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.

Get your free audit →

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.

Hey team,

This newsletter comes to you from a quiet corner of the Moxy in Williamsburg after a hectic but really special week at Yotpo’s revenue kickoff. So excited about what the future holds.

3 coffees too many from Devoción, snacks from Mesiba, it's really all I need to feel something

This week’s topic is a hot one that no one is talking about.

Most brands spend way too much time on people who don’t care about them in the first place.

They (rightfully) pour a ton of energy into pushing order number one to order number two. They build increasingly complex programs to re-engage customers who bought once, maybe twice, and disappeared.

At the same time, they do so at the cost of customers who have bought 4+ times.

This week, I want to talk about how to spot when your best customers are starting to drift and what you can do to keep them close.

Let’s dive in.

This week’s newsletter is brought to you by DigitalGenius.

The problem with most AI Agents for CX is that they don’t actually solve customers’ real and pressing problems. They are OK at answering surface-level questions (when they’re not hallucinating or pulling from the wrong data source), but most can’t perform the actions that your customers actually need them to.

Take order queries, for example. It’s easy for AI Agents to share a tracking link – but that’s not enough for a customer whose order is delayed. To be relevant, the AI Agent needs to be able to retrieve order status, compare it against SLAs, and even authorize a replacement or refund – just as your human agents would.

This is the challenge that DigitalGenius is solving for brands like Caraway, Everlane, and On Running. Through deep integrations to ecommerce tech stacks, AI Agents can take real actions to fully resolve queries, not just deflect them.

Plus, with a unique hybrid approach that combines LLMs and workflows, answers are consistent, hallucinations are minimized, and conversations result in genuine resolutions. DigitalGenius has ready-made workflows built on 10+ years’ experience automating for ecommerce brands, so you can get started quickly and drive meaningful change for your business.

If you want your AI to actually resolve queries, and not just reply, then speak to DigitalGenius today.

Use Code Eli2026 and receive a $100 gift card when you book a demo.

BOOK A DEMO

What you’re missing about your best customers

There’s a difference between how casual customers leave and how your best customers leave, and most brands don’t notice it until it’s already too late.

Customers who buy once and disappear tend to make noise. They unsubscribe, stop opening emails, and generally announce they’re leaving. You can point to price, timing, or competition and feel like you understand what happened.

Your best customers behave differently. They stay subscribed, keep opening emails, and sometimes continue engaging with content. What changes is how often they buy, and that shift happens slowly enough that by the time it shows up in reports, they’ve already mentally moved on.

You see this most clearly when something small goes wrong, and the customer doesn’t bother telling you.

I saw this on X this week and wanted to share it as an example.

This is exactly the type of customer most brands never hear from when something breaks.

Ross didn’t wait for Carson to say anything. He emailed directly, explained what happened, took responsibility, refunded the order, and shipped replacements without Carson needing to ask. That single email turned what could have been a quiet fade-out into Carson posting publicly about why he plans to keep buying from Cadence.

The important part here is that Carson wasn’t showing the usual signals people look for when someone is about to leave. He wasn’t angry, posting on social, or opening support tickets. He was simply going to buy less over time and eventually stop without ever explaining why.

Brands miss this constantly because their systems are designed to respond to noise rather than silence. Companies build elaborate processes for angry first-time buyers who blast them publicly, while long-time customers quietly assume someone will notice and fix the issue.

When that doesn’t happen, they change their behavior and move on. Interpreting that silence as satisfaction is how those customers end up with a templated response or no response at all.

Carson was on track to buy less and eventually stop, and Cadence wouldn’t have known why until much later. Ross catching it early and dealing with it directly changed the outcome, not because the fix was complex, but because the brand noticed before being forced to.

Why power users drift away

Power users judge you by a different standard than new customers.

First-time buyers compare you to whatever brand they tried most recently.

Tenth-time buyers compare you to the version of your brand they fell in love with in the first place.

That’s a much harder bar, because standing still looks like moving backward.

Long-time customers notice things newer customers don’t. Packaging that feels cheaper than before. Support responses that take longer or feel more automated. Product launches that feel designed to hit quarterly targets rather than solve real problems. Emails that sound like they could have been sent to anyone.

They also notice when loyalty is treated like a line item. When someone who has placed a dozen orders receives the same emails and promotions as someone who bought once during a flash sale months ago, the message is clear.

Someone who’s been buying from you for three years doesn’t want to be treated the same as someone who found you last month. You don’t need an elaborate rewards program to acknowledge that. It shows up in faster responses, more relevant access, and communication that reflects an actual relationship with the brand.

