Thought leadership

We're in 2026 and everything we still see on a website is static and generic. It's a single thing that's designed to meet everyone's needs and doesn't change.

Think about how crazy that sounds.

Here is the problem, stated as plainly as it can be.

A visitor loads a page and that page is fixed. It doesn't move. It doesn't change. Whatever happens over the next four minutes (and by the way, a great deal happens) the page has already made all the decisions it's going to make. It doesn't respond. It's as simple as that.

Which is a slightly silly way to run a shop, online or not, in today's day and age.

The alternative? Dynamic and individualised content.

And it's worth being precise about what that does and doesn't mean, because the phrase invites the wrong picture. This isn't about dynamic pages. Nobody wants components flying around mid-scroll, or a homepage that reassembles itself while you're reading it. It's about components. Messaging. A USP bar. A reassurance line. A recommendation strip. The small, movable furniture of a page, arranged for the person actually standing in front of it.

If that is both dynamic and suited towards the individual user, i.e. their needs at that particular time, then, ladies and gentlemen, that is personalisation: marketing's long-lost, unachievable treasure.

The problem with static and generic

From static-to-dynamic and from generic-to-individualised sound like one idea. Actually, they're two, and they're independent from each other. But combined they form the idea of what personalisation should be.

• Static to dynamic asks: does what I show change in time?

• Generic to individualised asks: does what I show change by person?

Put them together and you get four possibilities, where, rather humorously, almost everything built in the last decade sits in one of the first three.

That top-right box is where most personalisation programmes stopped, and it's why so many of them disappointed. A segment rule is sort-of-individualised. To a segment at least. That segment is usually:

1. rules-based (if user is in this segment, show this thing)

2. based on website attributes, a proxy for real intent, e.g., "users who land on PDP pages"

Neither of those two things is scalable, nor do they feel genuine. It's guesswork, assumptions up front. A genuine reason why the only form of personalisation we see nowadays is recommendations. A great example of dynamic content that's individualised to the user.

Most importantly, though, the reason why personalisation for an individual (that famed "121 personalisation" we heard so much about) didn't become the golden goose is because our websites don't respond. They're static entities. The decision is taken the instant the page renders, from whatever was knowable at that instant, and then it holds, no matter what the person does next.

Enter Made With Intent from stage left.

"So what?" I hear you ask. Besides being archaic, there are two problems with this approach.

1. The cost of generic is that averages hide losers.

Say you add social proof and it lifts conversion 5%. Good result, ship it. But inside that 5% are hidden segments: one group at −3%, another at −10%, another at −20%, all quietly subsidised by a bigger positive somewhere else. That happens because you served one message to everyone and assumed it landed on everyone the same way. It didn't. People arrive at different stages of a buying journey, holding very different levels of intent, with different jobs to be done.

The fix isn't a better aggregated message, but it's serving the same message to fewer, better-chosen people, which sometimes means not serving it at all, because for that person, at that moment, it isn't right. It's what we call "inappropriate" at Made With Intent.

Therefore the cost is not amplifying existing messaging or features.

2. The cost of static is that the decision gets made before the moment that mattered.

A page is not one moment. It's thousands. Someone lands on a product page with real intent and loses it without ever leaving. They scroll to the reviews, pause, open the size guide, back up to the images, sit still for ninety seconds, drift toward the browser bar, come back, re-read the delivery copy. Every one of those changes what they need next. Not one of them is a new page load. How many single page views do you have?

Decisions are made in between the pages. In between the events on the pages. The signals. That's where moments occur and hesitation lies, doesn't it? But our websites are so static they can't respond to those moments.

Therefore the cost is missed opportunity.

What actually moves each axis

1. You need a personal attribute

This is the part the industry got wrong for a decade. Personalisation was built on attributes like device, traffic source, returning versus new, pageview count. Those describe the circumstances someone arrived in. Spoiler alert: they aren't the person. They're website attributes disguised as aggregated proxies.

