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Two visitors land on the same product page. One is browsing for the first time, casually curious. The other has returned four times this week, lingered on the size guide, and added the item to their basket twice without checking out.

Both see the same testimonial: "Life-changing product! Five stars!"

For most ecommerce brands, the bottleneck isn't the testimonial bank. The bottleneck is matching the right testimonial to the right visitor at the right moment in their journey. Your reviews database is probably already deep enough. What's missing is the logic on top of it.

So we're on the same page, what are customer testimonials? They're endorsements from real customers that serve as social proof to reduce purchase risk for new visitors. Used strategically, they are one of the most effective tools in ecommerce conversion, but only when matched to the right visitor at the right point in their buying journey.

That latter bit is where most brands lose the plot.

What customer testimonials actually do

Let's go back to basics and define customer testimonials.

Customer testimonials reduce perceived risk. When someone is about to spend £80 on a jumper they can't touch, £200 on a skincare set they can't smell, or £600 on a sofa they can't sit on, they are buying on faith. A testimonial transfers a small amount of trust from a stranger who has already taken that risk.

The numbers back this up. A product with five reviews is 270% more likely to be purchased than one with none (). Reviews flip the equation from "I hope this is good" to "other people like me said it's good."

So far, so good? Most ecommerce teams have internalised this for a decade. They have collected reviews. They have stars on PDPs. They have widgets pulling in the latest five-star quote. The job, on paper, is done.

Or is it? Conversion rates haven't moved much. Bounce rates on PDPs haven't dropped. The reviews are present, the trust signals are loud, and yet visitors still leave.

The question worth asking isn't whether customer testimonials work. They do. The more interesting question is: whose trust do they transfer, and when?

A first-time visitor doesn't need the same reassurance as someone who has been comparing brands for a fortnight. A price-sensitive shopper doesn't need the same nudge as someone who has already decided this is the brand. The same testimonial cannot do all of those jobs at once.

The next section is where the dominant approach starts to break down.

Why the same social proof examples don't convert every visitor

Brands built the dominant social proof playbook for a world with no reviews.

Collect them, display them prominently, serve them to everyone who lands on the site. That advice made sense in 2012. It made sense for brands going from zero to ten reviews. It does not make sense for an established retailer with 50,000 product reviews and millions of sessions a year, in 2026.

But the playbook hasn't been updated. So most brands keep adding more. Louder banners, "327 people are viewing this", scrolling testimonial carousels, urgency timers that reset every visit. More signals, served identically to every visitor. It's overwhelming.

David Mannheim, our CEO, puts it bluntly:

"Social proof nowadays is basically the same message to everyone. The urgency, the review snippets, the scarcity. It's a belief that more is more, that everything serves everyone. But really, social proof should only appear to those users at the right moment. It's a persuasive methodology; a nudge or a tactic at the right time. Suppress your social proof. Don't serve it to those that are just browsing, give them a different message. And don't serve it to those that are ready to buy, give them a different message. It's those just in the middle that need a nudge over the fence." — David Mannheim, CEO, Made With è����Ƶ

This reframes social proof examples from "always-on trust signals" to "situational nudges". And once you accept that frame, undifferentiated testimonials look less like a neutral baseline and more like an active risk.

Consider the mismatch scenario. Imagine a visitor who has never shown a price signal. They came in via a brand search, went straight to a hero product, and showed no comparison behaviour. You serve them a testimonial that says "great value for money". They were thinking about whether the product was right. Now there's a price question in the frame that wasn't there before.

Or take a returning visitor who has bought from you twice before. You show them a quote that says "perfect for beginners". The implicit message is: this isn't really for someone like you.

Neither of these social proof examples is wrong on its own. Both are wrong for that visitor at that moment. Whether the mismatch actively depresses conversion or simply fails to help, the outcome is the same: the testimonial isn't working. The fix isn't the testimonial. It's how the testimonial is delivered and when.

The ecommerce social proof gap: one buying journey, three different trust needs

A buying journey is not one job. It is at least three jobs, each requiring a different kind of trust.

This is the Made With è����Ƶ framework: the three intent stages we use operationally with clients. Other segmentation models exist, but this is the one we've found most actionable. You can read more about how we define and detect these stages here.

But if you're new here, briefly, this is what they are:

Discovery (first visit). The visitor needs category credibility. "I didn't know this kind of product could do X." They are not yet evaluating brands; they are evaluating whether the category is worth their attention at all. Generic enthusiasm ("I love it!") works here, because novelty is the barrier they need to clear. They don't need specifics. They need to get interested.

Consideration (comparing options). The visitor needs differentiation. "I compared three brands and chose this one because the fabric held up after 30 washes." They have already accepted the category. Now they're choosing between you and two others. Generic praise is useless because every competitor has it. What they need is a reason to prefer you, not just a reason to trust you.

