Best abandoned cart emails: 10 real examples reviewed (and what they actually assume about your customer)

Why standard advice on eCommerce bounce rate might not be enough

In 2023, Google Analytics 4 replaced Universal Analytics (UA) as the default, shifting the definition of bounce rate entirely. Under UA, any single-page session counted as a bounce, regardless of how long the visitor stayed or what they did. Under GA4, a "bounce" is a session with no meaningful engagement in the first ten seconds (Google Analytics Help, 2023).

Most published advice on reducing eCommerce bounce rate, including almost everything ranking on the first page of Google right now, predates that change. The benchmarks cited, the comparisons drawn, the thresholds used to define a "good" or "bad" rate: much of it is calibrated to a metric that no longer exists in its original form. It's worth bearing in mind before treating a published figure as a reliable signal that something is broken.

There's a connection here to the intent signals argument. GA4 defines a bounce as a session with no meaningful engagement, which is itself the absence of any intent signal. However, the metric has quietly moved closer to what we're suggesting: that what matters is whether a visitor showed signs of engagement and intent, not simply whether they viewed more than one page.

The standard fixes aren't useless. A page that takes four seconds to load on mobile will lose shoppers. Navigation that buries products three levels deep creates friction. These are real problems. But they're also largely table stakes. Most mid-market eCommerce teams have addressed them, or at least know they need to.

What the generic checklist rarely asks is: why did this particular visitor leave this particular page? Speed explains some of it. Confusing navigation explains more. But there's a third explanation that gets far less attention: the visitor arrived with a specific intent, and the experience they landed on didn't reflect it.

What on-site intent signals actually are

Intent signals aren't abstract. They're specific, observable events already firing on your site every day, most of them visible in your analytics if you know where to look.

Consider what happens in the first thirty seconds of a session. A visitor lands on a product detail page. Do they scroll past the first image, or stop there? Do they interact with the size selector, or skip straight past it? Do they hover over the "Add to Basket" button without clicking? Do they navigate to a second product, return to the category page, or leave entirely?

Each of those micro-behaviours carries a signal. Taken individually, they don't mean anything. Taken together, they start to suggest something about intent: whether the visitor is browsing loosely, comparing seriously, or hitting a wall they can't get past.

Some of the most informative on-site signals include:

- Scroll depth: how far down the page a visitor gets before stopping or leaving

- Hover behaviour: where the cursor lingers without a click (interest that didn't convert to action)

- On-site search queries: what visitors type into the search bar, and crucially, what they do next

- Dwell time relative to site average: a visitor spending significantly longer on a PDP than average may be closer to buying than the raw bounce metric suggests

- Variant and size selection: engaging with product options is a meaningful buying signal, regardless of whether a purchase follows

- Back-navigation patterns: returning from a PDP to the same category page repeatedly often indicates comparison behaviour, not disinterest

None of these signals individually tells you what a visitor intends to do. But patterns across them might tell you something useful about why they're not finding what they came for, and whether the bounce that follows is a genuine commercial loss or an inevitable one.

For a deeper look at why behavioural signals tend to outperform the proxy metrics most teams rely on, Predictions Not Proxies, our blog post, is worth a read.

The signals worth watching by page type

One limitation of tracking bounce rate as a single site-wide number is that it flattens very different problems into one metric. A visitor who bounces from the homepage is probably experiencing something quite different from one who bounces from a product detail page after two minutes of engagement. The signals worth reading, and the interventions that might help, differ depending on where the bounce is happening.

Homepage

Homepage bounces are often about relevance at first impression. Was the visitor expecting something the page doesn't immediately surface? Traffic source matters here. A visitor arriving from a paid social ad promoting a specific sale and landing on a generic homepage is likely to read that as a mismatch before they've even scrolled.

You should, instead, consider time to first scroll, engagement with any navigation element, and click-through to any product page. A visitor who lands and never scrolls is usually gone for reasons that faster load times can't fully address.

Category pages

Bounce from a category page often points to a discovery problem. Either the product range isn't what the visitor expected, or the tools for narrowing it down (filters, sorting, on-site search) aren't doing their job.

You could monitor for filter use, scroll depth through the product grid, and whether visitors click through to multiple PDPs or just one (or none). A visitor who opens the filter panel but doesn't apply anything may be signalling that the available options don't map to what they had in mind.

