Abandoned cart emails are a CRM staple, but many are blunt, reactive and easily gamed. Here’s why smart CRM teams should rethink abandonment with smarter timing and real-time intent.
Abandoned cart emails are often seen as the gold standard for CRM success. They’re easy to set up, look great in reports, and are widely viewed as a no-brainer for driving conversions. But here’s the uncomfortable truth: they’re not the silver bullet we’ve been treating them as.
Most retailers rely on them as a core tactic. Yet the reality is they only reach a small fraction of abandoners. These are the people you’ve identified and secured permission to email. Even when these emails land in a shopper’s inbox, the moment has often passed. It’s like walking out of a store and having the assistant chase you down the high street an hour later. That window to influence the decision has already closed.
To make matters worse, customers have learned how to game the system. Many now abandon carts deliberately to trigger a discount code. According to our èƵ Gap research, 83% of online shoppers have used a discount code even when they were ready to pay full price. That’s margin erosion, but also proof that current approaches are blunt, and shoppers know how to exploit them.
It’s time to rethink how we handle abandonment. And it starts with the emails themselves.
The status quo: Abandonment emails as the default fix
Abandoned cart emails feel like an easy win: a shopper adds something to their cart, leaves, and a templated flow comes to the rescue. Subject lines like “Forgot something?” or “Your cart misses you” flood inboxes, often paired with a discount to lure the customer back.
On the surface, these campaigns perform well. High open rates. Strong click-throughs. Solid ROI. But let’s not kid ourselves: those metrics don’t tell the whole story.
Limited reach: Only a fraction of abandoners are identifiable and contactable.
Delayed timing: By the time the email lands, the shopper’s attention has moved on. Or worse, they’ve bought from a competitor.
Added friction: Unless you’ve captured an email address and marketing consent, most visitors are already out of reach.
Predictable patterns: Shoppers now anticipate these emails and wait for discounts.
Generic messaging: Emails rarely account for why someone abandoned in the first place.
If we’re honest, these emails are less of a personalised recovery tactic and more of a reactive safety net. And safety nets don’t work for everyone.
The Problem: Why they fall short
There’s no denying abandoned cart emails deliver some results. But they’re flawed:
Low impact at scale: Most shoppers won’t even see one. No email means no campaign.
Lack of context: “You left something behind” doesn’t consider intent. Were they comparing prices? Still browsing? Waiting for payday?
Delay kills momentum: The longer you wait, the colder the lead gets. What felt relevant in the moment quickly becomes noise.
Margin drain: Blanket discounts train customers to delay purchases and wait for incentives.
These emails aren’t inherently bad. But in their current form, they’re blunt and reactive. They’re also increasingly easy for shoppers to tune out or exploit.
The Reframe: Fix the email, then think bigger
We don’t need to throw out abandoned cart emails. But we do need to evolve them.
Start by making them smarter:
Segment for context: A high-intent abandoner may only need reassurance. A low-intent visitor might require education or a compelling USP.
Time with care: Not every shopper needs a follow-up within an hour. Some need space.
Rethink the content: Shift from discount-first to value-first messaging. Highlight free returns, flexible payments, or social proof instead.
This isn’t theoretical. One UK high-street jeweller used intent data to personalise abandonment emails, tailoring content and timing to match each visitor’s mindset. 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 story here.
But even the smartest emails have their limits. If we know when and why a shopper is about to abandon, why wait until they’ve left to act?
Every abandonment email is, by definition, too late. The shopper has already gone. That’s why leading retailers are complementing smarter emails with in-session interventions.
With real-time intent data, you can:
Detect when a shopper is hesitating in the cart.
Surface supportive messaging before they leave (e.g., save-for-later prompts or delivery reassurance).
Reserve discounts for visitors showing exit signals, rather than everyone.
This approach doesn’t just recover abandoners; it prevents abandonment in the first place. And because interventions happen in the moment, they feel like help rather than a hard sell.
Future Vision: Abandonment reimagined
Abandoned cart emails still have their place. But they’re no longer enough on their own.
The smarter play combines:
Smarter emails: Contextual, well-timed, and less reliant on discounts.
In-session interventions: Adaptive experiences that engage all abandoners, not just the small percentage you can email.
