cancel.

Let customers set frequency. Make skip easy. Use first-order behavior to suggest the right cadence. If someone buys a 60-day supply as their first order, please do not default them into a 30-day subscription like a lunatic.

OTP customers aren’t failed subscribers

I saw Olivia Kory from Haus post a clip from Andrew Faris on this, and it put words to something I’ve been seeing in audits.

Subscription customers look amazing in the first 90 days because LTV front-loads nicely. Recurring revenue, predictable orders, easy modeling. The dashboard gets all dressed up and tells everyone the strategy is working.

Then you look six or twelve months out and the gap between subscribers and non-subscribers can get a lot less dramatic.

One-time buyers often have higher AOV on the first purchase because they weren’t chasing a subscription discount. They have lower first-order friction because they didn’t have to opt into recurring billing. And if your replenishment marketing isn’t terrible, they come back.

The mistake is treating subscription as the only retention path and OTP customers as failures.

OTP customers can be retained. They just don’t show up as neatly in the subscription dashboard. You have to work a little harder to bring them back, but the economics can be just as strong, and sometimes cleaner because they’re not locked into a cadence that stops matching their life after three months.

The OTP funnel is under-optimized because subscription makes the spreadsheet feel safe. That doesn’t mean it’s always the better customer experience.

Cancellation is not the first retention moment

Most brands wait until someone opens the cancellation portal to “do retention,” which is completely backwards. By the time a customer clicks that button, they’ve already made the decision. The portal is just where they go to make it official.

The better retention levers happen way earlier. Onboarding sets expectations. Usage education helps them get results. Delivery timing prevents backstock. Failed payment handling catches accidental churn. Support moments build or destroy trust.

All of that happens before cancellation.

Save flows still matter, especially when they respond to the actual reason someone is leaving. The bigger win is making sure the cancellation data changes the business. If 40% of cancellations are “too much product,” the problem is not your save offer. The problem is your default frequency.

By the time the customer is in the cancellation portal, the brand is already playing from behind.

Most subscription brands are optimizing the cancellation moment and ignoring the decisions that happened before it: first purchase path, save logic, frequency setup, OTP retention, and the early customer experience.

That’s why so many subscription programs look good on paper and bleed out in month six.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

Hi Team!

It’s been a great week. We made it through the vicious heatwave in NY that made it feel like a European summer, and I restocked points and miles big time as well.

• Chase Sapphire Reserve 150k bonus (current offer)

• Chase Business Ink 100k (current offer)

• Amex Gold 150k (my wife and I both snagged a few months back)

We wiped through 300k points on our St. Kitts and Panama trip, but were able to recoup another 445k since then, with another 100k coming down the pipe :)

Sicily and Malta are booked for August.

Croatia, Montenegro, and Bosnia are booked for October.

The points-and-miles gods are shining on me this year. ☀️

A few weeks back, we talked about segmentation and why most brands are grouping customers by what they bought instead of who they are.

This week, I want to get a bit deeper and more practical: what useful personalization data actually looks like and what a few brands are doing differently when they build around it.

Most brands have gotten very good at using data that doesn’t actually tell them much about the customer. A customer viewed a hoodie, so the hoodie now follows them around the internet like a Victorian ghost.

That’s behavioral recall, and the issue isn’t that the data is wrong. It’s that the data is too thin to build anything meaningful on top of.

Clicks, purchases, abandoned carts, PDP views, discount usage, and maybe a quiz response from 2021 that nobody’s touched since. Helpful signals, but a very shallow foundation.

I want to talk about what doing this better actually looks like.

Let’s get into it.

This week’s newsie is brought to you by OuterSignal!

Their order tells you what they bought. OuterSignal shows you who they are.

OuterSignal tells brands 10x more about their customers than they know right now — age, occupation, property value, social following, family status, interests, education, and more — and uses that to identify VIP customers, build precision segments, and personalize outreach at scale.

Whether it’s flagging celebrities, athletes, investors, podcasters, buyers at large retail chains, or simply surfacing micro-influencers within a target cohort, OuterSignal finds, flags, and activates the customers that matter most with unprecedented precision.

OuterSignal will research 1,000 customers for free, and is offering friends of Eli 50% off their first two months when they upgrade.

Tell them Eli sent you!

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Shallow signals make shallow personalization

Most brands personalize off whatever data is easiest to grab: viewed product, purchased product, clicked email, added to cart, browsed collection. That data tells you what happened inside your store or email program. It doesn’t tell you much about the customer’s life outside of it.

Your behavioral data sees the same action no matter who’s behind it, so the brand sends everyone the same ‘Still thinking it over?’ email and calls it personalization.

The data is technically accurate. It’s also too thin to make the message feel like it was written for a human.

This is how brands end up with lifecycle programs that look sophisticated in Klaviyo and feel completely generic to the person reading them. The logic tree is impressive.

The message is still basically, “You looked at this. Please buy it.” Very moving stuff.

What Gratsi figured out

I recently came across Gratsi, a DTC wine brand selling boxed wine that does not look like something you’d hide under the sink before guests come over.

They had the same problem most brands have: people were buying, subscribing, clicking, and coming back, but the customer file still didn’t explain much. “Wine buyer” is technically true, but it doesn’t get you very far.

A retiree hosting friends, a health-conscious professional looking for a lower-sugar option, a founder sending client gifts, and a parent trying to get through Friday night without reading 19 tasting notes from a guy named Sebastian can all look identical in Shopify.

Gratsi used OuterSignal (this week’s sponsor) to understand their core personas. OuterSignal enriched their customer data and surfaced different identity-based groups inside the customer base: Active Retirees, Health-Conscious Professionals, Creative Entrepreneurs, and a few others.

Then Gratsi took one campaign and changed the message based on those groups. The send time, audience size, subject line, and product stayed consistent. The email itself changed based on the reason someone might care.

• Active Retirees got messaging around slowing down and enjoying the moment.

• Health-Conscious Professionals got messaging about zero sugar, zero guilt, and feeling better the next morning.

• Creative Entrepreneurs got a different angle entirely.

The persona-matched messaging drove a 47.2% lift in revenue, 54.1% higher click rate, 56.1% more subscription conversions, and 41.7% more orders. Open rates were basically flat, which is the part I care about most. The lift happened after the open, when the message matched the person reading it.

Purchase history is a lazy creative brief

This is where lifecycle teams get stuck. The brief says “past wine buyers,” which could mean dinner party hosts, weekday drinkers, collectors, gift buyers, wellness shoppers, empty nesters, suburban parents, founders, retirees, or someone who bought one box because an ad caught them at 11:43 PM after two glasses of someone else’s wine.

Same thing in apparel. “Bought a dress shirt” tells you almost nothing useful. One customer is an executive wearing it twice a week. Another is a groom buying it for one occasion. Another needs something affordable and wrinkle-free for work travel.

Same SKU, completely different customer, and if those three people get the same email, one might convert, one ignores it, and one unsubscribes because the brand keeps talking like they’re shopping for a life they don’t have.

The product is the shared detail. The reason for buying is where the money is.

Better data makes the copy less lazy

A lot of weak marketing starts before the copywriter ever touches the brief. The brief is boring because the segment is boring, and the segment is boring because the data underneath it is too basic.

The “insight” is that someone purchased Product A, so now the brand would like them to purchase Product B. Groundbreaking stuff. Somewhere, a lifecycle marketer is bravely dragging a product block into Klaviyo.

Identity data gives the creative team something real to work with. A health-conscious professional should get a different wine message than an active retiree. A new parent should get a different apparel message than a founder. A micro-influencer has a different value to the business than a one-time buyer at the same AOV.

This is also why brands end up over-discounting. When you don’t understand the customer, price becomes the easiest lever. So the brand sends 15% off, then 20% off, then a final final final chance email that somehow returns every month like a raccoon in the attic.

Better customer data gives you more to work with: status, use case, lifestyle, timing, identity, actual reason to buy. The discount becomes a tool instead of the whole damn strategy.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

Hi Team,

HOW IS IT JULY ALREADY?

Before I jump in, two important things:

1. If you wish you traveled more and want to travel for free this summer, get yourself a Chase Sapphire card and check out seats.aero to start looking at transfer options for your summer vacay. Anyone can do it, GO SEE THE WORLD. I will keep saying this until I am blue in the face. It’s free money.

2. If you have been to Kotor or Dubrovnik lately and have suggestions on where to stay, please let me know. Debating an October sprint with the kiddos. :)

Now for this week, I want to dive into maybe the hottest and most unnerving topic discussed here, which will certainly get me in trouble.

AI in CX.

Something I’ve been thinking about lately:

I still have yet to meet a single customer who told me they loved an AI support experience.

Every brand I talk to is implementing AI all over their CX stack. Your efficiency metrics look incredible on paper. Response times are down, handle times are shrinking, and your team can process way more volume than they could a year ago.

But nobody is talking about the fact that your customers hate a lot of it, and according to data from this week’s sponsor, >50% of bot-resolved tickets get reopened.

Not all of it and maybe not always, but enough of it that a meaningful chunk of them are one bad bot loop away from abandoning your brand entirely.

They are not even waiting long enough for the CSAT survey to hit, they already slammed the phone down and are jogging to your competitor instead.

This week, I want to talk about where AI actually works in CX and where it actively destroys trust, brought to you by real data.

Let’s get into it.

This week’s newsie is brought to you by Kim.cc.

Nearly 60% of consumers welcome AI for simple tasks, but demand a human when the stakes are higher.

Everyone keeps saying customers want AI support.

Turns out that’s only half true.

Kim.cc surveyed 1,000+ U.S. consumers to understand what people actually think about AI customer service.

Some findings were baffling:

• 50% blame company leadership, not the AI, when a chatbot makes an error.

• Over one-third have already abandoned a brand because of a bad AI support experience.

• Nearly half repeatedly type “agent” just to escape the bot.

The takeaway isn’t “don’t use AI.”

It’s that customers want AI for simple questions, and humans the second things get complicated.

Kim.cc is built around that exact idea: AI customer support with a human eye.

Through their Sentinel service, AI handles the work, while trained experts supervise the system - reviewing quality, handling exceptions, and continuously training the AI. They work with 200+ Shopify brands to help them move faster without turning support into a locked room with a chatbot in it.

If you’re building customer support for your Shopify store, this report is worth reading.

Check Out The Report

Learn More about Kim.cc

I Watched Brands Get This Backwards

I’ve started using two questions before letting AI touch anything, because I’ve seen enough brands roll out AI support, celebrate the efficiency gains, and then wake up a few weeks later to reviews about how impossible it is to get help.

The two questions:

1. Can the AI execute this task flawlessly?

2. Are the stakes low enough that if it goes slightly wrong, the customer doesn’t lose money, miss something important, or feel ignored during a crisis?

If the answer to both is yes, AI is probably fine. If the answer to either is no, you need a human close by.