When that recognition isn’t there, customers don’t usually get upset. They become indifferent, which is soooo much harder to reverse.

What actually works

You don’t need to rebuild your retention strategy from scratch to keep your best customers engaged. You need to consistently show that you’re still paying attention.

That shows up in small, meaningful improvements shipped regularly, even when they’re not flashy. It shows up in communication that sounds like a real person wrote it with context. It shows up in access or content that reflects an understanding of who these customers are and why they’ve stayed.

It also means having real conversations with them from time to time. Not pathetic surveys or mass NPS blasts, but actual back-and-forth discussions about what would make the brand feel better six months from now and then doing something with that feedback.

Most brands end up spending most of their time chasing people who never really cared much to begin with. Investing in the people who already care often feels less urgent because they aren’t complaining.

That priority order is usually backwards.

Your best customers are paying attention. They’re watching to see whether you’re still earning 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 💛

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,

Over the last few weeks, an open-source project called OpenClaw blew up, hitting millions of downloads. It is an AI agent you run 24x7 on your computer that writes code, books flights, handles email, and plugs into WhatsApp, Slack, and Telegram.

Folks are running their life through it. Building websites from their phones, fixing bugs overnight, and letting it operate continuously without being prompted for each task.

The future is coming really quickly, but what does it mean for CX?

It’s likely that in a near-future state, most customer interactions will be handled by automation. Your customer’s AI assistant talks to your brand’s AI assistant. Orders are placed, shipments are tracked, and issues are resolved without a human involved.

When that becomes the default, the moments when a human steps in become the entire experience.

Right now, brands think premium CX is about polished unboxing and a handwritten note, but customers judge you on whether someone stepped in at the right moment and fixed their issue without friction.

And ironically, automation is not the risk. Blind automation is.

I learned this at Jones Road. A customer ordered the wrong foundation shade and emailed us immediately to request a change before it shipped. The email came in on Friday at 6 pm.

By Monday, when support saw it and offered the right shade, the customer had waited all weekend for a 30-second fix.

This could have been solved in a second with the right automation, but not everything should be automated.

And when you do automate, you need a partner that truly gets how to build your AI teammates, not slop.

This week, let’s talk about building proper automation (this week’s partner truly gets it) and when (and where) human touch will actually deeply matter.

Excited for you to read this one, I wrote it from the heart with a bit of sass. 😏

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 out 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

When automation exposes what is missing

I checked into a Hyatt House in London last year. There was not a single human at the front desk. You scanned your ID at a machine, opted in or out of late checkout, and the machine printed a room key.

My introvert self appreciated the efficiency. But once I settled in, it became obvious that I could have stayed at a hostel for half the price if there was going to be zero hospitality. I paid Hyatt rates for a vending-machine experience.

The automation worked fine. What it did was make the missing piece impossible to ignore.

When everything gets handled by a machine, the absence of a human becomes the experience.

This is where a lot of brands are headed without fully thinking it through. They automate everything they can, which is often the right move, but they never stop to ask what happens in the rare moments when a human is still involved.

We are already seeing the backlash. OpenAI is reportedly working on a social network for humans only, where users would need to verify they are actually human, potentially through biometric checks.

We are moving so fast toward automation that we are already building walls to protect spaces for real human interaction.

That tells you everything you need to know about where this is going. Bots will be the default. Human interaction will become the premium feature.

At Jones Road, we automated a meaningful portion of customer support. Bots handled order tracking, basic product questions, and simple troubleshooting. That freed the team up to focus on harder problems.

The Power of Moments:

But we also built tripwires. Signals that told us when the bot was no longer helping and a human needed to step in. That’s the tactical version. The strategic version looks different.

Hotels like Ritz-Carlton have really nailed the idea of knowing where to step in.

I was talking to a friend, Izzy, CEO of Portless, about his recent trip to China and his stay at the Ritz there.

He had a mango every morning for breakfast, and one day he came down and asked for his daily mango.

They said that, unfortunately, all the local markets were out. He had a sad face. The next morning he came down and there was a beautiful mango sitting on the plate. They had traveled 45 minutes to get it.

What I love about this story is that if they just gave a random person this mango, it would mean nothing. They deeply understood the mango moment: what that moment meant for him and when they needed to jump in and do something special.

Every brand has a mango moment. The question is whether you’ve identified yours.