High intent (near add-to-cart). The visitor needs hesitation removal. There is one specific objection holding them back, usually sizing, delivery timing, returns policy, or quality longevity. A five-star quote does nothing for this. A testimonial that opens "I was worried about the fit but..." is the right…fit (if you pardon the pun)

You might read this and think, "Oh boy, it's another SaaS blog saying really great things about their product." But, let's see some proof. David talks about a specific example from one of our clients:

"We had a customer and they have social proof on their site. It increased conversion by 3.2%. Great. However, once they analysed what that social proof was actually impacting, they found it worked quite well for those with a medium level of intent that needed a nudge. But really poorly — minus 2.2% — for those with low intent: their browsing, discovering visitors. By just suppressing it to those low-intent users and only showing it to high and building intent, their conversion rate jumped up by 20%." — David Mannheim, CEO, Made With è����Ƶ

To be clear on what that 20% represents: it is not an absolute conversion rate. It is the lift attributed to the social proof tactic itself. Previously, the tactic produced a +3.2% improvement to their overall conversion rate. After suppressing it for low-intent visitors, the same tactic produced a +20% improvement, because they had stopped letting one cohort cancel out the gains from another. The drag was hiding inside the aggregate.

We can't always prove that mismatched social proof actively causes harm, rather than simply missing its mark. In this case, suppression alone drove the improvement, which means at minimum the testimonial wasn't right for that particular audience.

Charley Bader, our VP Strategy & Ops, sees the same pattern in the building-intent stage:

"For those building intent it really worked. The people who have shown that intent to purchase and just need that slight push to tip into high intent and go through with the purchase." — Charley Bader, VP Strategy & Ops, Made With è����Ƶ

The bottom line is that discovery, consideration, and high intent are not three flavours of the same job. They are three different jobs entirely. Serving a discovery-stage testimonial to a high-intent visitor doesn't simply underperform. It interrupts a decision that was already in progress.

That's the ecommerce social proof gap. Most brands have built one trust layer for three trust problems.

You already have the ecommerce reviews. What's missing is the routing logic

Whenever we talk about this with retail teams, the first reaction is often: "We need more reviews."

Almost never true. For a retailer doing millions in online revenue, the reviews database is already enormous. Thousands of products, tens of thousands of reviews, often hundreds of millions of words of customer voice already collected and sitting in a or export.

The gap is not volume. The gap is the logic that sits on top of your review database.

There are two distinct layers most teams conflate:

Tagging: Categorise existing ecommerce reviews by the objection they address, not just by star rating or recency. A five-star review that says "arrived in 24 hours, beautifully packaged" is a delivery-objection review. A four-star review that says "took me two tries to find the right size but the second one is perfect" is a fit-objection review. These two reviews do completely different jobs even though they look similar in a database.

Routing. Once tagged, assign reviews to the pages or visitor stages where the matching objection is most likely active. Delivery-objection reviews belong in the basket and checkout. Fit-objection reviews belong on PDPs, especially for visitors who have viewed the size guide. Differentiation reviews belong in front of returning visitors. Generic enthusiasm reviews belong on category pages and discovery surfaces.

We've found this is the structural gap in nearly every retailer we work with. Almost every ecommerce team has spent years optimising review collection: incentives, post-purchase emails, photo prompts, NPS triggers. Almost none have spent equivalent effort on categorising their reviews based on intent.

If you've heard enough and would like to know more about Made With è����Ƶ, why do you book a demo?

Social proof website examples: matching testimonial type to visitor signal

If you're looking to serve more appropriate testimonials to your prospective customers, here's some tips on how to begin categorising them:

1. PDP for a considered-purchase item: Replace the generic five-star quote at the top of the page with a hesitation-removal testimonial. If you're in fashion, it could be something like: "I was unsure about sizing because I'm between a 10 and a 12, but the fit guide was right. The 12 sits perfectly." That single change reframes the page from "people like this product" to "people like you bought this product and it was a great service".

2. Returning visitor on their second or third visit to the same product: This visitor has moved past discovery. Showing them another "Wow, amazing!" quote tells them nothing they don't already feel. Show them a differentiation testimonial instead: "I'd looked at three other brands and this was the only one that didn't fall apart after a month." They're likely to be sizing you up versus the competition, so give them what they want.

3. Basket or checkout page: A visitor at checkout has cleared the product question; they're now resolving logistics. Replace the enthusiasm testimonial with logistics and trust testimonials: delivery speed, returns experience, customer service responsiveness. "Returned a dress and the refund hit my account in 48 hours" closes a real objection at the moment that objection is live.

If you want a starting point for next week, here's our suggested approach:

1. Pull a sample of 200 of your most-used reviews. Tag them by primary objection: delivery, quality, fit, price, trust/brand, generic enthusiasm.
2. Pick one high-traffic PDP where you currently serve a generic testimonial. Start with one: this will likely mean a CMS change or an override on your testimonial widget.
3. Swap the generic testimonial for an objection-specific one that matches the likely hesitation on that page.
4. Run it as a 50/50 split test against the original, minimum two weeks or until statistical significance.

You don't need new tooling for that test. You need a spreadsheet, a CMS edit, and a desire to use testimonials differently.

If you'd like to see an example of this in practice, Hunter & Gather achieved a 14% conversion uplift by showing social proof only to the visitors who needed it — the same targeting logic, applied to a real catalogue.

What intent-aware customer testimonials look like at scale

Manual matching gets you a long way. It does not get you all the way.

And that's because visitor intent shifts in real time, and it shifts based on signals you can't see in a tagged-review spreadsheet. Return visit frequency. Product page depth. Comparison behaviour across categories. Dwell time on size guides. Whether they've abandoned a basket before. Whether they're price-checking or feature-checking.

When customer testimonials are connected to those real-time signals, the right trust signal surfaces automatically. A visitor showing price-sensitivity behaviour gets a value-validation testimonial. A visitor at high intent with no price signal gets a quality or delivery testimonial, because price isn't the unresolved objection for them.