It’s a shift from generic flows to contextual journeys. From chasing abandoners to understanding them. From reactive tactics to proactive engagement.
And when you get this right, abandonment isn’t just reduced. It’s transformed.
Abandoned cart emails are often seen as the gold standard for CRM success. They’re easy to set up, look great in reports, and are widely viewed as a no-brainer for driving conversions. But here’s the uncomfortable truth: they’re not the silver bullet we’ve been treating them as.
Most retailers rely on them as a core tactic. Yet the reality is they only reach a small fraction of abandoners. These are the people you’ve identified and secured permission to email. Even when these emails land in a shopper’s inbox, the moment has often passed. It’s like walking out of a store and having the assistant chase you down the high street an hour later. That window to influence the decision has already closed.
To make matters worse, customers have learned how to game the system. Many now abandon carts deliberately to trigger a discount code. According to our èƵ Gap research, 83% of online shoppers have used a discount code even when they were ready to pay full price. That’s margin erosion, but also proof that current approaches are blunt, and shoppers know how to exploit them.
It’s time to rethink how we handle abandonment. And it starts with the emails themselves.
The status quo: Abandonment emails as the default fix
Abandoned cart emails feel like an easy win: a shopper adds something to their cart, leaves, and a templated flow comes to the rescue. Subject lines like “Forgot something?” or “Your cart misses you” flood inboxes, often paired with a discount to lure the customer back.
On the surface, these campaigns perform well. High open rates. Strong click-throughs. Solid ROI. But let’s not kid ourselves: those metrics don’t tell the whole story.
Limited reach: Only a fraction of abandoners are identifiable and contactable.
Delayed timing: By the time the email lands, the shopper’s attention has moved on. Or worse, they’ve bought from a competitor.
Added friction: Unless you’ve captured an email address and marketing consent, most visitors are already out of reach.
Predictable patterns: Shoppers now anticipate these emails and wait for discounts.
Generic messaging: Emails rarely account for why someone abandoned in the first place.
If we’re honest, these emails are less of a personalised recovery tactic and more of a reactive safety net. And safety nets don’t work for everyone.
The Problem: Why they fall short
There’s no denying abandoned cart emails deliver some results. But they’re flawed:
Low impact at scale: Most shoppers won’t even see one. No email means no campaign.
Lack of context: “You left something behind” doesn’t consider intent. Were they comparing prices? Still browsing? Waiting for payday?
Delay kills momentum: The longer you wait, the colder the lead gets. What felt relevant in the moment quickly becomes noise.
Margin drain: Blanket discounts train customers to delay purchases and wait for incentives.
These emails aren’t inherently bad. But in their current form, they’re blunt and reactive. They’re also increasingly easy for shoppers to tune out or exploit.
The Reframe: Fix the email, then think bigger
We don’t need to throw out abandoned cart emails. But we do need to evolve them.
Start by making them smarter:
Segment for context: A high-intent abandoner may only need reassurance. A low-intent visitor might require education or a compelling USP.
Time with care: Not every shopper needs a follow-up within an hour. Some need space.
Rethink the content: Shift from discount-first to value-first messaging. Highlight free returns, flexible payments, or social proof instead.
This isn’t theoretical. One UK high-street jeweller used intent data to personalise abandonment emails, tailoring content and timing to match each visitor’s mindset. 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 story here.
But even the smartest emails have their limits. If we know when and why a shopper is about to abandon, why wait until they’ve left to act?
Every abandonment email is, by definition, too late. The shopper has already gone. That’s why leading retailers are complementing smarter emails with in-session interventions.
With real-time intent data, you can:
Detect when a shopper is hesitating in the cart.
Surface supportive messaging before they leave (e.g., save-for-later prompts or delivery reassurance).
Reserve discounts for visitors showing exit signals, rather than everyone.
This approach doesn’t just recover abandoners; it prevents abandonment in the first place. And because interventions happen in the moment, they feel like help rather than a hard sell.
Future Vision: Abandonment reimagined
Abandoned cart emails still have their place. But they’re no longer enough on their own.
The smarter play combines:
Smarter emails: Contextual, well-timed, and less reliant on discounts.
In-session interventions: Adaptive experiences that engage all abandoners, not just the small percentage you can email.