Five signals that a human needs to step in

1. When the conversation goes in circles

If someone asks the same question three different ways, the bot is not understanding. Premium brands catch that loop and route the conversation to a human before frustration sets in.

Most brands let the bot keep trying. The customer repeats themselves four or five times, gives up, and leaves believing your support is useless.

At OLIPOP, we had a simple rule. If the bot responded three times and the customer was still asking, a human took over. Not because the bot was bad, but because the customer clearly needed something it could not provide.

2. When emotion shows up in the message

All caps. Multiple question marks. Words like frustrated, disappointed, or angry. Bots can detect sentiment, and premium brands use that signal to escalate early.

Most brands either ignore the signal or train the bot to respond with canned empathy. That makes things worse. Nobody wants a bot apologizing for frustration when the bot is the source of it.

If someone is upset, get a human involved. A bot is not going to calm them down.

3. When someone explicitly asks for a human

This should be obvious, but most brands still make you work for it. They redirect you back to the bot. They ask if the bot can help first. They make you prove you deserve a person.

Premium brands just connect you. If someone asks for a human, give them one. Making customers justify that request is the opposite of premium.

4. When real money is involved

A $15 order issue can be handled by a bot. A $500 order that went missing should not be.

Most brands set automated thresholds based on cost savings, not customer impact. Bots handle refunds and replacements up to an arbitrary number that looks good on a spreadsheet and terrible in real life.

Premium brands understand that when someone is out real money, a human should be making judgment calls. Not because the bot would necessarily get it wrong, but because the customer deserves to feel like someone is actually evaluating their situation.

5. When someone has earned it

Your best customers should not have to prove they deserve human attention. Premium brands automatically route long-term or high-value customers to people.

If someone has been buying from you for three years, they should not be explaining a problem to a bot. They earned a shortcut.

Most brands treat everyone the same. That is efficient. It is also how you lose your best customers to a brand that recognizes them.

We are sprinting toward a future where bots handle most of the work. That is fine, and probably good.

The brands that win will be the ones that understand when to turn the bot off.

Premium is not about automating everything. It is about knowing when a human matters and never making people beg for one.

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,

What a week.

Over the weekend, I was driving with Noah to get some groceries before the storm, and five cops were chasing someone at 80mph. Guy slammed into us trying to escape. Side airbags deployed, my iPhone auto-called 911. The car called 911. The whole back wheel was ripped off, and the car was totaled.

Noah and I walked away seemingly without a scratch.

Modern cars are incredible.

And aside from the walloping we got from the snow on Sunday, we’re doing okay. Shaken but grateful.

Anyway. This week’s topic is fun, and one that a lot of you enjoyed last time I touched on it: returns.

Specifically, practical tips to make more money and improve CX from returns. Loop surveyed tens of millions of returns across 4,000+ Shopify merchants and put together a benchmark report.

I pulled together some of the most fascinating and practical bits from it.

Let’s dive in.

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

Most brands settle for the average. Top brands don’t.

Too often, ecommerce benchmark reports tell you what the average brand is doing, then leave you guessing how to beat it.

Loop’s 2026 Global Ecommerce Retention Benchmarks Report was built to change that.

It analyzes tens of millions of post-purchase actions across 4,000+ global brands, giving you the context to better understand where your brand stands, which tradeoffs matter, and which levers actually drive retained revenue and loyalty.

What stood out:

• Brands with flexible exchanges retain 42-58% more revenue than store credit-only setups.

• Exchanges are now the norm. 73.6% of merchants offer them, and nearly half add incentives to shop immediately.

• Return fees are increasingly selective. 65.2% of brands use targeted fees to protect margin while preserving loyalty.

• Fraud is being addressed earlier. 11.4% of return value is flagged as high risk, pushing teams toward smarter controls instead of blanket restrictions.

This report shows the path to better retention, durable growth, and real-world playbooks from brands like LSKD and Piper & Scoot.

Download it today

1. More options at returns = more money retained

Brands that offer three return outcomes (variant exchange, store credit, and refund) see 25% higher revenue retention than brands offering just one option. Same data shows 36% higher repeat purchase rate.

I’ve been saying this for years but seeing it across 23 million returns makes it harder to argue with.

Most brands default to refund-only because it feels simpler. Fewer decisions, fewer edge cases, less to manage. But that simplicity is pushing customers toward the exit at the exact moment they’re deciding whether to stay.

The customer is raising their hand saying something didn’t work. You can either solve the problem or hand them their money back. One of those keeps them around.