This is where intent-aware serving moves from a quarterly project to a continuous capability. If you're thinking about how this fits into a broader on-site personalisation strategy, Made With è����Ƶ analyses hundreds of behavioural signals in real time to score where each visitor is in their buying journey: the input that makes intent-aware testimonial serving possible at scale. You can see exactly how we apply this to social proof on the platform use case page.

If you're ready to see how Made With è����Ƶ identifies where each visitor is in their buying journey, book a demo.

Most brands only respond to cart abandonment after it's happened. That's like fitting a smoke alarm and calling it fire prevention.

online shopping carts are abandoned before checkout. That number hasn't meaningfully shifted in a decade.

Not because the industry hasn't tried. Cart abandonment emails are everywhere. Retargeting ads chase shoppers across the internet for days. Brands have invested millions in user recovery, the machinery that kicks in after someone walks away.

And it works. Abandoned cart emails recover between of lost baskets on a good day. But the thing about existing cart abandonment strategies is that they treat the problem after it has already happened.

What if there was a way to intervene before someone dumps their cart of goods? How would you get this visibility? In this blog post, we explore cart abandonment, discuss existing strategies and how new technology can provide timely interventions to stop basket abandonment in the moment.

What is cart abandonment? And why does it still matter?

Cart abandonment happens when a shopper adds items to their basket but leaves the site before completing a purchase. The global cart abandonment rate sits at roughly 70%, according to Baymard Institute's aggregated research across 49 studies — and it's been stubbornly consistent for years. For UK ecommerce brands doing millions in online revenue, that 70% represents an enormous amount of commercial value slipping away every single day.

The industry has treated this as a recovery problem. It's actually a visibility problem. Brands can't see why or, specifically, when shoppers hesitate, so they can't respond until it's too late.

The abandoned cart email: Essential, but not enough on its own

A well-built abandonment flow is one of the highest-ROI programmes in ecommerce, and the brands doing it well deserve credit for that.

But the returns are diminishing, and the reason is structural, not tactical.

A decade ago, a well-timed abandoned cart email felt personal. Now it's expected. Shoppers know the email is coming. Some even use it as a strategy: abandon the basket, wait for the discount code, complete the purchase at a lower price. If you always send these emails to every cart abandonment, you've trained consumers to do this.

The average abandoned cart email open rate is . That sounds impressive until you realise that fewer than half of those opens result in a click, and fewer than half of those clicks result in a purchase. You're recovering a fraction of a fraction.

The email programme isn't the problem. The problem is that it's the only thing working on abandonment. Everything that happens before the email. The entire session where the shopper was actually on your site is a gap.

Why shoppers abandon baskets (and why most brands get the diagnosis wrong)

Ask any ecommerce team why shoppers abandon baskets and you'll hear the same list: unexpected shipping costs, complicated checkout, required account creation, security concerns. Baymard Institute's checkout usability research has documented these friction points extensively, and they're real.

But they're also the easy answers. The ones that show up in surveys and exit polls because they're concrete and simple to articulate.

The harder truth is that most basket abandonment isn't caused by a single friction point. It's caused by unresolved hesitation.

A shopper adds something to their basket. They're interested, clearly — but they're not convinced.��

Maybe they're comparing prices elsewhere. Maybe they're not sure about sizing. Maybe they need to check with a partner. Maybe they're thinking about considered purchase and this is visit two of five. Maybe they’re just building a wishlist, and were never going to buy anyway?

None of these shoppers have a checkout problem. They have a confidence problem. And no amount of checkout optimisation or recovery email will fix that — because by the time the checkout loads or the email arrives, the moment has passed.

The real gap: what happens during the session

Let’s try a thought experiment. Let’s categorise ecommerce into black and white terms. On one end: acquisition, getting shoppers to the site. On the other: recovery, trying to win them back after they've left. The bit in the middle, the actual shopping session, is where you have the least visibility and the fewest tools at your disposal,.

Think about what a good shop assistant does in a physical store. They don't wait until you've put something down and walked out, then chase you into the car park with a voucher. They read the room. They notice when you're browsing versus when you're comparing. They step in when you look uncertain and step back when you're clearly decided.

Online, we do the opposite. We show everyone the same experience — same pop-ups, same messaging, same urgency banners — regardless of whether they arrived 10 seconds ago or have been comparing products across three sessions over two weeks. Then, when they leave, we send the email.

The gap isn't in recovery. It's in the session itself. The question isn't "how do we get them back?" it's "why didn't we respond to what they were telling us while they were still here?"

What in-session card abandonment intervention actually looks like

In-session intervention means responding to shopper behaviour during the visit, not after it. But (and this is the critical part) it doesn't mean bombarding people with more pop-ups and discount codes.

The problem with most "onsite intervention" is that it's based on static rules, and doesn’t interject at the moment. ����’s usually after the moment has passed. Show a pop-up after 30 seconds. Trigger an exit-intent overlay when the cursor moves toward the tab. Offer 10% off to everyone who has items in their basket.

These rules treat every shopper the same. A first-time visitor browsing casually gets the same intervention as a returning visitor who's viewed the same product four times and is clearly ready to buy. That's not intervention. That's bad manners.

Real in-session intervention requires knowing where a shopper is in their buying journey — right now, in this session, and responding appropriately.

For a visitor who's browsing early in their journey: don't push. Show them content. Help them discover products. Surface reviews, comparisons, and reasons to believe. The worst thing you can do is ask for a commitment before they’re ready.