Pageview count is the clearest example of the problem. It's used as a proxy for engagement, when a confused shopper racks up far more pageviews than a decisive one. The proxy points the wrong way, and vice versa.

The most personal attribute available isn't who somebody appears to be based on their device, iOS, traffic source or what page they landed on. It's what they're trying to do: whether they're discovering or considering, confident or hesitating, likely to buy, likely to leave. Their intent.

That is a description of a person rather than a description of a session. There's a reason why personalisation is a noun person-alisation: the act of being personal.

2. You need a decision engine to scale delivery

A mechanism that serves content at the time that it's needed for the user. A delivery system that recognises that intent, serves content, re-recognises it, serves different content and evolves as such.

This means reading behaviour continuously rather than sampling it once at, say, page load. This is where you get to true two way communication between user and brand. In our case, at Made With Intent, that's around 800 signals per interaction, returned as predictions every few seconds. The decision travels with the visitor instead of being stamped at the door.

And it means something is allowed to keep re-deciding. Not a rule that fires once, but an allocation that gets revisited: which of your experiences is working, for whom, and when. Continuously, rather than as a verdict reached once and then defended. An agentic delivery system that responds at the time that it decides, on your guidance and guardrails, for true scalability.

These two things combined are what we do at Made With Intent and why we exist.

Examples

The shift sounds large. People often asked about the explosion of content that's required. In practice, it's usually the same asset you already own, just triggered differently.

Think about your email capture pop-up.

Nearly every site has one, and nearly every one fires on something arbitrary: one pageview, three pageviews, ten seconds. Static and generic. So a visitor arrives and is immediately interrupted by a request for their email, before they've been given a single reason to care.

With Intent: change nothing about the pop-up (the same design, same offer, same copy) and change only when it appears, to the moment each individual visitor is actually receptive. Dynamic and individualised. Submission rates tend to move sharply. Why? Because it's more appropriate at the time the user needs it.

Your USP bar.

Often three messages, shown to everybody, on every page, all of the time. Static and generic. The instinct is that more messages mean more coverage. We've all been there. "We must show this message because that's what users care about".

With Intent: focus creates impact: one message, chosen for the person, beats three competing for attention. Give the system the three you already have and let it decide which one this visitor needs, and which page, and after how long. Dynamic and individualised.

Your "You may also like" recommendations.

Without intent, it's static and generic, stuck at the bottom of the page serving everyone all of the time, assuming it impacts everyone the same.

With Intent: for example, to someone in a high-intent, ready-to-buy state, a strip of alternative products can be a distraction from the thing they'd already decided on. To someone in a low-intent, still-exploring state, it might be the most useful thing on the page and belong further up it. Dynamic and individualised. Let the agent decide who is what and let it serve that content.

Notice what none of these require. No new content. No redesign. No new journey. The content already exists. What changes is the who and the when — which is why this is a smaller programme of work than it sounds, and why it doesn't add much to anybody's workload. It's largely the same things the team was doing anyway, with different logic deciding how they're displayed.

Your personal shop assistant

There's an analogy we use at Made With Intent, a lot.

A good shop assistant doesn't decide how to help you based on which door you came through, or what you were wearing when you arrived. They watch. They listen. They read whether you're browsing or hunting, whether you look stuck, whether you're about to give up and leave. Then they respond, and if you clearly want to be left alone, they leave you alone.

They have intuition.

That's two-way communication, and it's what a static page can't do, because the page decided everything before you'd done anything. The ability to respond appropriately is where impact comes from.

Book a demo to see how Made With Intent can help you move from static and generic content to dynamic and individualised experiences.

"The website is good, it probably could be even better, no doubts. But it's "good enough". It does the job. It's stable. It's mobile optimised. It's got a good conversion rate relative to others and our expectations. What else can we do? Is it the right thing to do to have a team purely focused on conversion rate optimisation, or should we look more into product and trading? What even is the potential of our site?"

That was a thought-provoking quote direct from a prospect of ours in a recent sales call.