I returned something last month from a brand I’d bought from a few times. The portal gave me one option: refund. No exchange, no store credit, no “want to try a different size?” Just money back. So I took the money. And I probably won’t think about them again.

That’s what a refund-only policy actually costs you. Not just the transaction. The relationship.

2. Variant exchanges beat store credit

This surprised me a little. Store credit alone gets you about 19% revenue retention. Variant exchanges alone (letting someone swap sizes or colors) gets you 27%.

Store credit feels like a compromise. Variant exchange actually solves the problem.

Someone ordered the wrong size. They don’t want credit to use later. They want the shirt in medium instead of large. Let them swap it without thinking about it.

When you layer store credit and Shop Now (exchanging for anything in your catalog) on top of variant exchanges, retention climbs to 32%. But the foundation is the size swap. That’s your baseline win.

For apparel and footwear, this matters a lot. Fit is the whole game. Make it easy to fix. Footwear brands that offer exchanges see only 5% of returns flagged as high risk. Customer-friendly policies and low fraud can coexist when you’re solving the right problem.

Most brands I talk to have this backwards. They lead with store credit because it feels generous but noncommittal. But store credit is a consolation prize. Variant exchange is problem-solving. Customers feel the difference.

3. Free returns quietly became free exchanges

65% of merchants now charge return fees. Average is about $9.

But here’s what’s actually happening: they charge on refunds and waive fees on exchanges.

This is targeted friction. You’re not making returns hard. You’re making one outcome slightly more attractive than another. Customer still has a choice. You’re just nudging.

Some brands pair this with bonus credit on exchanges. Average across Loop’s network is around $11. Swimwear brands go higher, closer to $16. The math works because you’re paying $11 to keep a $50+ order from walking out the door.

I talked to a brand last year that was terrified to charge any fees at all. They thought customers would revolt. They tested fees on refunds only, kept exchanges free, added a small bonus for store credit. Revenue retention went up. Complaints didn’t move.

The fear of fees is usually bigger than the reality. Customers understand that shipping stuff back costs money. They don’t want to feel punished for ordering the wrong size, but they get that refunds have a cost. Charging on refunds while keeping exchanges free threads that needle.

4. Your VIPs should have a different return experience

VIP shoppers at brands using personalized return policies had a 55% repeat purchase rate. Brands with one-size-fits-all policies? 21%.

That gap is driven entirely by how you treat your best customers when something goes wrong.

Black Sheep, an Australian apparel brand in the report, runs three VIP tiers with different return benefits. General customers get $5 bonus credit on exchanges. VIPs get $15. First-time buyers get fees waived entirely to build trust early. Their revenue retention is 72%.

Piper & Scoot charges $10.95 on refunds but waives all fees for VIP customers. They also exclude refunds on heavily discounted orders to protect margin. Revenue retention is 63.5%.

If you’re running a loyalty program and your return experience doesn’t change based on tier, you’re leaving an obvious win on the table. Your VIPs have already proven they’ll come back. Make the return experience feel like it.

5. Fraud risk varies wildly by vertical

• Electronics: 19% of return values flagged as high risk; average fraudulent return: $595.

• Footwear: 5% flagged, $230 average.

• Cosmetics: 8% flagged, $174 average.

• Jewelry: 16% flagged, $462 average.

Your vertical determines your risk profile more than almost anything else. Blanket fraud rules applied across all product types are either too tight for low-risk stuff or too loose for high-risk categories.

One interesting pattern in the data: brands with the most generous return policies often have the lowest fraud rates. They’ve built systems to tell good customers from bad actors, so they can stay flexible with everyone else.

Restricting everyone because of a few bad actors is the lazy approach. Segmenting based on actual risk lets you be generous where it’s safe to be generous.

If you sell electronics and you’re not thinking hard about return fraud, you’re probably bleeding money you don’t even see. If you sell footwear and you’re running tight restrictions across the board, you might be creating friction that doesn’t need to exist.

What to do Monday

• If you’re only offering refunds, add variant exchanges. Biggest lift for the least effort.

• If you already offer exchanges, consider charging fees on refunds only while keeping exchanges free. Add a small bonus credit if margins allow.

• If you have a loyalty program, make returns better for VIPs. Higher bonus credit, waived fees, faster processing.

• If you haven’t reviewed fraud rates by product category recently, do it. The variance is probably bigger than you think.

Returns are a retention moment. Design them like one.

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.

Happy almost February?

Hope you are well so far. Before I jump in, one quick announcement.