For a visitor who's been comparing across multiple sessions and has returned to a specific product: they don't need a discount. They need reassurance. Delivery information. Stock availability. Social proof that others bought and loved this item. Remove the doubt, and they'll convert without a price incentive.

For a visitor showing every signal of purchase intent but hesitating at the basket: now a small nudge might help. Free delivery. A modest discount. A reminder that the item is selling fast. But even here, the intervention should match the hesitation, not just throw money at it.

This requires intent data, the ability to model behavioural signals within a session and predict where a shopper is in their buying journey before they abandon.

Full disclosure: this is what we do at Made With è����Ƶ. But the principle holds regardless of how you implement it. If you can see where a visitor is in their buying journey, you can respond before they leave. Whether you build that capability internally, stitch it together from your existing stack, or use a dedicated tool, the strategic point is the same.

The economics of basket abandonment prevention�� versus recovery

Let's put some numbers on this.

A mid-market ecommerce brand with 500,000 monthly sessions, a 3% conversion rate, and a £75 average order value generates roughly £1.1M per month. At a 70% cart abandonment rate, shoppers are adding items to baskets in around 50,000 sessions but only completing 15,000 of those purchases. Roughly 35,000 baskets are abandoned every month.

A strong abandoned cart email programme recovers 3–5% of those, say 1,400 orders, worth around £105,000 per month. That's meaningful revenue, and it should absolutely keep running.

Now consider what happens if you can prevent even a small percentage of those 35,000 abandonments from happening in the first place. A 5% reduction in abandonment just by delivering better in-session experience would save 1,750 baskets. At £75 AOV, that's £131,000 per month in revenue that was never lost and never needed recovering.

Prevention requires investment, tooling, configuration, and ongoing optimisation. It isn't free. But here's where the margin argument gets interesting.

Recovery emails frequently include a 10% discount as the incentive. On £105,000 of recovered revenue, that's £10,500 in margin given away every month. Not because every one of those 1,400 shoppers needed a discount to convert, but because, without real-time intent data, you have no way to know which ones did.

This is exactly the problem Better Bathrooms faced. Without visibility into visitor intent, they were stuck in a choice most ecommerce teams know well: discount broadly and erode margin, or do nothing and accept the exit rate. Neither option was good enough.��

By activating in-session intent signals to target discounts only at visitors who had built purchase intent but were showing signs of dropping off, they broke out of that trade-off entirely, lifting conversion rate by 26% while protecting the margin they'd previously been leaking. The discount didn't change. The targeting did.

Over a year, that margin difference compounds significantly, often enough to fund the prevention capability several times over.

The two approaches aren't in competition. They're complementary layers. But most brands have invested heavily in recovery and barely at all in prevention. The opportunity is in rebalancing that investment.

Why brands haven't done this already

If in-session intervention is so effective, why isn't everyone doing it?

Because until recently, brands couldn't see what was happening during the session in a meaningful way.

Most ecommerce teams work with two types of visitor data. Historical data tells you what someone did last time, with things like their purchase history, their email engagement, their lifetime value segment. Page-level data tells you what page they're on right now. Neither tells you where they are in their buying journey in this session.

Are they browsing or buying? Are they comparing options or ready to commit? How likely are they to buy or abandon their purchase ?

Without answers to these questions, every visitor with items in their basket looks the same. You can't intervene differently because you can't see differently. So you wait until they leave, and you send the email.

è����Ƶ data changes this equation. By modelling hundreds of behavioural signals within a session — scroll depth, navigation patterns, time on page, return visit frequency, basket interaction, comparison behaviour — it becomes possible to predict where a shopper is in their buying journey before they abandon. Not after.

What this means for your abandonment strategy

If your entire cart abandonment strategy is a post-session email flow, you've built one layer of a two-layer system. The email works. The question is what sits alongside it.

Here's what an intent-based abandonment strategy looks like:

During the session: Use intent signals to identify visitors who are showing signs of hesitation. Respond with the right experience, reassurance for the nearly-convinced, content for the still-exploring, and targeted incentives only where they're genuinely needed.

At the point of exit: If a visitor does move to leave, your exit-intent experience should be informed by their session behaviour, not a one-size-fits-all overlay. A returning high-intent visitor doesn't need 10% off. They need a reason to buy now.

After the session: Continue running your abandoned cart email programme. But let it be the safety net beneath a more complete strategy, not the whole strategy. And personalise those emails with intent data from the session — a shopper who was comparing options needs different messaging than one who got to the payment page and stopped. For instance, if someone has multiple items in their basket, which item did they have a real preference for if any?

In addition, because you’ll be sending fewer cart abandonment emails, and only sending them to the visitors with appropriate intent, your number of sends and unsubscribes will naturally go down.

Across sessions: Recognise returning visitors who previously abandoned baskets. Their second visit is the highest-value moment in the entire journey — they came back because they're still interested. Don't waste it by showing them the same generic experience they saw last time.

The 70% isn't going away. Your response to it should evolve.

Cart abandonment isn't a problem to solve. A 70% abandonment rate reflects the reality of how people shop online — browsing, comparing, considering, returning, and eventually buying. Fighting that reality is a losing game.

What you can change is how completely you respond to it. Most brands have built strong recovery programmes. That's the foundation. The next step is building the capability that sits before recovery — the in-session layer that catches hesitation while it's still happening, responds to what each visitor actually needs, and prevents a portion of those abandonments from ever reaching the email queue.