Simple tracking requests, basic FAQs, store hours, return policy lookups. These are low-stakes, high-repetition tasks where speed actually matters and accuracy is mostly binary. Nobody needs to wait 18 hours for a human to say their package is still in New Jersey.

That’s where AI works.

According to Kim.cc’s survey, 44.5% of consumers welcome AI for simple tracking, and another 42% accept or tolerate it. For basic info like store hours, return policies, or FAQs, 42.9% welcome AI and 43.2% accept or tolerate it. Pretty clear green light.

But the second the issue gets expensive, emotional, or messy, people want a human. Only 15.9% welcome AI for financial transactions like refunds, billing disputes, or plan upgrades. 51.7% demand a human. For high-emotion issues like a terrible experience or flight cancellation, 59.5% demand a human.

Essentially, the lower the stakes, the higher the tolerance. The higher the stakes, the faster people bail. I know this might sound obvious to most of you all, but then half the brands I talk to are “99% AI,” so apparently we still need to say it.

This is where brands keep getting themselves into trouble. They see AI work for “Where is my order?” and somehow decide it should sit in front of every customer issue.

That is like saying because a microwave is good enough for leftovers, it should handle your wedding catering. Useful tool, wrong damn job.

The Stakes Are The Strategy

A tracking question and a refund dispute both count as one ticket. A return policy lookup and a damaged item escalation both count as one ticket. A subscription skip and an angry cancellation after three bad deliveries both count as one ticket.

But they are not the same thing. Some tickets are low-stakes and annoying. Others are trust moments.

If I call Delta to change my seat and an AI bot can move me from a middle seat to an aisle seat in 30 seconds, incredible. Give me the bot 10/10 times. I do not need a human, I do not need fake empathy, and I sure as hell do not need someone to say, “I understand how frustrating middle seats can be.”

Just move me away from 26B and let us both continue our lives.

But if I have an emergency, my flight leaves in 30 minutes, and I need to change the name on a ticket or get rebooked before I miss something important, please do not make me explain my life to a bot that can only offer three buttons and a cheerful apology.

Same airline, same general support category, completely different stakes.

That’s the part most AI CX rollouts miss. The better question: what happens if AI gets it wrong?

If your AI screws up a tracking update, the customer is mildly annoyed. If your AI screws up a billing dispute or gives wrong information about a return policy, the customer loses money or time, and now they’re gone.

The survey found that when AI gives bad information that costs customers time or money, 50.3% blame company leadership for deploying an unready tool. Only 17.7% blame the AI itself.

Customers see bad AI support as a deliberate cost-cutting decision. Once they see it that way, the damage compounds. They don’t think the bot is broken. They think the brand is cheap.

The Gatekeeper Problem

The Kim.cc survey calls one of the worst AI behaviors “The Gatekeeper”: refusing to give customers an option or button to speak to a human.

It is brutal because it is exactly what customers hate, and exactly what many brands are optimizing for. One of my least favorite metrics in CX is “deflection rate.”

Imagine telling a customer the goal of your support bot is to deflect as many people with issues as possible from getting to their ideal resolution in a painless manner.

Sounds less sexy on the vendor slide.

Most AI is designed to solve the issue before escalating. On a slide, that sounds reasonable. Why route to a human if the bot can handle it?

That assumes the bot actually can handle it. More importantly, it assumes the customer trusts the bot to handle it.

Those are different things.

When the bot misses once, maybe the customer rephrases. When it misses twice, the customer starts getting annoyed. By the third attempt, the customer has usually decided two things: the bot is useless, and the brand is making it hard to get help on purpose.

According to the survey, 47.6% of consumers have typed “agent,” “human,” or “representative” repeatedly to bypass the bot.

That is a damn revolt.

A customer should not have to trick your support system to reach someone who can make a decision. They should not need to type nonsense, threaten to cancel, pretend they want to buy something expensive, or call a separate phone number because the chat experience turned into a hostage situation.

It is an obstacle course with a CSAT survey at the end.

If your AI cannot solve the issue in two prompts, it should offer a human. Not after five attempts. Not after the customer starts typing in all caps. Not after they threaten to report you to the Better Business Bureau like it is 2006.

Two prompts.

Fake Empathy Makes It Worse

Customers hate fake empathy.

When a bot says, “I’m so sorry to hear that, I understand how frustrating that must be,” 32.3% of consumers say they find it manipulative or annoying because a machine cannot actually feel empathy. Another 17% say it actively angers them because it feels like a fake delay tactic. Only 18% find it comforting.

So roughly half of customers are actively put off by the exact language many brands are adding to make AI feel warmer.

AI cannot feel empathy, and customers know this. Pretending otherwise does not make the interaction better. It makes it feel dishonest.

If the bot is going to apologize, it should apologize for the delay or inconvenience and then immediately solve the problem.

“I’m sorry this is taking longer than expected. Let me pull up your order details now.”

That works because it is transactional and honest.

“I totally understand how frustrating this must be for you” does not work because the customer knows the bot does not understand anything. It is reading a script.

The brands that do this well do not try to make AI sound human. They make it sound fast and accurate.

The bot is a tool, not a therapist. Let it be a good tool.

What Actually Works

If the task is simple, repetitive, and low-risk, AI is fine. Order tracking, FAQ lookups, store hours, basic account changes, return policy questions, address changes before fulfillment. Let the bot handle it. Speed actually wins there.

When the task involves money, emotion, urgency, or any level of interpretation, route to a human quickly. Don’t make the customer fight for it. Don’t make them type “agent” five times. Build the escape hatch into the first or second prompt, and make it obvious.

Stop trying to make your AI sound empathetic. It’s a bot. Let it be fast, accurate, and honest about what it is.

Solve the problem or get out of the way. That’s the whole job.

Right now, too many brands are using AI like a blanket solution, and the data is screaming that it’s backfiring.

Your efficiency metrics look great. Your customer sentiment does not.

Fix the framework before you lose more people.

Again, highly suggest checking out the report from Kim.cc

Check It Out

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

Hi Team!

Spent a few days in Panama end of May, and 10 days in Israel at the beginning of June. Got hit with jet lag, a stomach bug, and the strong willingness to be a proper homebody. (at least until the Toronto trip in July and the Sicily/Malta trip in August 🙃)

Something that has come up a lot in conversations over the last few weeks with brands I’m chatting with is segmentation and its importance in overall retention efforts.

Most DTC brands have gotten really good at segmenting customers by order history. You’ve got your one-time buyers, your repeat customers, your high AOV spenders, maybe a VIP tier if you bought more than three times or crossed some dollar threshold.

The problem is that none of those segments tell you anything about the person. A C-suite executive and a college student can both be two-time purchasers who spent $80. Your segments treat them the same because your data only knows what they did, not who they are.

So you send them the same email, the same ad, the same offer, and one converts while the other churns.

This week, I want to talk about why segmenting on purchase behavior is a weak foundation for retention and growth, and why the brands pulling ahead are segmenting on identity instead: occupation, lifestyle, life stage, the stuff that actually explains why someone bought in the first place.

Let’s get into it.

This week’s newsie is brought to you by one of the coolest new tools I’ve personally ever seen and one that explicitly solves the problem we’re talking about today. Meet OuterSignal.

OuterSignal helps brands transform basic order data into fully researched, actionable customer profiles.

Right now, all you get is a name, email, and address. Imagine if every order also had age, gender, occupation, social following, property value, and 100+ other signals.

With OuterSignal, you can:

1. Segment your existing customers by data-driven personas – including occupation, life stage, lifestyle, and buying motivations– allowing you to personalize email, SMS, and customer experience to each persona

2. Know why each customer bought, so you can predict who’ll repeat, win back the right churned customers, and stop blasting everyone the same offer.

3. Spot VIPs in your customer base (celebrities, athletes, investors, influencers, buyers), and treat them like it.

Leading brands like Create, Jolie, Magic Mind, and Mizzen+Main trust OuterSignal to power their customer personalization and increase their ROAS by up to 160x.

OuterSignal will research 1,000 customers for free, and is offering friends of Eli 50% off their first two months when they upgrade.

Tell them Eli sent you!

Learn More

Your segments are SKU buckets wearing a costume

Most brands segment like this: anyone who bought Product A goes in Bucket A, anyone who bought twice goes in the repeat bucket, anyone who spent over $200 goes in the high-value bucket. You might dress it up with a name like “Core Enthusiast” or “Premium Loyalist,” but under the hood it’s still a filter on order count or SKU.

But something most retention folks learn over time is that buying twice doesn’t tell you if someone’s about to buy a third time or ghost you forever. It tells you what someone did after they decided to buy, but it doesn’t tell you why they bought, what problem the product solves in their life, or whether they’ll buy again.

A menswear brand could have 50,000 customers who each bought a dress shirt, and some of them are executives who wear it to board meetings, some are grooms who need it for a wedding, and some are retail workers who need something cheap and wrinkle-free for their shift.

Same SKU, totally different reasons for buying, completely different retention curves. Messaging all of them the same way means you’re treating the person who needs you every week the same as the person who needed you once.

The brands doing this differently segment on who people are

Mizzen+Main sells performance menswear. They could segment on repeat buyers or AOV, and they did for years. But when they enriched over 100,000 historical customer records with actual identity data, they found something better: a massive cluster of C-suite executives who had purchased in the past but weren’t responding to broad campaigns.

They built an audience around that profession, that lifestyle, that identity, and ran it on direct mail over BFCM. It became their highest-performing campaign in company history. Over 20x ROAS, $50 AOV lift above baseline, their number one campaign for the entire peak season. Same product. Same platform. Different understanding of who was buying it.

Gratsi, the DTC Italian wine brand, ran a controlled A/B test on this exact question. They took a single email campaign and split it: half the list got a generic message, the other half got persona-matched creative based on identity data like Active Retiree, Mindful Achiever, Creative Entrepreneur, Health-Conscious Professional. Same subject line, same send time, same list size. Open rates were nearly identical, which means the audiences were consistent.

What changed was what happened after the open. The persona-matched emails drove 47% more revenue, 54% higher click rates, 56% more subscription conversions. The generic email said “It’s Always a Good Time for Gratsi.” The retiree email said “You’ve Earned the Right to Slow Down.” The health-conscious professional got “Zero Sugar. Zero Guilt. Zero Hangovers.” Same wine. Same list. Just talked to them like humans instead of order IDs.

Identity explains behavior, behavior doesn’t explain identity

If you only know someone bought your product twice, you have no idea if they’re about to buy a third time or if they’re done. You’re flying blind. If you know they’re a new parent, a remote worker, a retiree, a competitive athlete, suddenly you have context. You know what problem your product solves in their life, how to talk to them, what else they might need, when to expect them to come back.

A beverage brand might see that someone subscribed and then canceled after two months. If you only have purchase data, that person looks like a churn risk or a lost subscriber. If you know they’re a shift worker who drinks your product before early morning warehouse shifts, you can message them differently than the yoga instructor who drinks it pre-class. One of them churned because the flavor got boring, the other churned because their schedule changed. You can win back one of those people, but you need to know which is which.