Product detail pages

PDP bounce is the most commercially sensitive, because this is where visitors are closest to a decision and where intent signals tend to be richest. Image engagement, variant selection, and dwell time relative to site average can all suggest whether a visitor is actively evaluating or has already decided the product isn't right.

A visitor who spends three minutes on a PDP, selects a size, and then leaves is a very different prospect from one who bounced in under ten seconds. Treating both as equivalent in a site-wide bounce metric misses that distinction entirely, and probably points the subsequent analysis in the wrong direction.

Paid landing pages

For pages receiving meaningful paid traffic, the most important signal is often the simplest: does the message on the page match the ad that brought the visitor here? Post-click relevance is frequently the first place to look when bounce rate on paid traffic is elevated, before speed or UX enter the conversation.

Consider a fashion retailer seeing high PDP bounce from paid social. You may think audience mismatch or slow load times. But scroll depth data tells a different story. Visitors are reaching the size selector and stopping, not scrolling away. The real problem is out-of-stock variants being featured in the ad creative. Shoppers arrive, find their size unavailable, and leave. This is nothing to do with a UX fix, simply a case of reading the right signal, the right context.

Acting on intent signals before the bounce happens

Reading these signals is useful. Responding to them is where it gets more interesting.

The standard eCommerce pop-up is a useful counterexample. A blanket overlay triggered by exit intent, offering a discount to everyone regardless of what they've been doing, ignores every signal the visitor has sent. A visitor who spent four minutes on a PDP, selected a variant, and then paused receives the same intervention as one who arrived and left in eight seconds. So, really, you're not delivering a proper personalised experience that's appropriate to your customers' different needs.

A more considered approach starts with the signal, instead of the user navigating away. A visitor showing strong PDP engagement (extended dwell time, variant selection, multiple image views) is telling you something. The right response is probably social proof surfaced at that moment: stock scarcity, recent purchase activity, a well-timed review. Not a discount. A visitor who used the search bar, found no useful results, and is now leaving needs a different intervention entirely: a related product suggestion, or a prompt to browse a relevant category.

The interventions don't have to be complex to be more relevant. Start with your highest-bounce page type, identify which signals are already firing there, and ask whether your current exit triggers reflect any of them. That's a reasonable first step, and it costs nothing to consider.

If you're looking at how this kind of approach works for browse abandonment specifically, our browse abandonment use case walks through one way to think about it.

What a bounce means for your CRM, and why it matters

Bounce rate is typically discussed as a traffic or UX problem. It's less often discussed as a CRM problem. But there's an argument that it should be.

Every visitor who leaves without converting, subscribing, or taking any traceable action represents not just a lost session, but a lost contact. For a brand spending meaningfully on paid acquisition, that loss isn't only the missed immediate sale. It's the absence of any first-party data to re-engage with later. The CAC clock is ticking whether or not the visit converts.

The intent signals that might help reduce bounce in the moment are the same signals that could inform a more relevant recovery sequence when the bounce does happen. A visitor who engaged with a specific category, hovered on a product, and then left is a different re-engagement prospect from one who arrived on the homepage and bounced immediately. Where that behavioural data is captured, it can feed a browse abandonment email or SMS that speaks to what the visitor was actually looking at, not a generic "you left something behind" message.

Most browse abandonment recovery today operates on a relatively simple trigger. The visitor viewed a product and left. Intent signals could make that logic more nuanced, and the message that follows more relevant to where the visitor actually was in their decision.

How to read bounce rate differently

Bounce rate, as a metric, doesn't tell you very much on its own. It tells you someone left. It doesn't tell you why, which page type to prioritise, or whether the bounce represents a genuine commercial loss or a session that was never going to convert.

On-site intent signals can't answer all of those questions. But they might answer more of them than page speed tests and navigation audits typically do. For eCommerce teams who've already done the basics and are still looking for what to try next, it's worth examining what visitors are doing before they leave, not just that they left.

That shift in framing, from "how do we stop bounces?" to "what are bounces telling us?", might be where the more useful work sits.

If you want to see how Made With Intent reads on-site intent signals across eCommerce traffic, book a demo and we'll walk through it with your site.