He's talking honestly about the idea of prioritisation and diminishing returns. A very well-known brand, decent infrastructure, good content, a site that works very well; probably even over-indexes on conversion rate efficiency if you're to compare it to competitors. He'd looked at the size of the remaining prize from making the digital experience better and quietly concluded it might be smaller than the prize from doing something else entirely.

This is a question of "where do you place your bets?"

I think they have reached a local maximum. For anyone who hasn't sat through the optimisation lecture: it's the top of a hill that isn't the top of the mountain. Every small step available from where you're standing leads downwards, so you stop climbing. Not necessarily because you've reached the highest point there is, but because you've reached the highest point reachable in small steps. Which is a fairly precise description of what a decade of testing does to an already-decent website. In other words, getting to a higher peak means changing direction.

He's asking the right question in my opinion and I think the honest answer is uncomfortable for most of the industry I've spent my entire career in, especially the purists.

This brand hasn't necessarily hit the limit of what's possible on their website. But instead has hit the ceiling of the average and anything further sees diminishing returns where the effort doesn't necessarily equate to the value. That, or he's potentially bored with the same-same solutions that are out there. Homogeneity is the killer of excitement.

I empathise. I got bored too.

I founded User Conversion; one of UK's most successful (read: largest?) independent conversion rate optimisation agencies. We did well, working with some of the biggest brand names the UK had to offer.

But like the above brand, over time, I grew more and more skeptical. A lot of our recommendations lacked creativity, they were all addressing similar problems with the same solutions. "Moving deck chairs on the Titanic" is what someone once put to me.

Conversion rate optimisation is a process of problem-solving with evidence based solutions. Learning, uncovering opportunities and problems, and fixing those problems; usually through AB testing (well, that's the outcome that most cared about; because it's sexy). And I'm not suggesting that the learning and the opportunities dissipate, but the solution often lacks impact because of the law of diminishing returns, their heterogeneity and the aggregated nature of them.

The evolution resolution beyond CRO

Conversion Rate Optimisation, for stakeholders at least, is a way to make the website earn more; and there are four ways to do that. Most of us have treated them as a maturity ladder. You graduate from one to the next, and the last one is the good one.

That's not quite right. It's less an evolution and I now see them more as levels of resolution where each one narrows the unit of decision.

Level one: conversion rate optimisation. The unit of decision is the average visitor. You look at where people struggle, you fix it, and the fix applies to everybody. This is genuinely valuable and I'd never argue otherwise but it is a) practically often an exercise in usability improvements and b) definitionally serving the mean.

The first statement encompasses this idea that the majority of solutions are things that don't change behaviour, they facilitate existing behaviour. Usability improvements. Small changes that ill-advised vendors purporting marketing promoting statistics have convinced us are worth the effort. We've all seen them. The famed 500% uplifts. Sticky add to cart buttons, adding trust signals under a call to action, that sort of thing. Read: deck chairs on the Titanic.

The second reinforces the statement that the mean doesn't exist. Instead, it is a continuously moving combination of different intent levels. Our own research found that, say, 10% of visitors sitting on checkout pages aren't ready to buy yet. Or that 34% of users never get past browsing, whatever page they happen to land on. There is no average shopper to optimise for, there are different jobs to be done. There's a distribution we've been flattening for twenty years because flattening it was the only thing we could do. And now we're used to that, we lack creativity of how to proceed.

Level two: experimentation. It's the same unit of decision, the average, but now you've proven it. This matters enormously and it's the most rigorous thing most organisations do.

From experience, it often comes in two flavours:

1. The immature version lives inside the CRO team or the marketing function, running client-side tests and it caps out at about four to six experiments a month. That ceiling is a resource limit, often not a statistical significant limit; people, build time, roadmap slots. You'll find here that you'll max out at a certain number of tests usually and your impact is limited to avoid cross-contamination of tests. Ever found yourself saying "we can't run a test on a PDP because we have something running there already?"