You already have the list, you don’t know who’s on it

Magic Mind, the productivity drink brand, installed identity enrichment on their customer base and found out that Kim K had been a customer for nearly two years. No partnership or sponsorship. She had just been buying organically. They sent a care package, she posted it to her Instagram story, hundreds of millions of impressions. The going rate for a sponsored post from her account is in the millions. They got it for the cost of a care package because they knew who was already buying.

Every brand has some version of this. You have customers who are executives, influencers, journalists, investors, people with audiences or purchasing power or strategic value that goes way beyond their LTV. But if you’re only looking at order data, they’re invisible. They look like every other two-time buyer who spent $60, and you’re sending them the same abandoned cart email as everyone else.

Behavior is the what, identity is the why

If you’re only segmenting on the what, you’re optimizing for a pattern you don’t actually understand. You’re building retention programs and acquisition audiences on top of a foundation that can’t tell you why someone bought in the first place or whether they’ll buy again.

The brands winning on this are enriching their customer data with occupation, lifestyle, life stage, and household attributes. They’re segmenting their email lists by persona, not by product. They’re building lookalike audiences based on who their best customers are as people, not what they happened to purchase. And the results aren’t incremental: Gratsi saw 47% more revenue from the same list, Mizzen+Main’s executive audience did 20x ROAS and became their top audience over the biggest shopping weekend of the year, Jolie’s identity-based lookalike drove 412% more sales than the average of everything else they were running.

You can keep segmenting on order count and SKU mix. Most brands are still doing that because it’s easier and the data’s already in Shopify. But easier doesn’t mean better. And the gap between the brands segmenting on purchases and the brands segmenting on people is getting wider every quarter.

Your customers are not their order history. They’re people with jobs, routines, problems your product solves in ways you probably don’t fully understand yet. Segment like it.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

Hi Team!

This newsie comes to you from a quiet corner in Yotpo’s new office in Ramat Gan, Israel.

The 10-hour flight with two little kids was exactly as chaotic as you’d imagine. Ari somehow managed to sleep less on the plane than his nap at home, which I didn’t realize was mathematically possible.

On the bright side, we’re here, caffeinated, and I’ve already consumed an irresponsible amount of delicious coffee.

Now onto this week’s topic.

Last week, I reviewed a handful of retention programs across brands doing anywhere from $10M to $100M+ annually.

Different categories, teams, and growth stages.

Same problem.

Everyone obsesses over the traffic spike. Almost nobody obsesses over what happens after.

A July 4th sale gets weeks of planning. Teams debate offers, paid budgets increase, creative gets refreshed, and landing pages get rebuilt. Everyone focuses on maximizing the five days the promotion is live. Then the sale ends, and everyone moves on.

Strange when you think about it.

If 100,000 shoppers visit your site during a sale and 97% don’t buy, that’s 97,000 people who raised their hand and showed interest. Most brands spend all of their energy generating that attention and very little energy recovering it. That approach is backwards.

The best retention programs I’ve seen treat the sale as the acquisition window. The revenue window is what comes next.

This week, I want to talk about how brands extend a five-day promotion into two or three weeks of additional revenue, and why July 4th is the last major opportunity to get this right before back-to-school and Labor Day.

Let’s get into it.

This week’s newsie is brought to you by Instant.

Here’s the part of a sale almost nobody plans for: the two weeks after it ends.

Picture a shopper who lands on your site during the July 4th sale on a Friday afternoon. She looks at the linen dress, clicks into the sage green, reads a few reviews, adds it to her cart, then gets pulled away and leaves.

The sale ends Monday. She still hasn’t bought. In most programs she now gets the same “you left something behind” email as the person who bounced off a TikTok link nine seconds in.

That’s the gap.

Instant identifies the shopper, remembers it was the sage green and not the black, knows she read the reviews and got close, and sends a follow-up that reflects all of it: the right product, the subject line most likely to get her to open, sent at the hour she actually tends to buy, with an offer only if she needs one.

If she ignores the first email, the next one changes. Maybe it leads with reviews. Maybe it handles fit. Maybe it shows the matching piece. Maybe it saves the incentive for later instead of training her to wait for 15% off.

That’s the difference between a flow that fires and a flow that sells. It’s also why Instant works on both halves of a sale: campaigns create the peak, flows monetize the tail.

On campaigns, that means you stop building ten versions of the same promo by hand. You build one multi-step campaign, let Smart Rules swap the content by shopper, and let Instant write a unique subject line for every single recipient.

The proof is hard to ignore. SKATIE went live in mid-February and crossed $1M in incremental revenue from Instant in four months. They hit 61x ROI in month one, 141x in month three, and are now at 180x in June and still climbing. Open rates across their flows are up about 12% and click rates nearly 18%.

The time savings are real, too. Their lifecycle manager used to spend roughly two hours a day building one email. Now they can build a week of campaigns in about 30 minutes. That’s why some of the fastest-growing brands like David Protein, Naturium, Liquid IV, Jenny Bird, Third Love and TRX made the switch to Instant.

If you’re a Shopify brand doing $300K+ in monthly site revenue, book a demo by June 21st. Instant will get you live before your July 4th sale, and you’ll get a $100 gift card of your choice for showing up.

BOOK A DEMO + GET A $100 GIFT CARD

Most Brands Stop Selling Too Early

One thing I’ve learned after years in retention is that teams consistently underestimate how much revenue is sitting inside existing demand.

The instinct is always to chase more traffic. More Meta spend, more influencers, more creators, more campaigns. But when I audit retention programs, I rarely find brands that have exhausted the opportunity already sitting in front of them.

A shopper visits three product pages, comes back twice, adds a product to cart, and leaves. That’s a follow-up problem, not a traffic problem.

The irony is that recovering interested shoppers is often far cheaper than acquiring brand-new ones. You’ve already paid for the click, earned the attention, and the shopper has already told you what they’re interested in. Yet many brands treat the interaction as over the moment the session ends. The best operators don’t.

Your Best Sale Revenue Often Comes After The Sale

Most teams think about sales in terms of a start date and an end date. Customers don’t.

A shopper who visits on July 3rd and buys on July 10th still came because of your July 4th promotion. The calendar changed, but unlikely that the intent did.

Here’s what nobody talks about: most core flows stop at 1-3 emails, which is why they go quiet right after the sale ends. Extend those same flows to 5 touches, gated on engagement so only the people who haven’t converted keep hearing from you, and the flow keeps working for another two to three weeks.

And the cost of doing that is almost nothing. An email costs cents to send, and the revenue per recipient across a strong flow can run $5 to $25 over its life. So the downside of a few more well-timed, personalized touches is rounding error, and the upside is no longer having to drive 90% of your revenue in the first 5 days of the month and anxiously weighing a summer savings push late July to save your number.

The brands that understand this don’t measure success exclusively by sale-week revenue. They measure how much of the demand they captured over the entire lifecycle of the event. That’s a meaningful difference in how you staff your team, how you build your flows, and how you think about what success actually looks like.

The Traffic Is Expensive. The Follow-Up Is Cheap

This is where many brands leave serious money on the table.

I still see companies spending hundreds of thousands of dollars driving traffic into experiences supported by abandonment flows that haven’t been touched in years. Same subject lines, timing, creative, and message for every shopper.

Think about what that actually is: you are renting back attention you already own.There’s nothing wrong with retargeting, but if someone already visited your site, browsed and added to cart, email and SMS should be your highest ROI channels for bringing them back. The intent is already there. You’re capturing demand that already exists, not creating it from scratch.

Every shopper your flows recover is a shopper your paid team doesn’t have to buy twice.

Why AI Is Suddenly Making This Easier

If extending flows and personalizing every touch is so obviously worth it, why doesn’t everyone do it? Because by hand it’s brutal.

Doing this properly means building dozens of flows, each with variants for different behaviors and segments, writing the copy, choosing the product, setting the timing, and then maintaining all of it as your catalog and pricing change. That’s the work that lives on every retention team’s roadmap and never gets done.

That’s why I’ve been paying attention to what companies like Instant (this week’s sponsor) are doing. The interesting part is that the system is continuously adapting based on actual shopper behavior: it writes the copy, pulls the exact product the shopper looked at from your live catalog, sets the send time per person, and runs more A/B tests in a month than most teams run in a year.

A shopper who viewed the same product page three times and added it to cart doesn’t need the same discount-heavy email as someone who bounced immediately. The high-intent shopper might convert with a softer nudge or even a reminder. The low-intent shopper might need social proof or a limited-time offer. AI can make those distinctions automatically instead of requiring a retention manager to manually build and maintain dozens of segments. AI makes those calls automatically, so that you can spend your time on where curated human attention actually moves the needle.

That shift matters because it means brands can finally personalize at scale without requiring massive teams or complex workflows, across both flows and campaigns. The follow-up becomes smarter, the conversion rates improve, and the cost per recovery drops.

What This Looks Like In Practice

A beverage brand I talked to recently ran a Memorial Day promotion that generated roughly $800K in revenue during the five-day sale window. Strong performance by any measure.

But when they looked at the full two weeks following the sale, they realized their abandonment flows and post-sale follow-up had generated an additional $300K from shoppers who visited during the promotion but didn’t buy immediately. That’s nearly 40% more revenue from the same traffic, with almost no additional acquisition cost.

The difference was that their follow-up was personalized based on actual browsing and engagement behavior, not generic cart reminders sent to everyone who abandoned.

Another apparel brand treated the window after their sale as part of the same event and extended a five-day promotion into nearly three weeks of selling. Shoppers who didn’t convert during the sale got follow up tailored to what they viewed, how many times they came back, and whether they’d engaged with earlier emails. Total event revenue came in more than 50% higher than the sale week alone.

These aren’t outliers. This is what happens when you treat the sale as the beginning of the revenue opportunity instead of the end.

July 4th Is Your Last Big Test Before Labor Day

Most retention teams are already thinking about the back half of the year. Back-to-school, Labor Day, Black Friday, Cyber Monday. The biggest revenue periods on the calendar

July 4th is your last real rehearsal before all of it. It’s your chance to find out whether your flows, your personalization, and your post-sale follow up can actually carry demand past the promotion window, while the stakes are still relatively low.

So this is the week to fix it. If your flows haven’t been updated in six months or longer, update them. If your core flows stop at 1-3 emails, extend them and gate the later touches on engagement. If you’re still sending the same generic cart abandonment email to every shopper regardless of their behavior, add the nuance. If you’re measuring success purely by sale-week revenue and ignoring what happens in the two weeks after, now is the time to expand your reporting.

The traffic you generate during July 4th is expensive. The follow-up is cheap. The brands that win are the ones that treat both as part of the same system.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

Hi Team!

Sorry for the brief absence. After 3+ years of rarely missing a week, I needed a short break.

Naturally, I used that break in the least restful way possible: spending an ungodly amount of time in Claude Code and Codex, dragging my life into the future.