Visual Editor has just shipped. It's a brand new feature for Made With Intent.

Yes, we know this isn't something groundbreaking, and you've used something like Visual Editor before, with your favourite tools, but this is one of the most requested features from our customers.

It lets you create and preview on-site changes against your own live website. You'll have more flexibility and control over the kinds of experiences you'd like our tool to test and deliver to your customers.

It'll reduce the need for developers to get involved, and give you a familiar, visual way of editing experiences, directly within Made With Intent, that you can ship on your own.

So, that's the short version. Here's a bit more background on what it does, why we built it and a sneak preview of what's coming next.

What Visual Editor does

Visual Editor lets you create and preview on-site changes against your own live website, without developer support and without guessing how something will look once it's live.

It solves a familiar problem. The person responsible for the site experience is rarely the person who can build it. As Ryan Jordan, our CPO, puts it:

"The people that are using our product are generally those who are responsible for the site experience but not necessarily technically able to always build for the site experience."

Here's a list of of some of things you'll be able to do with this new feature:

• Change a banner
• Update, restyle, hide or show a specific element on a page
• Fully customisable overlays, built to cater for every moment 
• Build and preview components on your live site before they go anywhere near a visitor.

You'll have, no doubt, used lots and lots of WYSIWYG tools before in your favourite A/B testing, experience and ecommerce tools. We've deliberately designed Visual Editor to be familiar, and work similar to the products you know and love, so you'll find it intuitive to use.

We built Visual Editor because it is the feature that was most requested by our customers. We feel it's a natural evolution of our well-loved template library. With the ability to essentially edit your site by clicking and typing, we think it'll be flexible enough for non-technical people to pick it up and build something quick and dirty.

But this new feature will also let you see the context of what you've built in-situ, perfect for your site and your context, instead of leaving it to imagination.

But Ryan reckons you'll go further: "Previously, campaigns delivered with Made With Intent were generally thought about from a \"template\" first approach, but the Visual Editor now allows you to think about how real-time moments of intent can change the page and its content"

Moving towards a goal-orientated mindset

We reckon our Visual Editor will help you change your mindset. Most experience delivery tools tell you to pick a format first. You decide you want a pop-up, then you go and fill it with content. You decide you want a sticky banner, then you fill that with content. The format leads, and the goal follows.

But that's backwards for a lot of what teams are trying to do.

Let's take basket abandonment. One brand might come in knowing they want a pop-up. Fine, build the pop-up, fill it with content. But another brand comes in knowing only that they want to offer a 20% discount when someone's about to leave. They've got the goal. Why should we dictate how you deliver that discount?

So we've built Visual Editor to work from either end. Start with a format and add your content. Or start with your content: the discount, the message, the countdown timer. Then see how it looks as a sticky banner, a pop-up, a slide-in, or an in-page element.

"Most of the market always just goes format-into-content," Ryan explained. "What we're doing by flipping it and being able to go content-into-format is giving people the ability to play around."

On paper this might seem like a small thing. But we think this will give you different options to explore different solutions to the same problems you've been optimising and experimenting with for years. You'll be inspired to create different solutions to the same problems you've been having for years.

A quick note on CSP

Some sites run Content Security Policy (CSP) rules that can stop a visual editor from working on the page. To help with that, we're rolling out a Chrome extension called Intent Studio that unblocks it.

If your site's CSP rules mean you can't use the editor on the page yet, nothing else changes, everything you could do yesterday, you can still do today. For sites that don't have CSP rules, you should be fine without Intent Studio.

So, what's coming next?

We'll be adding more templates. We've built Visual Editor to be flexible deliberately, so people get used to it and start asking "can it do this, can it do that," we'll keep adding to what's there.

Longer term, this is where things get a bit interesting. We're finding with tools like Claude Design, many, many people are designing experiences using prompts, instead of fiddling around manually.

Ryan says: "It's now no longer click on an element and change the background colour to blue. It's tell an AI, make this background blue, and it kind of just does it for you."

The widgets behind Visual Editor have been built so that AI can understand them and make changes to them. This is the groundwork for letting you describe the change you want and have it built for you.

There's no hard date on when this is coming, but Ryan said it's a matter of weeks, not quarters.

Made With Intent has always been about one thing: enabling you to respond to real-time intent on your ecommerce site. Everything we do is in service of making that easier and more effective.