I am hosting an ecom mission trip to Israel in June, and I’d love to have you. We will spend a few days meeting legendary founders, executives, and investors in the space, as well as attending exclusive briefings and eat our body’s weight in hummus.

Read more and register to join here.

Okkk, let’s jump in.

A while back, I sat with a team debating whether to kill a product that was selling okay.

The product lead thought it tasted worse than anything they’d shipped and suspected it was quietly dragging down LTV. CX said customers loved it. The founder was undecided. Marketing said the reviews looked okay, but admitted they hadn’t really looked at them in a while.

Someone pulled up a support ticket from months earlier. Someone else referenced a Reddit thread they couldn’t find. Everyone had something to point to. No one had enough confidence to actually move.

The meeting ended the way these meetings usually do. No decision or next steps, but of course, a loose agreement to keep an eye on it.

This plays out constantly. Feedback lives everywhere. Tickets, reviews, social, DMs, forums. But when a team actually needs to understand how customers feel about one specific thing, in a way that’s clear enough to act on, they can’t.

So teams stall. Or they wait for more data. Or the decision quietly follows whoever sounds most certain in the room, not whoever has the clearest view.

This week, I want to talk about why accessing customer feedback still breaks down inside most teams, how that gap quietly steers decisions, and what changes when feedback stops living in pieces and starts being treated like something you can actually rely on.

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

Syncly aggregates customer feedback from every corner (support tickets, product reviews, and social comments) to give you the single source of truth you have been missing.

Here is how it changes the workflow:

• Discover Insights with AI: Simply ask questions like you would with ChatGPT, and get answers backed by direct customer quotes.

• See Everything in One Place: Unify your internal support data with external social feedback (even video mentions!) for a truly comprehensive view of the customer journey.

• Organize Effortlessly: Syncly uses AI to auto-tag and classify feedback into granular topics, so you know exactly what drives customer sentiment without the manual lift.

• Act on What Matters: Get real-time alerts and automated dashboards to address critical issues before they churn customers.

You are in good company. From powerhouses like Fenty Beauty and Kosas to fast-growing disruptors like Neuro, Everyday Dose, and Hum Nutrition, brands are using Syncly to turn raw feedback into revenue.

Ready to scale your insights? Book a demo today and see exactly how Syncly can take your CX analytics to the next level.

BOOK A DEMO

The confidence problem

Most companies don’t struggle because they lack customer feedback. They struggle because they can’t get to what they already have quickly enough to feel confident making decisions.

When feedback is spread across tools and pulling it together takes real effort, decisions tend to drift toward whoever sounds most certain in the room. That certainty usually has little to do with having better information. It’s more about seniority, comfort, or simply being the person willing to speak first.

I’ve watched this show up in a few predictable ways that quietly cost more than people realize.

Product decisions get made by whoever tells the best story

A team is debating whether to build a feature. Someone says customers have been asking for it. Someone else says they’ve never heard it come up. Product says it’s doable but way too expensive. Marketing thinks it could help positioning, but no one is willing to say that with much confidence.

What nobody can answer is how often customers actually mention it. Or what language they use when they do. Or whether it shows up consistently across reviews, tickets, or social. The information exists somewhere. It’s just scattered enough that no one feels comfortable betting a decision on it.

So the conversation drifts. Eventually, the decision lands wherever the room settles that day. Months later, when the feature either works or doesn’t, it’s hard to explain why the call was made in the first place.

It wasn’t documented because it wasn’t grounded in anything concrete.

CX decisions optimize for what’s visible, not what’s costly

Support volume starts climbing, and the team feels underwater. Leadership approves more headcount to keep up.

What often goes unexamined is whether the issues driving ticket volume are the same ones driving churn. Some problems generate many tickets but barely affect retention. Other problems rarely surface in support at all. Customers hit them once, get frustrated, and leave without ever writing in.

When feedback isn’t organized in a way that connects issues to outcomes, teams naturally focus on what they can see most clearly. You end up staffing for noise rather than solving the problems that are actually costing you customers.

Marketing decisions lean on gut instead of customer language

A team is preparing to launch something new and debating how to talk about it. Is the value convenience, quality, or sustainability? Every option sounds plausible and has someone advocating for it.

The discussion stretches on, and eventually a decision gets made based on what feels familiar or what worked on a previous launch. At the same time, customers have already explained why they bought, how they describe the product, and what mattered to them. That language lives in reviews, comments, and forums. It just isn’t accessible while the decision is being made.

So the team makes a guess and hopes it lands. Sometimes it does. Sometimes it doesn’t. You only really find out once the campaign is already running.