I vibe-coded a bunch of tools for work, tinkered on too many side quests, and somehow found the brain capacity to finally redesign the newsie.

I brought more of my travel obsession into the look and feel, which makes the whole thing feel a bit more Eli-centric, including at least one boarding pass reference nobody asked for.

Outside of that, we celebrated Noah’s 5th birthday with a bouncy house, pizza, and a superhero theme, which is basically the holy trinity of five-year-old joy.

We’re also taking a last-minute points trip to Panama, 30k round trip on United via Air Canada points, and then prepping for 10 days in TLV in June.

On the work side, we launched Yotpo Discover, and we’ve been working on a few new ideas I’ll share here soon.

This week, we’re talking about why “having flows” is not the same thing as having a real retention program, why most brands are missing high-intent shoppers, and how to think about flows as a revenue channel instead of a checkbox.

Let’s get into it.

This week’s newsie is brought to you by Instant.

Most email flows were built for a world where personalization meant adding a first name and maybe showing the product someone left behind.

That bar is too low now.

A shopper who spent 20 minutes across three sessions looking at the same product should not get the same follow-up as someone who bounced after nine seconds from a TikTok click.

Instant helps brands fix this with AI-powered flows built around what shoppers actually do on site. It identifies shoppers, tracks the products they viewed, the variants they came back to, the carts they abandoned, and how close they were to buying. Then it sends personalized flows based on that behavior.

The product in the email matches what they were actually looking at. The timing reflects when that specific shopper is most likely to act. The offer is calibrated based on whether they need a nudge or would likely buy at full price.

The results are hard to ignore. Karen Kane added $1.1M in incremental revenue in 90 days. The Collagen Co. drove $10.6M. Linda’s saw a 109x ROI. July drove $3.9M. Nakie drove $2.7M.

Instant is quick to start with, easy to expand, and built to handle work your team never gets to. Most brands go live in under 30 minutes and see the lift within days.

It is as easy to turn Instant email flows on as it is to pause them. No long-term contract, no drawn out setup, and no extra lift from your team.

Book a quick demo with Instant before Memorial Day and get 50% off your first 60 days.

GET 50% OFF YOUR FIRST 60 DAYS

The “we have flows” problem

Ask a retention team about their email program and you will usually hear the same few things. Welcome sequence is live. Cart abandonment is live. Post-purchase is live. There is probably a winback flow somewhere in the account, though nobody is completely sure when it was last updated or whether it still references a product that got discontinued.

The team feels good about it because someone did the actual work to build those flows, and the program really is better than whatever came before it. Both things can be true. The program can be better than it was and still not be good enough.

A flow can be live and still miss most of the intent happening on your site. It can drive revenue and still underperform badly. It can look perfectly healthy in a dashboard while leaving money behind because the message, timing, offer, and product recommendation are too generic.

I once looked at a brand where more than half of email revenue was coming from the welcome flow. The team was proud of that number, but it should have made everyone uncomfortable. If the welcome flow is carrying the whole program, the rest of the lifecycle is probably underbuilt: browse abandonment is weak, winback is a blanket discount, price-drop isn’t running, and post-purchase is either too generic or pushy.

The brand thinks email is working because one room in the house looks nice. The rest of the house is empty.

Most brands stop at the obvious flows

Welcome, cart, checkout, post-purchase. Those are the basics, and that is also where most brands stop.

Customer behavior is more specific than those four buckets. Someone may view the same SKU five times. Someone may buy from one category and repeatedly browse another. Someone may purchase three times, then slowly go quiet. Those are all signals, and most programs treat them like noise.

What you end up with is a whole layer of mid-intent shoppers who are warmer than cold prospects but not close enough to convert on their own. The program has nothing thoughtful built for them.

A good retention program responds to meaningful behavior across the whole shopping journey. If a previously active customer starts going quiet, the winback should reflect what they used to buy and whether they are worth protecting margin for. None of this is new in concept. Every retention team has some version of it on a roadmap. The reason it stays on the roadmap is that building it properly is a pain. Each flow needs logic, copy, creative, segmentation, and ongoing maintenance. That overhead is what kills execution.

Campaigns build context. Flows close the loop.

Campaigns and flows should not have the same job.

Campaigns are for brand moments: product launches, seasonal pushes, editorial notes, big promos. They go out because the brand has something to say. Flows fire because the customer did something: viewed a product, browsed a category, abandoned a cart, stopped buying.

That means the message should be much closer to the behavior. A browse abandonment email does not need to open with a brand manifesto. The shopper was just on the PDP. They need the product they viewed, a clean reason to reconsider it, and an easy path back. A cart abandonment email does not need three lifestyle modules. Maybe they need shipping clarity. Maybe they need reviews. The job is to close the loop, not tell the brand story again.

Too many brands treat flows like smaller campaigns. They make the creative prettier, soften the message, add more brand language, and end up with something that looks nice but does not do the job. Pretty is nice. Useful converts.

Discounts are covering for weak relevance

A lot of brands use discounts to cover for lazy timing and generic follow-up. Someone abandons cart, so they get 10% off. Someone goes quiet for 60 days, so they get 20% off. Someone views a product twice, so they get routed into the same offer path as everyone else who did not convert.

This works just well enough to become dangerous. The dashboard shows revenue, the team feels validated, and nobody asks how many of those customers would have bought anyway, or whether what they actually needed was better timing, social proof, or simply the exact variant they had already shown interest in.

Basically, you trained your best customers to wait you out.

Lazy discounting erodes margin on conversions that were already going to happen, and once you set that expectation across your list, it is expensive to walk back. A loyal customer who buys every six weeks may not need a coupon on day 42. A first-time shopper who came back to the same PDP three times may need proof more than they need 10% off. When every shopper gets the same offer, you give away margin to people who did not need it and still fail to convert people whose actual objection was something else. Discounts are not bad. Lazy discounts are bad.

Open rates are not the scoreboard

Most retention teams are looking at the wrong numbers and calling it a strategy review.

Open rate has been unreliable since Apple’s privacy changes. In a lot of cases you are counting server pings as proof of engagement. CTR is more useful but still does not tell you whether the program is improving customer value or just catching shoppers who were going to buy anyway.

The better number is revenue per recipient, not per send, per person on the list. If it is growing month over month, your program is compounding. If it is flat or declining, something structural is broken and no subject line test is going to fix it.

The other number worth tracking honestly is incremental revenue: what you generated because the email was sent to someone who would not have converted without it. Email platforms are very good at taking credit. The actual lift is usually smaller than the dashboard suggests, and the programs that hold up under honest analysis are the ones where behavioral personalization is driving real additional conversions, not just getting credit for ones that were already happening.

What to audit this week

Start with the percentage of site revenue coming from email. If it is under 35% across campaigns and flows, your retention setup is missing the mark.

Then pull your email revenue breakdown by flow. If more than half is coming from the welcome series, you do not have a full lifecycle program. That means the same gaps are hiding in plain sight: the browse flow is not using the exact SKU, variant, reviews, or collection someone cared about. Cart and checkout are stopping too early instead of running a real sequence with smarter timing, proof, shipping clarity, and offer logic. Winback is just a blanket discount. Post purchase is not creating the next order.

Most brands will find the same thing: the foundation exists, but the coverage is thin.

Once you see it, the work becomes clearer. If someone looked at the same product three times, they should not get a generic “you left something behind.” If someone already bought the item, they should not get the new customer offer. If someone buys every 45 days, they should not get a panic discount on day 12. If someone is one click away from buying, do not bury the product under a paragraph about your mission.

Your customers are already telling you what they care about through their behavior. Most brands are only listening to the loudest signals. The brands that win will be the ones that hear the quieter ones, too.

The truth is, you probably already know this. These improvements are on your roadmap, but they keep getting pushed back. That’s where AI changes email and why you should take the leap: it gives your team the bandwidth to finally execute.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email! Cheers, Eli 💛

Hi Team!

The sun is shining, flowers are blooming, seasonal depression is nearly gone, and Noah is almost 5 YEARS OLD???

Where did the time go?

Something I’ve been thinking about since watching Noah start to communicate what he actually thinks: the gap between what people say directly to you and what they say when you’re not in the room.

Your CX team has gotten really good at one thing: managing what comes through the inbox.

Tickets are tagged, response times are solid, and AI handles the easy stuff. But the feedback that doesn’t come through support is growing way faster than the feedback that does.

Customers are posting about your product on TikTok, comparing you in Reddit threads, and leaving reviews explaining exactly why they switched.

None of that shows up in Zendesk. You optimized the speed at which you respond to a shrinking percentage of actual customer sentiment.

This week, I want to talk about why most brands are operationally faster but strategically blind, and what it actually looks like to build a feedback system that sees the whole picture.

Let’s dive in.

This week’s newsie is brought to you by Syncly.

Your CX team is fast. But fast at what? Managing the 10% of feedback that actually reaches your inbox.

Syncly was built to close that exact gap.

It pulls in feedback from support tickets, product reviews, and social channels into one place where you can actually search, ask questions, and spot patterns without spending a week pulling exports.

Here’s what stood out to me:

1️⃣ Ask it like you’d ask ChatGPT: Type “what are customers saying about our new product?” and get actual answers backed by real customer quotes. No dashboards to configure. No reports to wait for.

2️⃣ It sees what your helpdesk can’t: The whole point is that it goes beyond tickets. Amazon reviews, TikTok comments, Reddit threads — the places your customers talk when they’re not talking to you. That’s where the real signal lives.

3️⃣ You find the problem before it becomes a ticket: Real-time alerts on sentiment shifts mean you can catch issues while they’re still a Reddit thread, not after they’ve become 200 angry emails.

Brands like Fenty Beauty, Kosas, Neuro, Hum Nutrition, Needed, Organika, Banza, and Adidas are already using Syncly to turn scattered feedback into decisions they actually feel confident making.

If your CX stack only sees what customers say to you, you’re missing the bigger picture. Syncly shows you what they’re saying about you.

Book a demo with their co-founders to get a free demo with your own data.

Book a Demo

Think about how you actually behave as a consumer

When a product disappoints you, most people don’t open a support ticket. Sometimes they do, maybe, if the issue is transactional: wrong item, missing package, billing error. You need a resolution, and only the brand can give it to you.

When the issue is qualitative, the formula changed, the fit is off, the experience wasn’t what you expected, most people don’t email support. They post about it, leave a review, reply to someone else’s comment, and vent in a subreddit all in the same week.

The volume of external feedback is outpacing ticket volume by multiples, and the gap keeps growing.

Your customer just spent 11 minutes explaining why your product sucks in a Reddit thread with 400 upvotes. You’re still waiting for them to email you.

Side note: At OLIPOP when we shifted from more than 50% ecom in peak COVID to mostly retail in 2022. We saw almost 0 customer complaints from retail, for the same products that had tickets coming in on ecom.

This hits omnichannel brands even harder.