It’s a shift from generic flows to contextual journeys. From chasing abandoners to understanding them. From reactive tactics to proactive engagement.
And when you get this right, abandonment isn’t just reduced. It’s transformed.
Made With èƵ is an on-site decision engine for eCommerce businesses. It reads people's buying intent in real time and allocates experiences to the visitors most likely to respond to them
is a digital experience platform. It does web and feature experimentation, rule-based personalisation, content recommendations. It's designed for teams running structured test-and-learn programmes at scale.
This blog post explains where the two platforms complement each other and when you would use both versus just one on its own. But if you're too impatient to read to the end, we've got you:
Made With èƵ and Optimizely serve different functions. Optimizely handles experimentation and rule-based personalisation.
Made With èƵ reads visitor intent in real time and decides who sees an experience and when. The two work really well together:
Optimizely executes what, Made with èƵ decides who and when. We serve your Optimizely experiences when it matters (the right time within their session) and to whom, all based on what really matters; their intent.
How Made With èƵ is different to Optimizely
1. Respond to users in real-time with experiences tailored to what their digital body language tells you
Optimizely's strongest targeting comes from rule-based audiences in Web Experimentation and Personalization, boolean logic on geo, device, behavioural events, URL targeting, and page Tags, plus optional machine learning (ML) layers:
Adaptive Audiences (interest categories inferred from content engagement)
Content Recommendations (NLP-driven topic affinity per visitor)
Optimizely Data Platform (ODP) real-time audiences
The Stats Engine inside experiments is genuinely best-in-class — sequential testing with always-valid p-values — but the targeting decision still asks the marketer to define which audience rule a visitor fits into.
Made With èƵ flips the model. It reads buying intent in real-time from the first pageview. Stage, signals, trends, purchase confidence, abandon risk, shopper mindset, and re-scores every three-five seconds.
Targeting is driven by what's happening now, in real-time, by a human, not by which rule-defined audience or topic interest a visitor has been mapped into. Nor by behaviour that's already happened. This means you can respond to the signals that sit between events and pageviews; what we call moments that matter.
You can read more about How Made With èƵ Works, and the moments that matter, by clicking the link.
2. Segment the right experience to your customers automatically, without fiddling with rule trees manually
Your team currently builds and maintains audiences in Optimizely's Audience Builder, Dynamic Customer Profiles, Adaptive Audiences, and ODP. All with decision rule trees, content-tagging taxonomies, attribute conditions, event conditions.
Even with contextual bandits reallocating traffic within a defined audience, the audience definition itself stays manual: design the rules, QA the segments, redesign as the catalogue and content evolve. These segments are website-attributes, too. Not human attributes, not intent based (the most human of all attributes). That's where personalisation really succeeds.
Made With èƵ replaces all of the above with continuous intent prediction and delivery.
Our agent decides who sees what, in real time, across hundreds of intent combinations. You'll focus on strategy, creative and proof, instead of tweaking and analysing rules or segments all the time.
The agent retrains daily, which means the experience compounds toward intent combinations where impact is seen and felt. In an always on state, always learning, always getting better.
Where Optimizely's multi-armed bandits (if used) allocate traffic across the variants of a single experiment to maximise one fixed metric within one defined audience, Made With èƵ allocates across hundreds of audiences (intent combinations) — adding the layer of to whom and when an experience should be served, and measuring it against a holdback rather than just exploiting the winner.
Made With èƵ and Optimizely: In depth
Here is how the two platforms compare on the things that matter most for eCommerce teams:
Dimension
Optimizely
Made With èƵ
Core job
DXP suite — web and feature experimentation, rule-based personalisation, content recommendations, ODP, plus CMS/Commerce in the wider platform.
Audience Builder (boolean logic on attributes/events/Tags), Adaptive Audiences (content-interest categories), ODP real-time audiences (~2 min pipeline latency).
Live multi-dimensional intent read per visitor — stage, signals, trends, purchase confidence, abandon risk, shopper mindset. Updates every 3–5 seconds.
How fast it responds
Stats Engine and contextual bandits reallocate within a single experiment — optimising one fixed primary metric, from a 100% exploration cold start, across pre-declared attributes. ODP audience pipeline ~2 min. Personalization works once an audience rule matches.