What changes when feedback becomes usable

There’s been a lot of conversation recently about promptable analytics, where teams can ask questions directly and get answers without waiting on reports.

The real change here shows up in how decisions get validated.

When feedback is hard to access, strong opinions tend to dominate discussions. When feedback is easier to explore, assumptions get tested earlier and more often. Leadership can pressure-test ideas in the moment. CX can spot patterns before they escalate. Product and marketing can ground decisions in actual customer language rather than internal interpretation.

This only works if feedback lives in one place and is organized well enough to be useful. Adding a chat interface on top of a fragmented system doesn’t create clarity. It just makes the mess more visible, faster.

How teams stop guessing

One place to start is by looking backward. Pick a few recent decisions that actually mattered and ask whether the information was accessible at the time, or whether the decision relied mostly on confidence and instinct.

From there, consolidation matters more than you might think. Centralizing a single feedback source and making it easy for multiple teams to explore often reveals where the real blind spots are. The questions people ask once access improves are usually more telling than any dashboard.

It also helps to pay attention to how feedback changes over time rather than how much of it exists. Volume shows what’s happening. The pace at which sentiment shifts is often a better signal of what’s coming next.

This shift has less to do with adopting new tools and more to do with whether internal systems reflect what customers are actually saying. When feedback is accessible and trusted, decisions rely less on who sounded most confident and more on shared understanding. That difference compounds faster than most teams expect.

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,

Between the four emails a day from Wayfair, the third SMS this week about a sale I had already seen, and a push notification that appeared six hours after I had already bought the item, it’s hard not to notice how poorly coordinated retention has become.

None of this feels especially thoughtful. It mostly feels like systems talking past each other.

Somewhere along the way, retention turned into a damn performance. More messages, more schedules, and more systems layered on top of each other in the name of staying “top of mind,” without much interrogation of whether all this activity actually beats a quieter approach where systems respond to what customers are doing, rather than what a calendar says they should be doing.

The brands that seem most in control right now are rarely the ones doing the most. They are usually running simpler programs that hold up without constant babysitting, even when someone goes on vacation or priorities shift.

This week: how retention got this complicated, what campaign-heavy systems really cost, and why the most valuable thing you can build right now is boring enough that you forget it’s there.

This week’s newsie is brought to you by Tapcart AI, which matters less as an “AI launch” and more for what it signals about where retention is heading.

For the last decade, retention tools have mostly pushed teams toward more. More campaigns to plan, more segments to maintain, more calendars to manage, and more decisions to revisit every time performance shifts. That approach can work, but it depends heavily on constant human involvement to keep everything running smoothly.

Tapcart is leaning into a different direction with how they think about mobile retention. The emphasis is on reducing how much manual effort it takes to keep an app performing as a retention channel. Execution that responds to customer behavior automatically. App experiences that don’t need constant weekly manual upkeep. The kind of infrastructure that just runs without you having to think about it.

That’s what “self-driving” actually means in practice. Not replacing the team. Removing the repetitive work that eats time without moving the needle.

What Tapcart AI looks like:

• Campaigns that trigger based on behavior, not calendar dates you have to set every week

• Personalization that adjusts to customer patterns without manual segmentation

• App performance that improves over time without constant tweaks

If you want to see how Tapcart is thinking about this shift, you can explore Tapcart AI here:

Learn More

How We Got Here

Ten years ago, retention was mostly email. Welcome series, win-back campaigns, and promotional sends tied to sales. The stack was small because the options were limited, and you could understand the whole system without needing a diagram.

Then the channels multiplied. SMS became standard. Push became accessible. In-app messaging showed up. Loyalty grew into its own operational lane. Personalization tools promised to make everything smarter and came with long onboarding cycles and ongoing maintenance that nobody mentioned in the demo.

Each addition was reasonable on its own, and for a while, the promise held.

I started noticing the cracks about two years ago while helping a home goods brand audit their retention setup. We found fourteen separate automations spread across four platforms. Half of them had been built by people no longer at the company. One flow had been running for eighteen months, and nobody could say with confidence whether it still made sense. Nobody wanted to turn it off in case it was load-bearing.

The channels still work. Managing them has become its own job. Retention teams went from running campaigns to managing infrastructure, and a lot of strategic time got absorbed by coordination and QA.

Here’s how it compounds.

Someone adds cart abandonment because it performs. Someone layers in browse abandonment because customers are dropping earlier than expected. Someone builds a post-purchase sequence to drive repeat orders. Each decision makes sense in isolation.