What seeing everything actually looks like

The shift is structural. It’s about whether your feedback infrastructure treats support tickets as the only input, or as one input among many. When you can pull in reviews, social comments, and community discussions alongside your ticket data, a few things change, and they compound over time.

• You stop reacting and start anticipating: A sentiment shift on Amazon reviews often precedes a spike in support tickets by weeks. If you can see that shift in real time, you can act before it compounds.

• You stop guessing what customers actually care about: Internal teams spend hours debating what language to use, what features to prioritize, what’s driving churn, while customers have already explained it in a TikTok comment thread or a subreddit you didn’t know existed. If you’re not watching those channels, you’re making product decisions based on incomplete data.

• You stop optimizing for the wrong problems: Some issues generate many tickets but don’t affect retention, while others generate none but drive silent churn at scale.

If you can only see ticket volume, you’re allocating resources based on noise, not impact. You’re fixing the loud thing, not the thing that matters.

The uncomfortable reality

If a creator with half a million followers posted a negative video about your product tomorrow, how long would it take your team to know about it. If the answer is “when someone on the team happens to see it,” you have a blind spot that no amount of ticket automation can fix.

You’ve built a system that’s soooo good at handling customers who reach out, but completely useless for customers who don’t.

That’s most of them, by the way. The majority of dissatisfied customers never open a ticket. They just stop buying, and maybe they tell a few thousand people why on the way out.

The brands winning retention in 2026 aren’t faster at support. They figured out how to listen everywhere. They treat social mentions, review sentiment, and community discussions as first-class inputs, not nice-to-haves.

They can spot a product issue in a Reddit thread before it becomes a return trend. They know when a TikTok creator’s review is about to drive a spike in new customer sentiment. They’re not reacting to problems after they hit the inbox. They’re seeing them form in real time and deciding whether to act, adjust, or ignore based on actual signal, not gut feel.

Your ticket volume is a lagging indicator. Everything happening outside your inbox is a leading one, and right now, most brands are flying blind to the signals that actually matter.

Start with one channel this week

This isn’t about hiring a social listening team or adding another dashboard to your stack. It’s about rethinking what “customer feedback” actually means in 2026. Support tickets are one input, a valuable one, but if that’s the only channel you’re watching, you’re optimizing a shrinking slice of the conversation.

Pick one place your customers talk about you that isn’t your inbox. Reddit, TikTok comments, Amazon reviews, whatever. Spend 30 minutes reading through it. Not to respond, just to see what they’re actually saying when you’re not in the room.

You’ll find something your team has been debating for months already solved in a comment thread, or a complaint you didn’t know existed showing up 50 times in slightly different words.

The brands that win will be the ones who can see the full picture: what customers say to them and what they say about them. That’s the job now. Everything else is just speed.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

P.S. If you want to figure out how to get your brand to rank high in LLMs and show up in ChatGPT, Gemini, and more… check this out.

Hi Team!

As much as Vegas is a depraved, soulless city, Shoptalk was still fun as hell.

We spoke to hundreds of brand executives, vendors, and operators trying to figure out what the next two years will look like, and I walked away with a few things rattling around my head that I can’t stop thinking about.

I took a shot every time I saw “agentic” on a booth and was drunk at 9 am. Everyone’s talking about product discovery and shopping via LLMs. Loyalty programs are finally starting to look interesting and experiential.

And weirdly, even though there are a dozen players in the agentic CX space, almost nobody is thinking about what happens to the 10% when AI handles the other 90% of your tickets.

Let’s chat about the big themes.

This week’s newsie is brought to you by Horatio and Mentors CX.

I often get asked how to upskill a CX team without sending everyone to a $3,000 conference or sitting through a webinar that could have been an email.

Horatio and Mentors CX put together something genuinely worth your time.

It’s a 12-module CX masterclass built for operators who actually run these teams. Valued at $950, they made it available for free.

It includes: journey mapping, VoC programs, AI, omnichannel strategy, team building, the whole thing.

Each module comes with real frameworks and downloadable guides so you can apply it immediately, not six months from now when you’ve forgotten everything.

I’ve seen a lot of “resources” come across my inbox, and most of them are lead gen dressed up as education.

This one is different. It’s hosted on the Mentors CX site, fully free to access, and built with the kind of depth that reflects what Horatio actually does day to day running CX operations for some of the best brands out there.

If you’re a CX leader trying to get your team sharper, or you’re building out a function for the first time and want a real framework to work from, this is worth a few hours of your time.

ACCESS THE FREE BOOTCAMP

AI Consulting & Implementation Services

Agentic AI isn’t just about cutting costs. It’s about transforming how your business supports customers.

Horatio helps companies deploy and optimize agentic AI tools while integrating high-touch human support where it matters most. The result: more efficient operations, smarter automation, and better customer experiences.

And once AI handles the repetitive 80–90%, what’s left is the 10% that actually defines your brand and drives loyalty: the complex, high-stakes conversations that require real human judgment. That’s where having the right support team in place matters most.

If you’re ready to implement AI, or need a partner to guide you in the right direction, book a call with the Horatio team.

Explore AI Consulting & Implementation

“Agentic” is the new “blockchain”

I lost count of how many vendors told me their product was “agentic.” The word showed up in pitch decks, booth signage, demo scripts, and LinkedIn posts written in real time at the conference. And when I asked what that actually meant for the customer experience, the answer was usually some version of “well, it can handle multi-step workflows autonomously.”

Cool. So can a Zapier flow from 2019.

Sure, some of the tech is real. MCPs, multi-agent orchestration, whatever the vendors are hawking this quarter. It’ll probably matter at some point. But most brands I talk to are still trying to get their basic ticket deflection above 40%. Building for the future is great, but I’d reckon that only 2-5% of your customers would actually use your MCP today.

One brand exec I talked to put it perfectly:

“We implemented the fanciest AI agent setup you’ve ever seen. Customers still email us asking where their package is, and half the time the agent can’t pull the tracking number because it’s in a different system.”

The tooling is racing ahead of what brands actually need, and most of the ones I talked to are drowning in sophisticated solutions to problems they don’t have yet, while the simple stuff still doesn’t work.

Shopping via LLMs is coming and nobody knows what to do about it yet

Perplexity, ChatGPT, and Gemini are all racing to let people shop directly in the interface. Brands know it’s happening, they know they need to show up wherever their customers are, and almost nobody has a plan beyond “I guess we should make sure our product data is clean.”

I talked to a VP of Growth who said their team spent a month trying to figure out how to optimize for LLM-based product discovery, realized they had no idea what that even meant, and gave up. She’s not wrong to be confused because the playbook does not exist yet. SEO took a decade to standardize. This is month six.

The few brands I spoke with that are ahead of the game are obsessing over structured data, clean product feeds, and making sure their brand story doesn’t get mangled when some LLM scrapes it.

A few folks I spoke to are leveraging Shopify Knowledge Base to embed their product catalog so models actually show their stuff when someone searches for the best cruelty-free skincare for dry skin.

Will it pan out? Who knows. But they’re doing something instead of sitting around waiting for a Harvard case study in 2027.

(Shameless plug: Yotpo launched Yotpo Discover to tackle this, and we have a few hundred brands on the waitlist in the first few days. )

Loyalty programs are finally interesting again

I have spent years complaining that loyalty programs are just frequent flyer miles for people who buy deodorant. I still stand by that take for 90% of what I’ve seen. But I walked away from Shoptalk seeing a handful of programs that actually made me think, “Oh, that’s different.”

260 Sample Sale has a program where top-tier members get to skip the line at their physical sales. It sounds small, but if you have ever stood outside a sample sale in New York in February waiting to get in, you know that it is worth more than 500 points toward free shipping.

Fleur de Mal is letting top-tier loyalty members give input on product development, which turns your best customers into collaborators instead of just repeat purchasers.

Points are fine, discounts serve a purpose, but nobody ever felt special because they got 50 points for typing in their email. The brands that actually get it treat them like actual people with names who deserve better than a robotic birthday discount that got queued up at 2 am by some sad automation workflow. 🙃

Everyone’s fighting for AI CX, nobody’s building for the 10% that matters

There are at least 30 vendors right now, all fighting to be the AI-for-CX platform that wins the next five years. And most of them are building the same thing, automating password resets, order tracking, return labels, the stuff that should have been automated in 2018.

They will all do fine, some will win, some will get acquired, but the thing I could not stop thinking about is that almost nobody is building for what happens when that 90% goes away.

When AI handles the repetitive stuff, what is left is the 10% hard conversations.

The customer whose $400 order showed up damaged the morning of an event, or the angry email that is not really about the product, but the fact that someone feels ignored.

Those tickets require judgment, empathy, tone calibration, and the ability to make a call that is not in the playbook. Those edge cases that don’t inherently require a human, but would positively impact LTV and brand if you did get the right human response in the mix pretty quickly.

I talked to exactly zero folks focusing on this.

Everyone is still pitching “we can automate 80% of your tickets.”

The brands that win are going to be the ones who realize that AI is not the endgame, it is the thing that frees up your team to do the work that actually builds loyalty, even if it’s only 10-20% volume-wise.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

P.S. If you want to figure out how to get your brand to rank high in LLMs and show up in ChatGPT, Gemini, and more… check this out.

Hi Team!

If you are reading this, you have almost made it to the end of Q1.

Will you be at Shoptalk next week??? If you are, please swing by booth 2670 and say hi.

I had three conversations there last year about hiring for CX, and every single one started the same way: we can’t find good people.

SORRY for the hot take, but I think the problem is you’re looking for the wrong people.

You’re still hiring people to answer tickets, and that part of the job is slowly going away.

This week, let’s talk about what the CX role actually turns into once AI swallows the grunt work, why your best hire probably doesn’t know Zendesk exists, and the one skill nobody bothers screening for.

Let’s get into it.

This week’s newsie is brought to you by KODIF AI.

As your brand scales, customer conversations explode across support, pre-purchase, and every corner of your site. The question becomes: do you build a larger team, or a smarter one?

The AI CX market is crowded, but here are a few reasons KODIF stands apart from what I’ve seen:

1️⃣ Built for complexity — not just basic FAQ.

From what I’ve seen other AI agents break when SOPs get multi-system and conditional. KODIF asks you to give them your most complex SOP, and automate that.

For example, I saw how KODIF chat agent handled Aura Frames complex troubleshooting product issues end-to-end — walking a customer through diagnostic steps and, if needed, triggering a replacement part order inside the same flow. I was impressed.

2️⃣ An AI CX team > one AI Agent

They build AI CX Team, purpose - specialized agents that mirror CX roles: an AI Support Agent for resolution, AI Concierge for pre-purchase and conversion, AI Analyst for insights, and AI Manager for continuous improvement. It’s a team model, not a catch-all model.

3️⃣ Deep ecommerce integrations (not surface-level)

They’ve built 178+ ecommerce integrations and shipped 88 new ones in 2025 alone.

You all know that matters because automation only works when the system can actually take action, not just answer questions.