Continuous re-scoring every 3–5 seconds during a live session. No audience-rule prerequisite — model is cross-merchant trained on 50bn+ events.
How it measures impact
Stats Engine: sequential testing with always-valid p-values, mSPRT, FDR control, CUPED, guardrail metrics. Holdout groups supported but positioned as a feature, not a default.
Bayesian A/B with a holdback group on every experience by default. Reports incremental orders/revenue against a true no-intervention baseline.
Platform reach
Web (JS snippet), server-side (19+ language SDKs), mobile apps, Edge Workers, CMS-native (CMS 13).
Web only. Platform-agnostic via a single 7kb GTM tag. No PII. ISO 27001.
Team workload to run it
Steep learning curve per G2; “basic” visual editor, code editing in IDE then paste-back. Personalisation layered on Experimentation is repeatedly flagged as complex.
Light. 30–60 minute GTM install. Agentic campaigns reduce manual segmentation work after setup.
Where does Made With èƵ integrate with Optimizely?
We've broken this section down into three parts. We want to be honest about where you'll gain functionality by utilising Made With èƵ with Optimizely, where we augment it, and things we simply don't do, or Optimizely does better.
How Made With èƵ adds new functionality to Optimizely
Understand and act on every visitor (including anonymous) from the first pageview. Optimizely's Personalization is rule-driven. It works once a visitor matches an audience rule. Content Recommendations builds a per-visitor interest profile from content engagement, which strengthens as the session progresses.
Made With èƵ's model, which collates 50bn+ monthly events across 150+ retailers, reads continuous intent from pageview one, every few seconds. Anonymous, identified, first-time, returning. The opening moments of a session get the same intent read as the tenth pageview.
Automatically identify and serve the best experiences to people without guessing. Optimizely's contextual multi-armed bandit (CMAB, powered by Opal) is the closest thing in their stack, and it's genuinely good, so it's worth being precise about what it does.
A CMAB picks the best-performing variation for each visitor based on context (device, geo, behavioural history) to maximise one primary metric, within a single experiment.
Three design choices define it: the context attributes are declared up front and can't be added or removed once it starts (even paused); it optimises exclusively to a single primary metric fixed at launch; and it begins with a 100% exploration phase, randomly serving variations until it has gathered enough data before it shifts to exploiting the winner.
It's a smarter way to split traffic across the variations you built for the audience you defined.
But we'd like to go into detail on what that context is.
Device, geo and behavioural history are proxies for a person. They describe who a visitor appears to be, not what they want or how close they are to buying. They're arbitrary website attributes that correlate with conversion only loosely, and a bandit optimising over them is tuning against a weak signal.
èƵ — buying stage, momentum, hesitation, purchase confidence — is the proximate driver of what a visitor actually does next. The proxies describe identity; intent describes decision, and decision is what moves the metric. Optimising the allocation over the wrong variable caps how much a CMAB can ever find.
Rather than splitting traffic across the variations of one experiment to maximise one metric, it allocates across hundreds of intent combinations.
Diamonds Factory, a Made With èƵ customer, , where the 'context' is live, multi-dimensional intent (stage, signal, trend, purchase confidence, abandon risk, mindset) that updates every 3–5 seconds and is discovered by the agent, not enumerated by the team upfront. Learn more about basket abandonment here.
There's no per-experiment exploration tax, because the model is trained across 50bn+ events, 150+ retailers and live from page view one.
And because a bandit is built to shift traffic toward winners, it has no standing no-treatment baseline — Made With èƵ keeps a holdback on every experience, so it can answer "did this cause incremental orders," the question a metric-maximising bandit structurally can't.
Causal incrementality on every experience. Optimizely's Stats Engine reports variant lift with always-valid p-values; genuinely strong for variant comparison.
Made With èƵ runs a holdback group on every experience by default and reports incremental orders against a no-intervention baseline.
The difference is between "variant A beat variant B" and "this experience caused X orders that wouldn't have happened otherwise."
Where Made With èƵ improves Optimizely
These are things Optimizely does that get better with Made With èƵ on top:
Audience Builder and ODP audiences become intent-aware Instead of boolean rules on attributes and events, or content-engagement interest categories, Optimizely audiences can take Made With èƵ's live intent attributes and target on signals that actually predict conversion. ODP's segment builder picks these up as attribute conditions; Web Experimentation picks them up as Tags.