For a long time, acting on intent meant working within the formats we gave you or dev resource. Visual Editor changes that. It lowers the barrier between knowing what you want a visitor to experience and actually building it — without waiting on someone else to do it for you.

The gap between who owns the site experience and who can build it just got a lot smaller.

And the next step, simply describing the change instead of building it manually, is next.

Login to Made With Intent to see Visual Editor in action. If you're not a customer yet, and are curious, why don't you book a demo?

MandM, a British online fashion retailer, captured 88% more email signups from their popups. They didn't rewrite the copy. They didn't redesign their site. They just changed when their message appeared.

How did they manage this? Simply, they made the switch from rule-based experience delivery, to an intent-based approach.

And the results across email capture and product recommendations tell a consistent story: rule-based experience delivery forces a single answer on a question that has many right answers.

Rigid, predefined rules can't answer those questions. Intent signals can. Improving the impact of your onsite experiences is all about sending the right message, at the right time to your customers. We'll show you how Ollie Wilson, Insights Activation Manager at MandM does this with Made With Intent.

Editor's note: This blog post is a write up based on our first Intent Live: How MandM maximise revenue per user with personalised experiences. The session was hosted by Ollie Wilson, Insights Activation Manager at MandM. He showed how Made With Intent helped deliver better, more appropriate experiences to his customers, getting 88% more email signups.

The problem with rules-based personalisation

Most eCommerce personalisation sits on top of a set of rules. A customer views a Product Landing Page (PLP), then two Product Display Pages (PDPs), then gets hit with an email capture popup.

Or they get served "last viewed" recommendations based on browsing history. Or a basket abandonment email fires after 10 minutes of leaving the site.

These rules work to a point. Delivering the same experiences to every visitor using predefined rules, based on what they've done before gives you a critical foundation, but it puts a ceiling on growth.

They treat the journey as a sequence rather than a state. And a customer's state when they trigger your rules can be completely different depending on who they are, why they're there, and what they're about to do.

MandM saw this clearly in their email capture data. Their rule-based popup was capturing emails, but they were seeing broken journeys. The pop-up was technically firing at the right moment in the sequence. It wasn't firing at the right moment for the person and their intent.

As Ollie Wilson, Insights Activation Manager at MandM, puts it:

"It's not necessarily specific things a customer does in the journey. It's more so the timing and intent really helped us leverage this in a more efficient way."

The argument we're making here is that it's key to make this distinction. Rules track what a customer has done. The moment for you to intervene has gone. Intent-based experience delivery is issued in real time, predicts what they're about to do, and allows you to take appropriate action.

Pop ups delivered at the right time

The email capture popup is one of the highest-value tools in eCommerce, but also one of the most frequently misused. Use it too early and you interrupt a customer who hasn't found a reason to stay yet. Fire it according to a fixed rule and you'll hit some customers at the peak of their interest, but most others at exactly the wrong moment.

Ollie and his team tested a different approach. Instead of triggering the popup after a visitor hit a fixed sequence of pages, they introduced intent signals to identify when a customer was building meaningful engagement.

When those signals crossed a threshold, the popup fired. Exactly at the moment the customer was most receptive.

When talking about this, Ollie said: "We were hitting them at the right time because we knew they were building intent. They were right at the peak of their journey. Whereas before we were very much relying on this rule-based system which potentially wasn't the right time."

The results across three metrics tell the story. And these figures are lifted directly from the numbers Ollie shared during Intent Live:

- 55% increase in email sign-up rate, the rate at which people served the popup chose to subscribe

- 88% uplift in total emails captured, the volume consequence of that improved rate

- 15% resubscription rate among previously unsubscribed customers

That last number is particularly significant. Lapsed customers don't re-subscribe because of a well-timed popup by accident. They re-subscribe because they were caught at a moment of genuine brand or product affinity, at the right time. This simply isn't possible with rules-based personalisation.

What MandM actually changed

What's actually surprising is how little MandM had to do to arrive at these results.

They were already using Bloomreach for their on-site experiences, including the consent popup. The popup itself, design, copy, offer, stayed exactly the same.