The brands that build both layers don't just reduce their abandonment rate. They reduce their dependency on discounts to recover lost revenue. They protect their margins. And they build a better experience for their shoppers — one that responds to what visitors need in the moment, rather than chasing them after the moment has passed.

Your abandoned cart email is the safety net. It should stay. But the real opportunity sits earlier — in the session, in the signals, in the moments where the right experience could have kept that shopper moving forward.

Made With è����Ƶ helps ecommerce brands see where every visitor is in their buying journey and respond in the moments that matter, reducing cart abandonment. If you want to add the in-session layer to your abandonment strategy, book a demo.

You've done the work. You've split your lapsed customers from your actives, your high-AOV buyers from your one-time purchasers, your engaged openers from the dormant half of your list. You've built the flows and set the rules. And your recovery rates are still flat.

The problem is your email segmentation tells you who's on your list and what they've done. They don't tell you who's in a buying moment right now. That's not a gap in your execution, it's merely a limitation of how segmentation works. And until you see it clearly, you'll keep optimising the wrong thing.

Email segmentation is built on historical data. Purchase history, past engagement, demographic profile. It tells you who someone was. But, it doesn't tell you what they're about to do. The signal that tells you whether someone is actively considering a purchase right now is a different kind of data entirely and it lives somewhere most email strategies never look.

What email segmentation actually tells you

Standard email segmentation is genuinely useful. Don't let the argument of this article dispel this notion.

When you split your list by purchase history, you're identifying category affinity and buying patterns. When you score subscribers by recency, frequency, and monetary value, the classic RFM model, you're surfacing the customers most likely to respond to an offer at a population level. When you tag customers by product category or browsing history, you can send more relevant messages than a broadcast to everyone.

These are real improvements. A CRM manager who has moved from bulk email to properly structured behavioural segmentation has genuinely raised their ceiling. Open rates improve. Revenue per send goes up. Unsubscribe rates come down.

However, segmentation is a map of things that have already happened. It's also focused on past behaviours, i.e. when someone has already signed up for an email. The ability to act immediately simply isn't there.

Your post-purchase segment tells you someone bought. Your lapsed segment tells you they haven't bought recently. Your engagement segment tells you who opened your last campaign. None of it tells you which of those subscribers is actively considering a purchase right now, in this session, having visited your site three times this week to look at the same product.

That's the gap. And it's why flat recovery rates survive even well-built segments.

The gap: why past behaviour isn't the same as present intent

To understand why this matters practically, you need to separate two things that email marketing tends to conflate: who someone is on your list and where they are in their buying journey right now.

There are two dimensions to this: the in-session signal (what this person is doing right now) and the broader buying window (are they actively in the market at all?). Standard segments miss both.

The historical data problem

Think about RFM. A customer in your "champions" segment (high recency, high frequency, high value) is someone you'd rightly treat as a priority. But their segment tells you they've bought before and bought recently. It doesn't tell you whether they're actively considering a purchase today. They might be. They might also have bought what they needed last month and have no intention of buying again for six weeks.

RFM is excellent at predicting which customers are likely to respond to a campaign over time, at a population level. It isn't designed to tell you which specific customer is in an active buying moment right now. Those are different questions, and treating the first as a proxy for the second is where the gap opens.

The engagement data problem

The standard workaround is engagement-based segmentation: split your list into openers and non-openers, active and inactive, and weight your sends accordingly. The principle is sound. The inputs have a serious problem.

Since Apple introduced Mail Privacy Protection with iOS 15 in September 2021, a proportion of email opens have been pre-fetched by Apple's servers rather than triggered by a subscriber actually opening the email.

According to , over 50% of email opens now occur on devices with Apple's Mail Privacy Protection activated. For UK fashion, beauty, and home retailers, where iPhone dominates device usage, the proportion of affected opens on your list is likely significant.

If your engagement-based segments were built or last reviewed before late 2021, it's worth auditing what proportion of your "engaged subscriber" definition rests on open rate signals and whether click and conversion data could give you a more reliable proxy. However, this isn't us saying "abandon engagement segmentation". We're saying you've got a reason to check that your inputs still mean what you think they mean.

The timing problem

Even if your segment is perfectly accurate, it doesn't solve the timing problem.

Suppose you have a genuinely high-intent customer: they're in market, they've been browsing your site, they're ready to buy. If that customer is in your 60-day lapsed segment, they'll receive your win-back flow on whatever cadence that flow runs. If they're in your post-purchase segment, they'll receive your next post-purchase email at the point the flow schedules it.

Neither of those flows asks: is this person ready to buy today? The segments tell you who to send to. They don't tell you when that person is receptive, or when they're actively in the middle of a buying decision that the right message could tip.

What "ready to buy" actually looks like

Buying intent doesn't show up in your CRM. It shows up in behaviour, but not in the way most email strategies assume.

The instinct is to look for observable signals: a customer who's visited the site twice this week, spent time on the outerwear category, or added something to their basket and removed it again.

These patterns feel meaningful, and they are. But they're proxies. They approximate intent rather than measure it. And proxies are very good at generalising.

Return visits to the same category could mean active consideration. It could also mean idle browsing, research with no near-term purchase plan, or a customer who's decided not to buy and is still processing why. Two customers can leave identical behavioural footprints with completely different intent trajectories.

The distinction that matters here is between an intent proxy and an intent prediction.