Personalization variants get the right routing Made With èƵ decides which Optimizely-created variant a visitor should see based on their current intent state, replacing rule-based audience routing. The marketer keeps the variant production; our agent handles the allocation.
Web Experimentation tests get causal lift on top of variant performance. Run Optimizely tests with Stats Engine as you do today. Add Made With èƵ's holdback measurement on the experience itself to answer "would users have purchased regardless of any variant?"
Content Recommendations recommend to live intent, not just topic affinity. Made With èƵ lets Content Recommendations reflect what's happening in this session, not just historical topic engagement. Particularly useful for anonymous visitors where you’re not sure what their behaviour is telling you.
What Made With èƵ doesn't do
Feature flagging and server-side experimentation. Optimizely Feature Experimentation (SDKs in 19+ languages, Edge Workers, Agent microservice) is a category leader. We don’t offer anything here.
Sequential testing methodology for variant comparison. The Stats Engine's mSPRT-based always-valid p-values are best-in-class for inferring variant winners under continuous monitoring. Made With èƵ uses Bayesian A/B with holdback.
CMS-native content personalisation across the wider DXP. Optimizely Content Cloud, Content Marketing Platform and the broader DXP integration are all things that are not within Made With èƵ's scope.
Made With èƵ isn’t going to change your recommendations process Optimizely's NLP-driven content recommendations and ecommerce product recommendations are well-established. Made With èƵ adds an intent layer; we don’t replace what’s powering your recommendations process.
Mobile app personalisation. Optimizely Feature Experimentation has native SDKs for iOS, Android, React Native. Made With èƵ is web-first.
Edge experimentation. Optimizely's Edge Worker integrations (Cloudflare, Akamai, Fastly) sit outside Made With èƵ's use case
How to get started with Optimizely and Made With èƵ
Use Made With èƵ to create intent-ready audiences in Optimizely: Pass Made With èƵ's live intent attributes — purchase confidence, abandon risk, buying stage, intent trend — into ODP as attribute conditions, or into Web Experimentation as Tags.
Build audiences like "high purchase confidence + declining intent trend" (needs reassurance), "medium confidence + high abandon risk" (needs timely intervention), "low confidence + active comparison signals" (needs guidance, not a discount).
Execute experiences in Optimizely using those audiences. Use Web Experimentation and Personalization for what they do well, so things like variant production, Stats Engine analysis, content variations across the DXP.
Serve the experience (or a variant) through Made With èƵ's decision agent, where the experience is served based on visitor intent rather than rule-defined audiences.
Use Made With èƵ to prove the incrementality of "Optimizely + intent". Holdback groups on top of the Optimizely experience answer the CFO question: did this cause incremental orders, or did we just personalise for visitors who would have bought anyway? Particularly valuable for discounting — Made With èƵ surfaces which high-intent visitors needed no incentive.
Neve Jewels Group, the luxury jewellery brands Austen Blake and Sacet, moved from universal promotions to intent-level targeting across their basket abandonment strategy.
Rather than applying the same discount to every abandoning visitor, Made With èƵ segmented interventions across four intent levels, targeting what Director of Customer Experience Jo Homer described as "the nudge moment."
The result: 13% conversion uplift on basket abandonment, and £2.4m in annual revenue uplift overall, 4.8x the original business case, paid back within a single experience. You can learn more here.
What sort of businesses work best with Made With èƵ?
This all depends on the size and structure of your eCommerce operation.
Mid-market retailers (£20m–£100m online revenue)
Made With èƵ often leads here. Optimizely's Intelligence Cloud commonly lands at £50–80k+/year for this band, based on and procurement data. Made With èƵ's session-based pricing, 30 to 60 minute install, and agentic layer fit eCommerce teams of two to ten people who need to ship and prove things quickly.
If you are running Optimizely for the Stats Engine and feature experimentation and those are load-bearing, keep them. Add Made With èƵ for intent targeting and causal measurement of your on-site experiences.
Enterprise retailers with dedicated personalisation teams
Both, with clear role separation. Optimizely for the experimentation surface, Stats Engine credibility, server-side feature flagging, and DXP integration. Made With èƵ for on-site intent-driven decisioning and causal measurement.