These systems never stay isolated. They run in parallel, with different triggers, timing rules, owners, and assumptions. A customer abandons a cart at 2pm. Your email flow fires immediately. Your SMS goes out at 4pm because that’s when batch sends were scheduled. Your push arrives at 6pm because the timing was set months ago and hasn’t been revisited.

None of these systems know what the others are doing. The customer just knows they got hit three times in four hours about the same cart and is now slightly less inclined to come back.

The Real Cost of Always-On Management

The obvious cost of a complex retention stack is time. Someone has to build flows, monitor dashboards, adjust timing, update creative, manage deliverability, and troubleshoot edge cases.

The less obvious cost is fragility.

A fashion brand I know had their entire retention program effectively held together by one person who had been there for three years. She knew which flows couldn’t be edited without breaking downstream triggers, why the win-back series had a random 36-hour delay, and which fields could not be touched without breaking a loyalty sync.

When she left, that knowledge left with her. The team inherited a Jenga tower of automations that nobody wanted to touch.

Change becomes expensive, so teams default to keeping things running. They tweak around the edges, but the underlying system stays the same because the risk of touching it feels higher than the upside of improving it.

A beverage brand described their retention program as “25% strategy, 75% not breaking what already works.” That ratio should be inverted.

The Quiet Shift Already Happening

I’ve been watching this shift over the last year in conversations with retention leaders who have started simplifying their stacks.

A DTC apparel brand cut push from twelve scheduled sends a month down to three. Everything else became event-driven, responding to browsing behavior and purchase patterns in real time. Push open rates went up 31%, and nobody on the team thinks about scheduling anymore.

A supplements company deleted their browse abandonment flow entirely. It was generating a tiny fraction of retention revenue and driving most of their unsubscribe volume. Performance held and complaints dropped.

A home goods brand told me their goal for 2026 is “systems we can ignore,” meaning infrastructure reliable enough that the team can focus on strategy instead of maintenance.

The pattern is consistent. Teams are moving away from campaign volume as a success metric and toward resilience as the standard. Fewer scheduled sends and more emphasis on systems that can run without constant attention.

Performance stays flat or improves.

Teams aren’t getting bigger, budgets aren’t expanding, and expectations keep rising.

What Good Looks Like

I spoke to someone last week who described their retention program as “me-proof,” meaning it was clearly documented and built simply enough that someone new could take it over without having to reverse-engineer years of decisions.

That should be the standard.

Every tool you add increases surface area for failure. Another login, 10 more integrations, another dependency that can break at the wrong time. The best retention setups in 2026 won’t be the most sophisticated. They’ll be the ones you can explain in five minutes to someone who just joined.

That also means prioritizing owned channels that compound over time, rather than paid re-engagement that resets every month.

Where Humans Still Matter

Even in a boring retention model, strategy still matters.

Humans decide what the experience should feel like, what trade-offs to make between frequency and relevance, and which behaviors actually deserve a response. Systems can execute rules reliably, but the rules still need judgment behind them.

Scheduling sends is not a great use of human time. Deciding whether your brand should feel calm or urgent is.

The goal is to spend less time maintaining execution and more time shaping the experience.

Why Boring Wins

The best retention programs are the ones you barely think about because they run consistently and free up time for work that actually moves the business forward.

I keep coming back to a retention director who told me their biggest win last year was not launching anything new. What they did instead was make the system more durable. Their retention became boring in the best possible way.

If your stack still feels like it requires full-time babysitting, it’s worth asking what would need to change for it to become quieter. Not because quiet is virtuous in itself, but because systems that operate without constant attention give teams room to do better work.

Boring retention is better retention. And in 2026, that’s the only model that scales.

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,

Happy 2026. We’re back.

If you’ve been online over the last few weeks, you’ve seen the same things I have. Q4 was either described as incredible, disastrous, or the moment the recession everyone has been predicting since 2022 finally decided to arrive.

From where I sit, it looked maybe a tiny bit less dramatic than that?

Most brands I spoke with had a softer first half of 2025. Spending faced more scrutiny, growth felt harder to manufacture, and efficiency started to matter again. As the year went on, a surprising number of those brands finished stronger than expected. In nearly every case, it came down to the basics holding up when things felt tighter.

Which brings us back to retention.

I see this cycle every year. Acquisition gets harder. Retention becomes the answer. Next year is when everyone promises to get serious about it.

“IF we only increased retention by x, our business would change overnight.”