On changes, Ollie says: "To be honest, it was so easy. We already had our consent popup in Bloomreach as a web layer. It was just a trigger we had to change. With Made with Intent's integration being so easy, we could just feed all of the data into Bloomreach and then use that as the trigger for the popup rather than those stringent rule bases."

Agentic campaigns: testing 157 segment combinations at once

The email capture result came from MandM's first phase of work with intent signals. Their second phase went further, introducing a different kind of challenge.

MandM runs an active personalisation testing programme. At any given point, they're running recommendation strategy tests: last viewed versus category affinity versus the Bloomreach Loomi engine versus most popular, and so on.

Each test runs for roughly two weeks, produces results for a specific segment or device type, and then the cycle starts again.

The problem isn't that the testing doesn't work. It's that it's slow. Each test answers one question, for one segment, in one context. And by the time you've worked through a few cycles, the results of the first test may not apply to the next season, the next acquisition cohort, or mobile versus desktop. And not to mention how resource intensive this all is.

Agentic campaigns changed this by running multiple recommendation strategies in parallel, doing the segmentation work automatically.

MandM tested five homepage recommendation strategies simultaneously. Rather than splitting traffic across two variants and waiting two weeks per test, Made With Intent's optimisation agent tested all five, across 157 unique segment combinations, and allocated each visitor to the strategy most likely to drive Ollie's defined commercial goal; revenue per user.

The result was a 2% increase in revenue per user. But the more valuable output was just how granular MandM could get. Not just "strategy X wins." For MandM it was strategy X wins for loyal mobile customers, strategy Y wins for new desktop visitors, and for your most engaged customers, showing recommendations at all might be the wrong call.

Let that sink in: showing recommendations at all might be the wrong call.

On the PDP, where new customers arriving from paid search land and recommendations have some of the most direct commercial impact, MandM ran a similar test with five strategies including a "hidden" variant (no recommendations shown at all). The result: 4% increase in revenue per user, from 130 unique segment combinations tested.

On the webinar, Colin Spooner, Principal Value Consultant, at Made With Intent, describes what this looks like inside the platform:

"There's kind of no winners or losers anymore. It just is the best experience to give that visitor at the right time."

Sometimes, doing nothing is the best strategy

In MandM's PDP test, 14% of visitors were allocated to seeing no recommendations at all, and for that segment, it was the highest-performing option.

That segment, as Ollie describes it, is your most loyal customers. The ones who know the site, know what they want, and don't need or want a carousel of "You might also like" items interrupting their path to purchase.

Ollie says: "For a certain subset of customers, your very loyal customers, the ones that know the site, they know what they want, removing recommendations is actually beneficial. Sometimes it can be a bit of a loop for a customer."

The PDP recommendation loop is a real issue. A customer lands on a PDP, clicks a recommendation to another PDP, clicks another, and ends up in a browse abandonment spiral that started as a purchase intent session. It's almost like you're giving them too much to navigate through.

Removing the recommendations breaks the loop and lets the customer do what they came to do.

This can be uncomfortable for personalisation teams to hear whose KPI is coverage, ensuring every visitor gets served something. But it reflects a more mature way of thinking about personalisation: not "show more" but "show what's right, when it's right."

For some customers in some moments, the right thing is nothing.

What MandM's results point to

MandM's results across two distinct experiments expose the same fundamental flaw in how most onsite experiences are built. They're designed to give every customer an answer at predefined moments, when real impact is about giving each customer the right answer for their specific moment.

Rule-based email capture fires at step three of the journey regardless of whether the customer is engaged or about to leave. Sequential recommendation testing finds one winning strategy for one segment, then starts over.

Ollie used intent signals and agentic campaigns both push against that. One changes when you fire an experience based on real-time behavioural signals. The other changes what you show based on continuous, parallel testing across hundreds of segment combinations. The outcome, in both cases, is the same — fewer experiences wasted on the wrong customer in the wrong moment.

For MandM, the next step is applying agentic testing to placement-level messaging. So, buy now pay later, delivery propositions, app downloads, and letting our agent match messages to customer segments in real time across the same placements.

If you enjoyed this blog post, why don't you watch our Intent Live series over on YouTube? Failing that, we're going to be running these sessions frequently, so you can sign up here for our next session.

While we're doing CTAs, here's another one: If this has piqued your interest, why don't you book a demo with our team?