A proxy uses a single observable signal. So, page views, return visits, time on site, as a way to characterise specific behaviours.

A prediction uses hundreds of behavioural signals together. Things like scroll patterns, hesitation, comparison behaviour, click timing, revisit frequency, and models the probability that a specific visitor, right now, is likely to purchase.

That difference matters for email specifically because your flows are triggered on schedule, not on signal.

Consider a customer in your 90-day lapsed segment. Their segment says win-back flow, probably with a discount. But a real-time intent prediction across their session behaviour might tell a different story: they returned twice this week, built strong product affinity for outerwear without adding to cart — affinity that was visible well before any CTA click — and their intent has been rising, not falling, across the session.

That isn't a lapsed customer who needs persuading. Instead, it's someone ready to buy. They don't need a discount at all.

If you're offering discounts to people like that, all you're doing is eroding your margin. And let's just play with some numbers for a second. If you have a £120 average order value, a 20% discount on sale you would've made anyway costs you £24 pounds for that one sale. But it also costs for every single customer you've offered the same discount to.

Your email strategy can see who someone is and what they've done before. It can't see the modelled probability that they will purchase today. That requires a different layer of data entirely. (Spoiler: It's Made With è����Ƶ)

How to close the gap between your CRM and what's happening on-site

There are three ways to close it, and it's worth being honest about what each one costs.

1. Layer session behaviour onto send triggers — trigger sends based on a real-time on-site event rather than a profile segment on a schedule. If your ESP is Klaviyo, the ActiveOnSite flow trigger gets you closer. You're still dependent on session-entry events rather than continuous in-session behavioural signals, but it's a step in the right direction.

2. Prioritise timeliness over segment precision — tighten the timing of your event-based flows. Klaviyo's , covering more than 143,000 flows, recommends sending the first recovery email within 2-4 hours of abandonment. Most brands set this window far wider. The window of peak intent closes faster than most email schedules assume.

3. Use intent-based scoring to qualify your existing segments — add a signal layer on top of your CRM segments that identifies which subscribers are currently showing on-site behaviour indicating an active buying moment. Platforms like Made with è����Ƶ analyse hundreds of behavioural signals in real time: return visit frequency, product page depth, comparison behaviour, session patterns. The result is a send informed by both who the customer is and where they are right now.

What this means for how you build segments

Audit your current flows against three questions:

• What is the trigger? Profile rule, onsite event or intent prediction?
• Does it rely on open rate engagement? If built before 2021, review whether click/conversion data could replace open rate as the proxy.‍
• Is it event-led? Tighten the send window to match the actual window of intent.

The question your segments can't answer

The most commercially important question: is this person ready to buy right now, isn't answered by a session behaviour alone. It's answered by a prediction built across hundreds of behavioural signals in that session, continuously updated as the customer moves.

That's not something a segment can produce. And it's not something a single observable proxy can substitute for.

Want to learn more about intent-based selling? Grab yourself a demo.

Social proof is everywhere. And that’s the problem.

Most brands run it sitewide, triggered on page-load to each and every visitors. It works brilliantly for some visitors, but damages conversion for others. Early urgency messages can cause anxiety and exits, especially for visitors who have yet to show any intent to purchase.

We covered the status quo of social proof already, but for a quick recap, if you only measure generic conversion rate uplift, you see the benefit for those it helps but miss the hidden downside for those it turns off. That’s why the first step is to rethink why you’re using it at all.

Remind yourself why you’re using social proof

Why are you really doing social proof? It may start off as a best practice, that is low-hanging fruit to “increase conversion rate” or “drive more revenue”, but that’s only half the story. Social Proof is ultimately about encouraging a certain behaviour from an individual by using the influence that the actions, choices or approvals of others have on them. And when you apply the message to everyone, all the time, you’re missing the real opportunity - and ultimately playing conversion roulette.

Urgency-style proof like “X people bought this today” might give high-intent visitors the final nudge, but it can spook a casual browser into leaving. Without understanding these nuances of an experience (by splitting results by audience and keeping a control group for each stage), you’ll never see the drop hiding inside your averages. Which brings us to the next question: who exactly are you showing it to?

Rethink who you’re delivering it to

Do all your visitors get the same message at the same time? They shouldn’t. High-intent visitors close to purchase are often persuaded by urgency. That same message can make a low-intent browser feel pushed and leave. In fact, urgency messaging can decrease conversion for low-intent visitors by 4–5%.

Instead, Bestseller messaging can help low-intent users refine their choices by pointing them to popular items. But it can also distract a high-intent shopper who’s already found what they want, just like showing unrelated recommendations in checkout can derail the final purchase. The key is knowing which messages work for which segments — and that’s where a more targeted, step-by-step approach comes in.

Step-by-step: How to get started with intent-based social proof

1: Analyse or test across intent stages

Start with what you’re running today, but split results by low, building, and high intent. Maintain a control group for each stage to compare “no message” against “message.” This is where the surprises appear. Urgency might lift high-intent conversion by 10% but drop low-intent by 4–5%.

2: Exclude the unsuited audiences

If urgency is scaring off browsers, remove it for those segments. Replace it with different messaging styles that fit their stage. Bestseller or top-rated messages can help low-intent visitors explore, while reassurance works better for those already on the brink of purchase. For segments where the original Social Proof didn’t work, consider testing alternative messages entirely.