Some recent cool reviews on the success stories:

Dollar Shave Club:

“The customizability of KODIF is what excites me most. I’m in there every day. Containment is great and all, but quality matters just as much or more. Our automation rate is above 70% but more importantly we are leveraging KODIF for retention.”

— Jack Dukesherer, Sr. Manager, Member Engagement Ops

Fellow Coffee:

“The Chat AI Agent can walk someone through calibrating their grinder — it’s super resourceful with the right inputs. Furthermore, address changes and cancellations? Completely automated. I haven’t thought about a cancellation in ages.”
— Cindy Rodriguez, Director of CX

Just Food For Dogs:

“We started with deflection as the goal, but it turned into more — now the AI helps customers discover products, understand ingredients, and convert. That’s real ROI.”

• Cristina Fucchi, Chief Customer Officer

Something that stood to me is their founders’ strong engineering background. Their CTO, Norm previously built Uber’s customer support automation platform, automating 150 million customer requests per month.

KODIF is trusted by top ecommerce brands such as True Classic, Ruggable, Dollar Shave Club, Liquid I.V., One Skin, Aura Frames, Fellow, Neuro, Who Gives a Crap, Million Dollar Baby, Ivy City, Helix Sleep and many others.

Many teams review their CX stack in Q1, and if your support workflows are complex, it might be worth a conversation with Chyngyz and the KODIF team to see if it’s a fit. They promise to go live in 3-5 days.

Also, they are offering my readers 3-months for free… Just hit up their team and drop my name, Eli Weiss. This offer is valid only through March 31, 2026.

Book a demo and meet the founders

The job posting is a lie

Pull up your last CX job posting. Does it ask for experience with Zendesk or Gorgias, strong written communication skills, and the ability to handle high ticket volume?

Congrats, you just described someone whose primary value is about to get automated into oblivion.

Sorry, but most CX job descriptions are written by someone in HR who last talked to a customer in 2019. They ask for platform experience and typing speed like they’re hiring a court stenographer.

Meanwhile, the actual job, six months from now, is going to be 60% emotional triage and 40% creative problem-solving a small amount of high-value tickets under pressure, and nothing in that posting screens for either.

I met someone last year who’d been managing a boutique hotel front desk for six years. Never touched a helpdesk platform. Never heard of CSAT. But they could read a room in four seconds, de-escalate a screaming guest while rebooking their suite, and remember that the couple in room 4B mentioned it was their anniversary.

That’s the person you want on your CX team when AI eats 80% of your inbound volume. The problem is most job postings would filter her out in round one because she doesn’t have two years of ecommerce support experience.

You know that hire who crushes tickets like a robot but goes silent when someone calls in tears? That worked yesterday. Now you need someone who stays calm when the building’s on fire, makes the call without checking Slack seventeen times, treats an angry customer like a real problem instead of ticket number 4,352. That old playbook from two years ago is gathering dust.

Screen for hospitality, not helpdesk

When AI handles password resets, shipping updates, return labels.. what’s left?

The disasters, the stuff that makes people pick up the phone or write an email in all caps at 2am.

Like a $400 order that showed up destroyed the day before a wedding. Someone who just discovered their subscription renewed after they’re sure they cancelled it three weeks ago. A customer whose 78-year-old mother accidentally ordered six months of product, and now they want to return it, but your policy says no refunds after 30 days.

Sure, tagging tickets correctly is nice. But what you actually need is someone who can take the emotional hit, make a call on the spot without running to a manager, and leave the customer feeling heard without lighting your margins on fire. That’s hospitality.

The best CX people I’ve ever hired came from hospitality. Four years managing a busy Brooklyn brunch spot, handling Karens mad about cold eggs, a kitchen 45 minutes behind, servers walking out mid-shift.

They can learn Gorgias in a week. But the instinct for matching tone to the situation, apologizing in a way that lands, breaking policy when it matters and holding the line when it doesn’t… You don’t get that from a PDF.

Front desk managers, restaurant hosts, school teachers, and retail folks. They’ve spent years absorbing customer chaos under pressure, just not in your ticketing system.

They can read what someone’s really upset about instead of taking their words at face value. They de-escalate without a script. They make calls when things explode, no permission slip required. But your current job posting doesn’t speak their language.

The one skill nobody screens for

The one skill very few people screen for is: can this person actually read a room instead of robotically following a script?

A customer whose $12 lip balm is two days late does not need the same response as someone whose $400 anniversary gift showed up shattered the morning of. One needs a quick acknowledgment and a tracking link. The other needs someone who can say oh god, that’s awful, let me fix this right now, and actually mean it.

Great CX people do this on instinct. Warm and casual one message, urgent and apologetic the next, whatever the customer actually needs right now. Good people catch tone shifts mid-conversation, adjust without breaking stride.

Most people can’t do this. They pick one tone and ride it into the ground. They’re either overly formal with everyone or way too casual when someone is genuinely pissed. They don’t calibrate.

And the problem is you can’t really test for this in an interview. You can role-play a scenario or two, but the person who nails a fake angry customer exercise might completely botch it when a real one comes through and the stakes actually matter.

The closest proxy I’ve found is to ask about a time they had to handle someone who was irrationally upset about something that wasn’t their fault, and how they handled it. Not what they said, but how they felt during it, and what they did after.

If they tell you a story about absorbing someone’s anger and then figuring out how to fix the underlying issue even though the person was being unreasonable, that’s your hire. If they tell you a story about how they followed the script and escalated to a manager, keep looking.

AI’s chewing through the boring tickets. The repetitive stuff, the no-brainer questions, the ones where you don’t need judgment or empathy or any ability to read a room. What’s sticking around is actual work. And the people who are killer at it are not scrolling LinkedIn for your Zendesk power user posting.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

P.S. If you want to figure out how to get your brand to rank high in LLMs and show up in ChatGPT, Gemini, and more… check this out.

Hi Team!

The sun is finally shining on the East Coast. We are in the 70s this week?! Seasonal depression is gone. Everything feels possible.

Which makes this the perfect time to talk about something nobody wants to hear. You’re probably not operational enough to scale. I know you want to talk about brand positioning, creative, and market strategy.

You want to workshop your next campaign or debate packaging redesign. Nobody wakes up excited to audit their SKU catalog, renegotiate their 3PL contract, or document their return policy.

But this kills growth. Going from 20 to 50m, operations are often the bottleneck. Let’s get into it.

Global growth fails when checkout experience doesn’t match customer expectations.

International shoppers expect the same transparency they get domestically. When costs change late in checkout or at delivery, trust drops—and so does lifetime value.

Passport Global helps brands like Comfrt, HexClad, Ridge, Carpe and Ogee deliver a consistent, conversion-optimized global experience with:

• Accurate landed costs shown before purchase

• Flat-rate international shipping strategies

• Clear checkout price breakdowns (including new Shopify integrations)

• Localized storefront and payment experiences

• Guidance on navigating tariffs and recovering refunds

That means higher conversion today and stronger retention tomorrow.

👉 Schedule a call to explore your global opportunity

Want a quick benchmark first?
Get your free, personalized International Growth Scorecard to see how your brand stacks up against global sellers, with actionable tips to improve the customer experience.

Schedule a Call

The $10M to $20M Death Zone

You hit $10M and things feel chaotic but manageable. You’re scrappy, you figure stuff out, and customer service is you and two part-timers in a shared inbox. Inventory planning is a spreadsheet you update when you remember. Your 3PL sends invoices and you pay them without really auditing the line items.

Then you push to $20M, and suddenly you’re drowning.

Customer service is underwater with a 48-hour response time. Inventory is a complete mess. You’re out of stock on your hero SKU while sitting on four months of a variant nobody wants. Your 3PL is charging you surprise fees you didn’t know existed. Returns are eating 6% margin and you have no idea why it jumped from 4%. You can’t launch the new product you’ve been planning because your systems genuinely cannot handle more complexity.

Most brands stall here. The operational foundation cracks under the weight. You’ve got demand. You’ve got a good product. But the infrastructure? Duct tape and a dose of hope. Every order stresses a system already breaking.

Your team is burned out. Same fires, different week. I’ve watched this happen a dozen times.

Nobody Wants To Do The Boring Work

I get it. You started this company with a vision for a brand, a point of view, and a product that should exist. You want to build, create, tell stories, and connect with customers. That’s the fun part. That’s why you started this.

SKU rationalization is not fun. Auditing your product data so every variant has clean attributes is not fun.

Building an actually functional inventory planning model is not fun. Documenting your return policy so it’s consistent across email and your site and your CX team is not fun. Negotiating better 3PL rates and understanding what a pick and pack fee actually includes is not fun.

Hitting $25M and having everything break is also not fun. Spending half your day putting out operational fires instead of building the business is not fun. Watching your unit economics erode because you’re getting nickel-and-dimed on fulfillment and hemorrhaging margin on returns you could’ve prevented is not fun.

Most founders would rather brainstorm a brand activation than fix their warehouse receiving process. Then they wonder why they can’t scale past $25M without the wheels coming off. The answer is sitting right there in their 3PL contract and their product catalog and their inventory spreadsheet. It’s just the answer they don’t want to hear.

The brands that break through do the boring work first. They know their COGS down to the penny. They’ve modeled out their fulfillment costs by SKU. Every process is documented, not trapped in someone’s head. They’ve invested in the systems and infrastructure that make speed possible without chaos.

The boring stuff is what scales brands.

What Operational Maturity Actually Looks Like

• Clean product data: This means every SKU has accurate dimensions, weight, and attributes. Your 3PL isn’t guessing. Your website filters actually work. Your inventory system knows what you have. Sounds basic, I know. I’ve seen $50M brands where the product dimensions in Shopify don’t match what’s in the warehouse, so every order is a surprise fee waiting to happen.

• Documented processes: Your CX team has a source of truth for how to handle every scenario. New hires can onboard without just shadowing someone for three weeks. You’re not reinventing the wheel every time. When someone asks how to handle a damaged item return, there’s a doc for that. When you need to explain your exchange policy, it’s written down somewhere other than Slack. (FYI today you can build SOPs like this in Claude in 5 min + a few tokens)

• Inventory planning that actually works: You have a model, you know your lead times, and you know your sell-through rates by SKU. You’re making purchase orders based on data, not vibes. You’re not sitting on dead stock while being out of stock on your best seller. You’ve built buffer stock targets that account for variability. You’ve negotiated payment terms that give you breathing room.

• Return policies that make sense start with actually analyzing why products come back. You’ve tightened your policy where it matters. You’ve made it easy where it builds loyalty. You’re not just eating costs because you’re afraid of upsetting someone. Maybe you’ve realized that one SKU has a 15% return rate because the size chart is wrong, and fixing the chart would save you $80K a year. Maybe you’ve learned that your most loyal customers never return anything, so you can afford to be generous with them.