Maybe due to this newsletter, dozens of brands reached out to me this year with CX or retention emergencies. Some were simple: “I oversold and have no inventory to ship.” Others were deeper. Cohort retention, timing issues, stuff that doesn’t have an obvious answer.

As much as I love fielding them, I feel that it’d be 10 times more valuable to the class if I could share them and answer them here.

So this year, I’m going to start sharing those conversations in a new section called “Help Desk.”

Each issue will feature one real submission, anonymized for privacy, followed by my thoughts on how I’d approach it if it landed in my inbox.

You can submit here: https://forms.gle/wnbbWMuUvLGRpe9JA

This week, I want to discuss 5 things that might seem unserious but have transformed many brands I spoke to this year. And none of them are very complex.

Shall we???

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1. Time between order and fulfillment

I order from Dell more than I should. Partly the Amex Platinum Business credit. Partly because I keep convincing myself I need yet another eufy camera for another angle of my front yard.

(PSA: Eufy is made by Anker and has same quality devices as Ring or Nest without the monthly fees…)

Every time I place an order, I get the tracking within hours. The thing ships fast with consistent updates, from a boringly large company.

Most brands sit on orders for 24, 48, or sometimes 72 hours before anything moves.

To them, it’s processing time. To the customer, it’s dead space between paying and seeing progress. That gap is when buyer’s remorse sets in, or someone forgets why they bought it.

Brands that close that window fast feel like they have their sh!t together. Often the shipping is not even express, but they feel trustworthy when they act fast after you pay.

The only caveat is if you’re running pre-orders or delayed processing. Then you need to say that everywhere.

2. What happens when someone tries to cancel a subscription

Most brands either bury the cancel button or let people proceed without asking for confirmation.

The ones that get retention make it a conversation.

They don’t make it hard. They ask: What’s going on?

Sometimes the answer is “I don’t need this anymore.” Fine. But they can offer a skip instead of a cancel.

If it’s “I’m not seeing the value,” and it’s month one of a multivitamin subscription, you can push education in the flow. Then fix the post-purchase flow for everyone else.

Fixable problems. But only if you ask.

The brands I’ve seen handle this well use cancellation as a feedback loop. What appears there appears everywhere else later. Product gaps, messaging problems, timing you didn’t know was off.

3. Email tone after someone becomes a customer

Acquisition emails are polished. They’re selling, they’re working to earn attention, and they are very often “direct-response” offer testing.

Then someone buys, and the emails change. They get generic and templated, like circular slop, hoping something lands.

Your customers shouldn’t know if they’re in a flow or getting a campaign. They should feel like the messaging is resonating and speaking to them, regardless of the medium, message, or timing.

4. Whether you tell customers about problems before they notice

Most brands are reactive. Something breaks, customer reaches out, support fixes it.

By the time someone emails, they’ve decided you didn’t know. Or that you knew and didn’t care.

I’ve seen this dozens of times. Shipment delayed, no update until the customer asks. Item goes out of stock after purchase, confirmation still goes out. Payment fails, order still shows placed.

The issue was fixable. The customer thinking you had no idea wasn’t.

Brands that handle this well catch exceptions and communicate before being asked. “Your package is late, here’s why, here’s when it arrives.” Or “We’re out, we can send X instead or refund now.”

You can’t prevent every problem. You can prove you see them.

If you wait for the customer to tell you something’s wrong, you already lost.

Early in my career, I had a boss who always said “don’t wake a sleeping bear,” which I tend to agree with, but if the bear will likely wake up anyway and be 10x angry, I prefer to get ahead of it..?

5. Going back to customers after you fix the thing that broke

Most brands treat a bad experience as done. Someone had a problem and they churned.

A lot of those problems weren’t about the brand. The product didn’t work for their skin. Shipping to Canada took three weeks. They ordered the wrong size, and returning it sucked.

The thing they tried didn’t fit. No one came back to say anything had changed.

I’ve watched brands write off whole segments. Someone complains about slow shipping, gets refunded, and disappears. Six months later the brand switches carriers, cuts delivery time in half. No one follows up.

Same with product. Customer tries something, doesn’t work for them, moves on. Brand launches something new that would’ve been perfect. Silence.

Brands that get this right track why people left and reach out when it changes. Not generic win-back. Specific: “You said shipping took forever. We fixed it.”

Most churn isn’t permanent. But if you never go back, it becomes permanent.

Tag them. Use NPS with subscores. Track the reason code. FOLLOW UPPP!

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.