3: Layer in real-time signals

Static triggers are blunt. Use signals like a drop in purchase confidence or increase in a visitor’s likelihood to abandon to time your messaging more precisely. For example, one jewellery brand predicted basket backtracking and swapped urgency for reassurance style Social Proof. This single change lifted conversion for that segment by 25%.

4: Bring in affinities and more

Make it personal. If someone’s deep into a specific brand or category, show them messaging that reflects it. “Custom rings designed this month” speaks to an engagement ring shopper. In a multi-brand store, tie the experience to brand loyalty, like “People who love [Brand X] also love this.”

5: Iterate and evolve

For segments without strong affinities, use it as a discovery tool rather than reassurance. Keep testing new messages for each stage, refining your targeting, and improving your timing. Social proof should get sharper over time, not sit as a static, set-and-forget feature.

A real-world example from a jewellery & watch retailer

A premium UK jewellery and watch retailer faced a familiar problem. Shoppers were reaching the basket, then backing out to browse again or revisit product pages. For higher-value, considered purchases, this hesitation was a sign of uncertainty. The team realised not every basket visitor needed urgency, some needed reassurance.

Using intent signals, they built a segment of visitors showing backtracking behaviour and delivered targeted in-basket reassurance, highlighting flexible delivery and secure payment options. This message appeared only to those who needed it, avoiding unnecessary noise for confident buyers. The result was a 25% uplift in conversion for that segment.

This example shows how the right message at the right moment can have a big impact, which leads to the bigger picture of what happens when you get intent-based social proof right. You can read the full customer story here.

The impact of using intent in social proof

When intent guides your targeting and timing, you keep the uplift without the hidden drop-offs. Better timing can amplify gains, and removing harmful triggers boosts coverage. Over time, social proof becomes a natural part of the journey, helping with discovery, reassurance, and evaluation, instead of a blunt, one-size-fits-all tactic.

Social proof works when it works for the right people. è����Ƶ makes that possible. Want to see what that could look like for your brand? Book a demo.

Social proof should be one of the most powerful tools in ecommerce. At its core, it’s the influence that the actions, choices or approvals of others have on an individual’s behaviour.

People look to others when they’re uncertain about what to choose, who to trust, or whether to act. In ecommerce, that influence can appear anywhere in the journey. As reassurance that a brand is worth buying from, or as urgency to act before missing out.

It comes in many formats: scarcity messages (“only 3 left”), activity indicators (add to baskets, recent views, recent purchases), reviews and ratings, and trending or bestseller labels. Used well, these cues can reassure, create urgency, and help people find what’s popular or trusted.

The problem is, social proof has become one of the most overused and underthought tactics in the game. ����’s often deployed as a blanket message to everyone, with little thought about whether it fits their mindset or the brand experience.

Retailers love it because it’s quick to turn on and almost always delivers an aggregate uplift. But those uplifts are often driven by a smaller group, and the negative effects on others are hidden in the averages.

The status quo of social proof

Most ecommerce teams apply it generically, showing the same messages to everyone – often on every product page. The most common use is as a conversion-driving technique late in the journey, but there’s a growing trend to apply it earlier in discovery (e.g., “bestseller” on PLPs).

Its popularity comes from being considered “best practice,” easy vendor implementation, and the reliable ROI it shows on aggregate. But those aggregate numbers are disproportionately influenced by high-intent visitors, which hides the harm it can cause to others.

What works for one mindset can actively put another off. As part of our research for The è����Ƶ Gap Report, we found:

• “Trending” overlays on PLPs positively impact low-intent browsers.
• “X sold last week” overlays on checkout pages deliver an average +5% conversion lift for high-intent visitors but cause a -1% drop for low-intent visitors.

Luxury and exclusivity-driven brands often avoid generic social proof entirely. In high-consideration categories, it can feel out of place – an engagement ring buyer doesn’t want to hear that “20 others bought this today,” and a £3000 jacket doesn’t need a flashing urgency tag over carefully curated imagery. In these cases, overlays can jar with the brand and undermine the premium feel.

When social proof is everywhere, it stops providing reassurance or focus. The message becomes noise, prompting the question: why stick with this approach?

Because most retailers rely on page-type triggers (e.g., PDP = ready to buy). But many PDP visitors are still browsing. Without behavioural context, tactics are based on where someone is, not how they’re behaving. That one-size-fits-all approach ignores timing and mindset. And that’s exactly why it needs a rethink.

Social proof with intent

Social proof can reassure early in the journey or create urgency later, but timing and fit are critical. Softer cues like “bestseller” or “trending” help those still discovering products. Urgency or scarcity works best when someone has decided what they want and just needs a final nudge. Use it too soon, and it risks creating anxiety or distraction.

Think of walking into a DIY store paint aisle: if you’re browsing, you don’t want someone saying, “Only three tins left – buy now!” before you’ve chosen a colour. But if you’re holding the exact tin you want, that message might spur you to buy. The same logic applies online.

Or picture a luxury sales assistant with a £3000 jacket. They wouldn’t start with “20 people bought this today.” They’d focus on its quality, heritage, or popular combinations, tailoring the message to the moment.

Real-time intent signals mean you can:

• Show discovery-style social proof to those exploring
• Reserve urgency and scarcity for visitors with strong product interest or signs of hesitation
• Avoid showing it altogether to those it might deter

When you match the message to the moment, social proof stops being background noise and starts driving action.