• Customer service tools that don’t suck: You’re not still using a shared Gmail inbox at $15M. You have a real helpdesk. You have macros and reporting. Bottlenecks are visible. You know which types of inquiries take the longest to resolve. You know which agents are underwater. You can forecast headcount needs instead of hiring when you’re already drowning.

This stuff is boring. It’s also the difference between a brand that stalls at $20M and one that gets to $50M without imploding.

The Brands That Scale Are Operationally Obsessed

The founders who make it aren’t just good at brand. They’re operationally obsessed. They know their 3PL contract inside out. They’ve built inventory models that actually work. They’ve rationalized their SKU catalog so they’re not carrying 47 variants when 12 would do the job better.

They do the boring work because operations are the brand. Two-day shipping instead of five? Brand. One interaction to resolve an issue instead of four? Brand. Never running out of your hero product? Brand. That’s what operational excellence builds.

Your operations are customer-facing whether you think about them that way or not. Every fulfillment delay is a broken brand promise. Every out-of-stock is a moment where someone goes to your competitor. Every clunky return experience is a reason they don’t come back. You can have the best creative in the world and it won’t matter if your operations are a mess.

Brands that scale execute on creative because the foundation holds. New product launches don’t break systems. Spend increases don’t collapse customer service. Speed comes from infrastructure. You move fast when you’re not constantly firefighting.

Operational chaos doesn’t get fixed with branding. A broken fulfillment process doesn’t care about your creative strategy. Eventually, you build the boring infrastructure. It’s the only way to scale.

Yeah, the sun is shining. Everything feels possible. But to actually get there, you might need to audit your 3PL contract, fix your product data, and build a real inventory model.

Not sexy. Do it anyway.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

P.S. If you want to figure out how to get your brand to rank high in LLMs and show up in ChatGPT, Gemini, and more… check this out.

Hi Team!

March is chaos. Wife is working 80-hour weeks and I’m solo for the best hours of the day with both kids. 6-8am and 6-8pm, twice a day, every day. Just me and them. It’s exhausting and also the best part of my week, which is a weird thing to say when you’re running on four hours of sleep.

Aside from that, my sister got married last night, and a ton of my friends and family are in a war zone. But we’re all good considering.

This week, I want to talk about something that’s been rattling around in my head since I left Jones Road. If AI handles 70-80% of CX chats in the next year or two, what does the job of a CX hire actually become? It won’t be the things we looked for back then, which were speed, macros, or tool fluency.

So what does the new CX perfect hire look like?

Let’s get into it.

This week’s newsie is brought to you by Armin!

If you run an ecommerce brand, you know the pain: 60-80% of your support tickets are the same repetitive stuff. “Where’s my order?” “Can I get a copy of my invoice?” “I need to change my address.” Your team spends their day copy-pasting tracking links instead of solving complex inquiries.

ArminCX was built specifically to fix this.

1️⃣ Tickets that resolve themselves. ArminCX’s AI reads the customer’s message pulls the order number, checks shipping status in your backend, grabs the tracking link, and sends a complete reply. Brands are seeing 50-80% of tickets fully automated.

2️⃣ Deep integrations where it matters. Shopify, WooCommerce, Klaviyo, your local shipping carriers ... ArminCX connects natively to the systems your team actually uses. Agents can refund, cancel, or update an address without leaving the tool.

3️⃣ Every channel, one inbox. Email, WhatsApp, Instagram, live chat, phone, etc. All in one place with full order history in the sidebar. No more tab chaos.

If your support team is buried in repetitive tickets and held together by five different tools, ArminCX is worth a serious look.

Check them out!

Learn More

The Old CX Playbook Was Built for a Different World

For most of the last decade, CX hiring was optimization-driven.

You looked for people who could move fast, hit SLAs, manage a ticket queue without drowning, and know their way around Zendesk or Gorgias. If someone had seen a few different brands, had clean escalation instincts, and could churn through volume without making a mess, you felt safe.

That playbook made sense when every ticket required a human. When “support” meant a person reading and responding to everything, throughput mattered. Macros and process-following mattered. You could argue about tone later, but first, you had to survive the queue.

The problem is that we are hiring for that world while the ground is shifting under us.

If AI handles the repetitive layer, then speed and tool fluency stop being differentiators.

AI Takes the Volume, Humans Inherit the Weight

In an AI-first world, most of the ticket types that make up daily CX life get absorbed.

WISMO, address changes, invoice copies, subscription pauses, and clean refunds that fall neatly inside policy. The stuff that chews up an entire day and gives you nothing to show for it besides “we cleared the backlog.”

AI will do that faster than any team of humans, and it will do it consistently. That is not a scary statement, it is simply the direction everything is going.

So what is left for people?

The edge cases, emotional moments, weird situations, and conversations where the customer is not just asking for information, but making a decision about whether they still trust you.

Those are lower volume and much higher leverage.

This is where CX stops being a ticket function and starts being a relationship function.

A Small Story That Made Me Hate Myself

I had a moment recently that made this feel very real.

I have spent thousands of dollars with a brand over the years. I have recommended them to friends. I have been the kind of customer brands want, the one who keeps coming back without needing a promo every time.

A recent order arrived and an item broke after a few days of usage. I reached out, and I was five days outside their return window.

The responses I got were polite and technically correct. They referenced policy, they were structured, and yet they also felt cold in a way that is hard to unsee once you feel it.

I explained the situation, mentioned my history, tried to be reasonable. They told me, for the fourth time in the thread, that nothing could be done.

At some point, I realized something that surprised me.

As a VIP customer, I would have rather had AI let me down slowly than have a human read my email and then choose to be policy-first in such a blunt way. At least with AI, you can tell yourself it is a system limitation. When a human does it, you feel the message underneath the message, which is that you are not worth the exception.

The dollar amount did not matter as much as the interaction did.

It changed how I feel about the brand.

Hospitality and CX Are Going to Merge

Imagine you are a regular at a restaurant. You have eaten there for years, know the staff, bring friends, and tip well.

One night, the food comes out mediocre. You mention it calmly, not in a dramatic way, just honest feedback.

If the manager walks over and reads you the policy off the back of the menu, you are done. You might finish your meal. You might even pay without making a scene. You are not coming back.

That is the case in hospitality.

It is also CX.

The only difference is that ecommerce has gotten away with being colder because you cannot see the person on the other side of the screen. AI is about to remove even more of that human friction, which makes the moments where a human does show up even more important.

In 2026, the human agent is not primarily a problem solver. The human agent is the person who turns tension into trust when the automation does not.

That is the job.

What Hiring for CX in 2026 Actually Means

This is where I think a lot of traditional CX profiles will struggle.

Many support environments train for compliance, risk avoidance, and consistency. They reward sticking to macros, escalating quickly, and staying inside strict guardrails. Those instincts are not evil, and they exist for real reasons, but they are not the instincts that create loyalty when the only tickets humans see are the ones that matter.

If AI takes the 80 percent, humans own the 20 percent that drives retention.

That means you should hire for a different set of traits:

• Judgment under ambiguity, because the “right” answer will often be situational.

• Emotional intelligence, because the customer is rarely just asking for the literal thing they typed.

• Writing that sounds human and on-brand, because if your human sounds like a bot, then you should have just used a bot.

• Comfort operating without scripts, because the best high-touch interactions are contextual, not templated.

• A hospitality instinct, where the goal is protecting the relationship, not closing the ticket.

Tools can be taught quickly, and unfortunately, taste and EQ cannot.

What This Changes Inside the Team

If automation is doing the repetitive work, CX teams will not look the same.

They will likely be smaller. More senior. More brand-aligned. The work will sit closer to retention than to ops. The remaining humans will be handling fewer interactions, but each interaction will carry more weight.

The cost savings from automation should not disappear into the void.

If you are going to automate the volume, you should reinvest into the humans who remain. Pay them better. Train them differently. Give them the context that makes judgment possible, including customer segmentation, LTV, and what you are willing to do for a top customer versus a first-time buyer.

If your best people still feel like they are being graded on handle time while they are trying to save relationships, you are going to get the worst of both worlds.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

P.S. If you want to figure out how to get your brand to rank high in LLMs and show up in ChatGPT, Gemini, and more… check this out.

Hey team,

Happy Thursday. If you’re on the East Coast and want to strangle that groundhog, get in line.

We just finished digging out from the last snowstorm, and I was not exactly thrilled to pick up the damn shovel again.

It reminded me of a different kind of digging I’ve done more times than I’d like to admit. 🙃

The kind where you open your inbox and realize a campaign landed somewhere it absolutely should not have.

At some point in your retention career, you are going to send the wrong email to the wrong person. Not necessarily because you’re careless, but because your data has blind spots, your lists are never perfectly clean, and your customers are living lives your segments will never fully capture.

Like the win-back email that hits someone who just rage-canceled.
The re-engagement campaign that reaches a customer disputing a charge.
The promo that lands two days after someone paid full price.

You cannot eliminate those moments entirely.

What you can control is what happens next.

Today I want to talk about two things: how to reduce the odds before you hit send, and how to recover the relationship when you inevitably get it wrong.

Let’s jump in.

This week’s newsie is brought to you by KODIF AI.

As your brand scales, customer conversations explode across support, pre-purchase, and every corner of your site. The question becomes: do you build a larger team, or a smarter one?

The AI CX market is crowded, but here are a few reasons KODIF stands apart from what I’ve seen:

1️⃣ Built for complexity — not just basic FAQ.

From what I’ve seen other AI agents break when SOPs get multi-system and conditional. KODIF asks you to give them your most complex SOP, and automate that.

For example, I saw how KODIF chat agent handled Aura Frames complex troubleshooting product issues end-to-end — walking a customer through diagnostic steps and, if needed, triggering a replacement part order inside the same flow. I was impressed.

2️⃣ An AI CX team > one AI Agent

They build AI CX Team, purpose - specialized agents that mirror CX roles: an AI Support Agent for resolution, AI Concierge for pre-purchase and conversion, AI Analyst for insights, and AI Manager for continuous improvement. It’s a team model, not a catch-all model.

3️⃣ Deep ecommerce integrations (not surface-level)

They’ve built 178+ ecommerce integrations and shipped 88 new ones in 2025 alone.

You all know that matters because automation only works when the system can actually take action, not just answer questions.

Some recent cool reviews on the success stories:

Dollar Shave Club:

“The customizability of KODIF is what excites me most. I’m in there every day. Containment is great and all, but quality matters just as much or more. Our automation rate is above 70% but more importantly we are leveraging KODIF for retention.”

— Jack Dukesherer, Sr. Manager, Member Engagement Ops

Fellow Coffee:

“The Chat AI Agent can walk someone through calibrating their grinder — it’s super resourceful with the right inputs. Furthermore, address changes and cancellations? Completely automated. I haven’t thought about a cancellation in ages.”
— Cindy Rodriguez, Director of CX

Just Food For Dogs:

“We started with deflection as the goal, but it turned into more — now the AI helps customers discover products, understand ingredients, and convert. That’s real ROI.”