Try Made With èƵ today
There you have it. Made With èƵ and Optimizely are designed to complement one-another, not compete with one another. Many of our clients use Optimizely to continue A/B testing, while using Made With èƵ to serve experience to customers on a 1:1 basis.
If you're interested in learning more about how Made With èƵ works, and how you can use it in your tool stack, book a demo here.
Disclaimer:This comparison is based on publicly available information from Optimizely's documentation, marketing site, and customer reviews as of July 2026. Both products evolve continuously. If anything looks out of date, get in touch.
Editor's note: this article is adapted from a recent èƵ Live session with Jack Simkins, Digital Product Manager at Golfbreaks.com.
A test that lifted time on site by 14% still spent its first week looking like a failure. Conversion rate was down. Under a traditional A/B testing programme, Golfbreaks.com would have pulled the experiment.
They didn't. And the reason why says a lot about what happens when a genuinely high-consideration purchase journey meets a testing programme built for one-session eCommerce.
is a golf tour operator based in Windsor, with offices in Copenhagen and Charleston, though the Made with èƵ account focuses on the US and UK. It sends golfers on trips ranging from a single night in the UK to a week in Spain or Portugal, across a lot of different golfer segments. It's a lead generation business first. Visitors don't check out online in one sitting, they enquire, then a sales agent works out flights, transfers, accommodation, and course access, and gets them to a booking over the phone, sometimes weeks later.
That's not unusual for travel, where research and comparison typically happen across several separate visits and sites before a decision gets made. Optimising a single-session conversion rate for a purchase that actually plays out over weeks measures the wrong moment entirely.
A booking journey that can't be forced into one session
Jack has spent seven years at Golfbreaks.com, the last couple focused on conversion rate optimisation. "We've got quite a unique scenario whereby we're trying to encourage that inquiry," he said. "Particularly in travel, in the industry in general, it's quite an unusual thing to not be able to book entirely online."
A small portionof trips get booked online. But most go through a sales agent, because a golf trip has too many moving parts (courses, transfers, flights, accommodation, and group logistics) for most visitors to configure and commit to in one sitting.
Colin Spooner, Principal Value Consultant at Made with èƵ, put his finger on why that matters: "It's not our traditional eCommerce brand where it's a pure purchase journey. But that almost plays into the hands of intent, where you need to think about that considered purchase and how to get people through the funnel before even thinking about the booking, weeks and months down the line."
Measuring what happens before conversion
"You are what you measure" is a phrase the Golfbreaks.com team has adopted internally. If a new visitor is unlikely to enquire on their first visit, optimising purely for enquiry rate on that visit measures the wrong thing.
So alongside enquiry rate, the team tracks bounce rate and time on site together (a new visitor who bounces immediately clearly hasn't been given a reason to stay), pages viewed per session as a depth-of-exploration signal, and, specifically, movement from low to building intent, the kind of signals behind Made with èƵ's content prioritisation and messaging use cases. None of these are vanity metrics here. They're proxies for whether a visitor is progressing through a decision process that runs across several sessions, not on a single visit.
Before building any experience, the team asks these questions to get in their customers shoes:
What is a brand-new visitor actually trying to work out?
Who are Golfbreaks.com?
Can we be trusted?
Do you have to pay full price up front?
Can you book online at all?
That last one is really important. Because Golfbreaks.com can't be booked entirely online, setting that expectation early avoids disappointment later in the funnel, right when a visitor is closest to converting. As Colin put it, getting that messaging right up front was "a huge realisation" for how the whole experience needed to be built.
What agentic campaigns change about testing
Golfbreaks.com's testing programme runs on Made with èƵ's agentic campaigns. In standard A/B testing, you decide up front which segment sees which variant, based on a hypothesis about who will respond to what. Agentic campaigns invert that: you define the strategy (the moment you're trying to influence, and the goal, whether that's enquiries, conversions, or a secondary metric) and hand the agent your set of tactics. It tests them against real segments and works out which one performs best, for whom, and when, using the same intent signals that power the rest of the platform.
For Jack, a self-described non-developer, the practical benefit was speed. "The tool allows me to get these experiences up much faster," he said. "My concept-to-live process is significantly shorter... it means the agents have got time to learn."