The path to better social proof

While we’ll cover how to move from generic application to something more intent-based in a follow up, the core steps are:

1. Analyse performance by visitor mindset, not just aggregate.
2. Exclude audiences where a message harms conversion.
3. Adapt style and timing to fit both brand tone and visitor context.

The benefits? Higher incremental gains, reduced brand risk, and interactions that build trust.

Social proof works – but not for everyone, not everywhere, and not all the time. The more you align it with intent, the more it delivers.

Ready to deliver social proof that meets the moment? Discover how Feature Delivery with è����Ƶ works.

Abandoned cart emails became a key part of the CRM playbook for a reason. They’re easy to set up, look great in reports and are seen as a no-brainer for driving conversions. But let’s be honest. Most of them are blunt. They ignore why shoppers abandoned in the first place and often end up adding noise instead of value for the visitor.

In a recent piece, Rethinking abandonment emails with intent, we explored why this tactic so often falls short. The reach is limited to visitors you can actually email. The timing often misses the moment. And blanket discounts don’t just erode margin, they train shoppers to delay purchases.

If you haven’t read that yet, it’s worth a look. But this article is about moving forward. Here’s how CRM teams can use use intent with abandonment emails to make them smarter, more targeted and more effective.

Review why you are sending abandonment emails (and who to)

����’s easy to assume the goal of abandonment emails is simple: recover a lost sale. But was it a lost sale to begin with? Just having items in a cart isn’t always a signal of high purchase intent. Shoppers use them to shortlist products, compare options or as a save-for-later tool.

If the goal is to recover real opportunities, this tactic needs refining.

Not every abandoner should get an email. Without context, CRM teams risk:

• Sending to shoppers who are still browsing and not yet ready to buy.
• Triggering emails too soon or too late.
• Flooding inboxes with irrelevant reminders.

The consequences? Unsubscribes. Inbox fatigue. And lost trust. Excluding shoppers who aren’t ready to buy isn’t only a better experience. It also leaves room for emails that actually work, allowing you to send them with impact.

If someone’s adding to cart to compare or wishlist items, hitting them with a salesy reminder could risk turning them off completely.

So how do you make abandoned cart emails smarter, more targeted and more effective? Start here.

Optimising abandonment emails with intent

This isn’t about rebuilding from scratch. ����’s about fixing the foundations first, reducing downside and then optimising for growth.

Step 1: Analyse

Start by reviewing your current campaigns with intent data. Segment visitors by mindset and product affinity. Understand which groups engage and which ones churn. Look beyond standard metrics like open rates. Ask who clicked, who converted and, crucially, who unsubscribed.

Step 2: Exclude

Stop sending to low-intent visitors. Protect your list health by cutting out disengaged shoppers who are unlikely to convert. Focus efforts where they’ll actually move the needle.

Step 3: Improve

Optimise the emails you do send to high-intent visitors. This isn’t just about tweaking subject lines. Think about mindset. If a shopper is in discovery mode, avoid hard-sell copy. Instead, highlight educational content, social proof or unique selling points to build confidence. For those showing strong purchase intent, timely nudges and delivery reassurance might be all they need to convert.

One area many retailers get wrong is discounting. Blanket incentives erode margin and train shoppers to wait. Instead, reserve offers for visitors showing clear signs of hesitation, with an unlikelihood to return to site.

Grow with better emails and beyond the inbox

Once you’ve reduced downside and optimised for impact, you’re ready to grow further.

In email, you can tailor creative based on intent stage and affinities. Think beyond discounts or nurture flows. You can even explore dynamic recommendations for basket builders and cross-sell opportunities. This isn’t about flooding them with options but about making the right product feel obvious.

One of our customers, a leading UK jeweller, faced this challenge head on. Their CRM team realised their “one-size-fits-all” abandonment email was limiting relevance and risking engagement. They started with our exclusion and optimisation steps, then moved onto more context-driven creative.

They created three visitor groups: low, building and high intent. Each group received tailored emails. High-intent abandoners got a timely, persuasive message tied to browsed products. Lower-intent visitors were sent softer campaigns focused on brand USPs. Some received no email at all to protect list health.

The result? A 12% uplift in click-through rates and a strategy that felt more like a conversation than a conversion ploy. Read the full customer story here.

But your response doesn’t have to stop at the inbox.

Onsite, once you detect exit signals in real time, you can trigger supportive nudges before visitors abandon, such as delivery reassurance or save-for-later prompts. You can also surface email capture prompts for unknown visitors at the right moment to grow your contactable base.

When onsite and email journeys are connected, you’re no longer chasing abandoners after they’ve gone. You’re helping them complete the journey in the moment.

The impact when you get this right

When you rethink abandonment emails with intent, shoppers feel understood instead of pestered. CRM teams send fewer, smarter emails that actually drive revenue.

Metrics improve across the board too:

• Unsubscribe rates drop dramatically due to less inappropriate emails
• Click-through rates (CTR) climb as relevance improves
• Higher return visits and positive movement on intent to return metrics.
• On average, Made With è����Ƶ users see a 65 percent increase in campaign impact overall

This isn’t just about improving KPIs. ����’s about changing how shoppers feel when they hear from you. è����Ƶ-based emails create relevance, reduce noise and rebuild trust. They don’t just recover sales. They set the stage for long-term growth.

Want to see how intent-first abandonment emails work in practice? Get a demo to learn how Made With è����Ƶ helps teams recover more revenue without damaging their shoppers’ experience.