• Cristina Fucchi, Chief Customer Officer

Something that stood to me is their founders’ strong engineering background. Their CTO, Norm previously built Uber’s customer support automation platform, automating 150 million customer requests per month.

KODIF is trusted by top ecommerce brands such as True Classic, Ruggable, Dollar Shave Club, Liquid I.V., One Skin, Aura Frames, Fellow, Neuro, Who Gives a Crap, Million Dollar Baby, Ivy City, Helix Sleep and many others.

Many teams review their CX stack in Q1, and if your support workflows are complex, it might be worth a conversation with Chyngyz and the KODIF team to see if it’s a fit. They promise to go live in 3-5 days.

Also, they are offering my readers 3-months for free… Just hit up their team and drop my name, Eli Weiss.

This offer is valid only through March 31, 2026.

Book a demo and meet the founders

Part one: How to recover when it happens

I recently spoke to a pet brand that ran a pretty standard re-engagement campaign a few months ago. Lapsed customers plus some new prospects from a third-party list, which is the kind of thing retention teams run every quarter without lighting candles and reflecting on the meaning of life.

The problem was simple and brutal. The list had no idea whose dogs had passed away.

The emails went out, and within hours, their inbox popped off in the worst way. Some people were confused, some irritated, and a few were grieving. It was the kind of Monday where you open Slack, see the subject lines stacking up, and immediately understand how the rest of your day is going to go.

The part that made this story worth repeating came after that. The brand happened to be using KODIF when the fallout began, and the responses were handled in a way that felt steady.

When someone wrote in saying their pet had passed, the reply acknowledged it directly and apologized without turning it into corporate theater. When someone was simply annoyed, they apologized and handled the opt-out cleanly without dragging the exchange out longer than necessary. It did not try to impress anyone. It just responded appropriately.

Their CX lead told me, “For the ones where the pet had passed, it was showing empathy. For the ones who just didn’t like it, it was saying sorry and passing along the feedback.”

Most teams would have fumbled that recovery, not because they are careless, but because their CX layer is usually optimized for speed. Close the ticket, hit the SLA, keep the queue moving.

That works when someone wants to know where their order is. It does not work when someone replies to your campaign with something personal that your segmentation logic could never have predicted.

Recovery is not complicated, but it does require discipline. You acknowledge what happened in plain language and you own it without turning the apology into a memo. You make the next step obvious. If someone wants to opt out, it should take seconds. If they need help, they should not have to explain their situation twice.

The reason this becomes difficult is scale. Doing this well requires context, and most teams do not have the capacity to craft nuanced replies consistently when a campaign misfires and hundreds of messages arrive at once.

Savings and deflection are useful metrics. They just are not the full story. The moments that matter most are the ones where the customer is reacting to something your brand did.

If a campaign goes sideways late at night and someone replies with something your team did not anticipate, that exchange often determines whether the relationship stabilizes or erodes.

Part two: How to limit the damage before it happens

You cannot fully prevent this, and anyone promising zero mistakes is selling fantasy. There is, however, a clear difference between brands that misfire once in a while and brands that seem to repeat the same avoidable mistake every quarter. That difference usually comes down to process.

Most of the time this is not a tooling issue. It is a coordination issue. Marketing pulls a list and hits send. CX finds out when the replies start landing. Now support is managing the emotional and reputational fallout for a campaign they did not know was going out.

That is less of a tech stack problem and more of a meeting problem.

The brands that handle this better usually build in one small pause before the send. Someone pressure-tests the list. Why are these people here? What do we actually know about them beyond purchase history? Where are the blind spots? If this lands badly, what is the most predictable reason it would?

That five-minute exercise catches more risk than another subject line brainstorm ever will.

Pre-send reviews tend to focus on creative and copy. Very few focus on the edges of the audience. The edges are where this goes wrong. Adding one deliberate “what are we missing?” check into your workflow costs almost nothing and prevents more damage than most teams expect.

Suppression logic is the other piece, and most brands build it reactively. You suppress recent purchasers because someone complains loudly enough. You suppress customers with open disputes because one campaign burned you. Over time the rules pile up, nobody maps them end to end, and the gaps sit there quietly until you trip over them again.

Once a year, it is worth auditing that logic intentionally. Are there customers in a transactional or financial situation where outreach right now is more likely to frustrate than help? You are not going to eliminate mistakes entirely, but you can meaningfully reduce the ones you cause yourself.

Third-party lists require a more honest internal conversation than most teams are willing to have. Your owned data is imperfect. Rented data is usually worse. When you email a list you did not build yourself, you are choosing higher variance on outcomes. That can be a rational tradeoff, but it should be explicit.

On paper, the campaign looked routine and the spreadsheet looked clean enough. What nobody paused to consider was what the file did not capture. That omission is usually where these situations begin, which is why it is worth naming the risk before you hit send

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

P.S. If you want to figure out how to get your brand to rank high in LLMs and show up in ChatGPT, Gemini, and more… check this out.

Hi team,

A year ago, LLM sounded like an airport code. Now your mom is asking ChatGPT how to decorate her living room, and your dad is Claudemaxxing to plan vacations.

Commerce moved first.

According to Adobe, 55% of consumers now use AI for product research. No longer fringe behavior, it is a mainstream discovery.

At the same time, according to Yotpo data, traffic to brand websites is falling across segments. Brands doing over $100 million are down 10 to 25 percent. Mid-market brands are down 35-40%. Smaller brands are seeing declines as steep as 70%.

And yet purchases attributed to AI-powered search have increased 11x this year. Orders coming through that channel carry 30% higher AOV compared to other sources.

TL;DR: Fewer people are visiting your site. The ones who do are higher intent and spend more. Given all the legwork they did on LLMs before hitting your site, their decision is increasingly shaped before the site visit happens.

Which means:

If you are not trusted by the machine, you never make the shortlist.

And if you never make the shortlist, you never get the highest intent customers.

Today I want to break down how LLMs actually decide what to recommend, why most brands are underbuilding the trust layer that feeds those decisions, and what you can do this quarter to fix it.

Let’s dive in, shall we??

The 71% Statistic You Can’t Ignore

Whitelisting, Spark Ads, Partnership Ads—whatever you call them, one thing is certain: they are crushing it for 2026 and minisocial is our go-to partner.

Creator-led ads, whatever specific flavor you choose, are crushing. Meta reported that 71% of consumers say they make a purchase within a couple of days after seeing creator content, partnering with minisocial is the best way to partner with high-quality creators and streamline your UGC production.

Brands including Topicals, Ayoh!, MadeGood, and Rocksbox already trust minisocial for creator-made content that performs!

Here’s what brands working with minisocial say:

• “Ads made with minisocial content are performing better than anything we’ve made in-house.” - Michael, Growth Marketing Manager

• “minisocial’s cost per creator is cheaper than our in-house program.” - Andrea, Digital Campaigns Manager

• “The price for what you’re getting is just insane... minisocial handles every step of the influencer management process.” - Kaylee, Director of Marketing

Ready to See Results?

Work with the team behind the best Whitelisting assets:

Learn More Here

Before we dig into how this actually works, two things worth taking a peek at:

• Yotpo published research on what shoppers are actually asking LLMs. 53 million shopping prompts every single day. If you want to see what people are searching and who shows up when they do, it’s worth reviewing.

• Second, if you want to know where you actually rank on LLMs right now and quick fixes you can deploy, there’s a free tool that shows you.

Your customers are choosing before they ever visit your site

For years we optimized what happens after the click. Stronger PDPs, faster load times, tighter checkout, better copy. That still matters.

But more of the decision now happens before the click.

When someone asks ChatGPT, “What is the best prenatal supplement for conception?” a shortlist gets assembled before your site is even in play. If your brand does not appear in that answer, your beautiful PDP might as well not exist for that buyer.

The frustrating part is that these customers convert better and spend more when they do show up, because they have already done the research.

If you are invisible at the LLM layer, you are missing your highest-intent customers.

How LLMs actually evaluate your brand

LLMs are not reading your page like a human or reacting to your hero banner the way a person would. They evaluate signal drawn from across the web.

That signal usually comes from three areas: user sentiment, independent validation, and structured product data.

User sentiment reflects what real customers say in reviews, Reddit threads, Q&A sections, and community forums, including the specifics, the complaints, and the details that show how a product performs in real life. Independent validation includes media coverage, expert commentary, and third-party reviews that exist outside your owned channels.

Structured product data means your information is clear and consistent enough that a machine can match you to a query without guessing, including ingredient breakdowns, certifications, pricing tiers, compatibility notes, and clearly defined use cases presented in a structured way.

When those layers are strong and aligned, you are easier to recommend. When they are thin or inconsistent across sources, you gradually fall out of answers and most brands do not notice until the damage is done.

The funnel did not disappear. It moved earlier.

At the top, the model builds a shortlist from broad awareness questions like “What are the best running shoes for flat feet?” It relies on review depth, clear use-case language, and category clarity. If your reviews never mention overpronation, you are giving the model little reason to connect you to that query.

In the consideration phase, buyers narrow down with prompts like “Brand X vs Brand Y.” The model gathers ingredients, pricing tiers, policy details, recurring themes from reviews, and how third parties describe each option, then forms a summary based on the weight of that evidence. If a competitor has clearer attributes and stronger off-site coverage, the comparison will tend to lean in their direction.

At the decision phase, buyers want reassurance. Prompts like “Is Brand X worth it?” or “Do people like their customer service?” draw heavily from recent reviews, shipping feedback, and real-world outcomes. If that signal is generic or outdated, confidence erodes and recommendations shift.

Most brands assume comparison and final decision happen on their PDP. Increasingly, much of that work happens before the first visit.

A decent chunk of trust signal lives somewhere you don’t (easily) control

A significant portion of what shapes whether an LLM recommends you comes from outside your domain, including forums, Reddit, independent review platforms, and media coverage. The trust calculation is happening whether you are actively managing those surfaces or not.

Authentic, specific, and recent reviews used to feel like retention hygiene. In an AI-mediated discovery world, they function more like infra. When review banks become generic or overly polished, the signal weakens because the model has less real-world evidence to rely on.

The audit is simple. Use the same tools your customers are using. Ask ChatGPT or Claude the questions buyers ask at each stage. See who appears and which sources are cited. Then look inward. Are your reviews recent and detailed? Do customers describe real use cases and outcomes? Is your product data structured clearly and consistently? Are independent sources discussing your brand?

If those answers are uncomfortable, adjusting a flow will not fix it.

Your trust layer needs attention, and right now that layer determines whether you are even in the conversation.

If you want deep dives on topics like these + actionable ways to show up in LLMs, check out Tomer’s (CEO Yotpo) newsletter here.

That’s it for this week!

Any topics you’d like to see me cover in the future?

Just shoot me a DM or an email!

Cheers,

Eli 💛

P.S. If you want to figure out how to get your brand to rank high in LLMs and show up in ChatGPT, Gemini, and more… check this out.