But the deeper change is what gets removed. "No longer am I having to set up those individual segments, or serve experiences to segments that I think will benefit from them," he said. "It's in the hands of the agent to then work out what segments it would benefit from... It's a much wider net." A message built for low-intent visitors might also help a segment already building toward a decision. A manual test is only as good as the human guess behind who it's shown to. An agent testing against a hundred segments simultaneously doesn't have that blind spot.
The "do nothing" variant is a genuine conversion tool
One of the more counter-intuitive parts of Golfbreaks.com's setup is what Jack calls the "do nothing" variant. In standard A/B testing, every visitor sees a control or one of several variants. Agentic campaigns add an option where the visitor sees nothing added or changed at all.
"The do nothing essentially sits within those variants as a copy of the control," Jack explained. "It's a safety net because it prevents us from showing negative experiences to customers that don't need to see it."
"In most experience it's always about adding things onto your site," Colin observed. "Having a version where actually sometimes the best thing is leaving the customer alone to progress, or even suppressing things on site, is a nice alternative to what we've experienced over the last 10, 20 years in experimentation." As the data below shows, it's frequently the top performer, because some visitors don't need an intervention. They're already progressing on their own, or they arrived with enough context that added messaging just gets in the way.
When the agent's early data looks wrong
Here's where the seven-day lesson from the top of this article comes back in. Jack's team built two welcome-visit experiences using the same messaging, one for the homepage, one for a location page such as a product discovery landing point for someone who searched "golf breaks in England."
The homepage experience delivered a 14% uplift in time on site. But conversion rate showed a negative trend for the first seven days. "With traditional AB testing, I would have perhaps turned it off," Jack said. "I would have panicked when I saw negative 14%, and I would have said, this isn't working."
He didn't, because the agent was still learning. That's consistent with what independent testing research shows more broadly: . Once the agent had enough data, performance turned around.
Same messaging, two different visitors
The location-page test surfaced something else: identical messaging performed in opposite ways depending on where a visitor arrived. On the homepage, a "how to book guide" message performed best, evidence of a genuinely low-intent visitor who needs some hand-holding.
On the location page, the top performer was a trust-building "number one tour operator" message and the do-nothing variant. Jack's take is that a visitor who searched "golf breaks in England" already has affinity toward the destination, closer to a returning visit mindset than a cold product visit. They don't need the basics explained.
A message about Golfbreaks.com's customisable packages underperformed with brand-new homepage visitors. It's true and important, but it's the wrong message at the wrong moment, the same lesson behind Made with èƵ's discounting use case: showing a message before a visitor is ready for it does more harm than good.
The surprise that only showed up in the data
Asked what surprised him most, Jack pointed to something that had been sitting in plain sight. An early "ready to plan?" message aimed at brand-new visitors looked like a reasonable nudge. But in the data, it actually came across as overbearing.
"If you think about a new user landing on the site and asking, are you ready to plan? It's probably a bit overbearing," Jack said. "At the time, when you're setting up those tests, it's like, right, I'm going to use the same messages for the homepage, same message for the location page. They're surely going to work." They didn't, for every segment.
Colin's read: "The amount of times we see customers who have a predefined view of what will work, and it's completely different. That point around being subjective comes to life when you start to see the way the agent starts to make decisions." That tracks with the broader shift in shopper expectations, , a generic message is no longer neutral, it actively reads as a miss.
When a message doesn't resonate with any segment, the fix is simple. Delete it, let the agent relearn, and add a new tactic later if needed. You don't need to manually re-segment.
Measure the journey, not just the moment
None of this is unique to golf holidays. Any purchase with a real consideration cycle shares the same shape, including B2B software, where . A first-time visitor is rarely the same as a returning, further-along one, and treating them identically wastes the message on the visitor least ready to act on it.
Three things carry over regardless of your industry. You don't need to be a developer to find early wins, a visual editor is enough to start. If your purchase journey has any real consideration cycle in it, top-of-funnel testing should measure more than conversion. And build a tactic sheet of messages at a global level, then let the data show which ones resonate with which visitors, rather than deciding that yourself up front.
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 (, 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
èƵ 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. èƵ 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 èƵ reads on-site intent signals across eCommerce traffic, book a demo and we'll walk through it with your site.
June 30, 2026
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