2. The more mature version is server-side experimentation, owned by engineering, decentralised across product teams, with testing built into the release process rather than bolted onto it. That version is genuinely near-limitless in cadence, and if you can get there you should; it's fantastic.

But note what even the mature version is for. It's designed to prove or disprove a claim about the population. One answer, for everybody, with confidence attached. That's the instrument working exactly as intended; but it's still an answer about the average. It's also an answer about the website, not the visitor (more on that later).

My prospect's line was exactly this: "For the years we have experimented, we did not see huge benefits. That's probably also what hindered further investment." I've heard that sentence in some form from almost every brand I've worked with.

Level three: personalisation. Here the unit of decision finally narrows to the segment. And here is where the industry has spent a decade making promises it couldn't keep. Unfortunately, to the extent where we now all hold PTSD; personalisation traumatic stress disorder.

Personalisation didn't fail (that's right, I said it failed) because it was a bad idea, every boardroom still talks about it to this day. Trust me, I literally wrote the book on it: The Person in Personalisation.

It failed because it doesn't scale, for two reasons, both of which compound.

1. First, you have to guess the segments before you have any evidence about which distinctions matter. Those pre-defined segments like "returning visitor," "paid traffic," "landed on a PDP" are website attributes, not people attributes. That's not person-alisation that's website-alisation, isn't it? Pageview count stands in for engagement, when a confused shopper racks up far more pageviews than a decisive one i.e. it's not true person-alisation.

2. Second, the arithmetic defeats you. Split traffic three ways and every test takes three times as long to reach significance. Try to prove the segments genuinely differ and you're chasing an interaction effect that needs roughly four times the sample again. A three-week test becomes a quarter-long project, and most teams call it early and ship an artefact, or just don't have the traffic (and therefore patience) i.e. it's not scalable.

So personalisation became a small number of hand-built manual rules, maintained by someone who'd rather be doing something else, delivering less than it promised. Ever wondered why recommendations was the only successful personalisation that brands have achieved? Because it's autonomous; in other words, scalable.

Level four: agentic delivery, with intent as the context. This is where we, Made with Intent, sit. The unit of decision becomes the person and what they're trying to do in the moment. Not the segment on retrospective data. Also, not the average. And critically, nobody writes the rule.

You give the system a strategy and a set of experiences that are already evidenced. It works out which of them suits which state of intent, person by person, at the time that it matters, and it keeps working it out. Nobody writes the rule.

Take one of our customers, Diamonds Factory who had a single basket-abandonment tactic: 25% off, to everybody. They gave the agent four options instead and let it choose between them. Most people, it turned out, didn't need the full discount to convert. And 15% needed no intervention at all.

That last number is the one that matters, because no level below four can produce it. A test has no vocabulary for show this to nobody as a good outcome. Not even the control of an experiment can show you that because a user is never bucketed into both the control and the treatment. A rule-based segment can't discover it. It only appears when something is allowed to decide, per person, whether to act at all, in the moment that it matters.

The Future of Personalisation

I think we treated this as four evolutionary components within a single ladder when, in fact, they're two.

• Levels one and two are about how sure you are. "Does this work." Think of this as 80% exploration, and 20% exploitation.

• Levels three and four are about how precisely you aim. "For whom does it work best, when should it be shown, and do a proportion of users even need it at all?" Think of this as 20% exploration, and 80% exploitation.

Conflating them is why so many personalisation programmes were run by people optimising for certainty, and why so many experimentation programmes never escaped one answer for everybody. Confidence and aim are different problems. You need both, and the tools for each are not the same tool.

The industry has worked out that continuous contextual allocation beats a fixed split. That's 50% of personalisation; serving "the right person, at the right time, with the right message".

The other 50% are the attributes that determine whether something is personal; and for us that's their intent. Their context. Their job to be done. The differentiator is what you put in the context window. Not a website attribute like device or location, because they describe who someone appears to be. Intent describes what they're about to do. One of those is a proxy and one of them is the thing itself.

What I'd actually tell my prospect

Not "invest more in CRO." And not "stop doing CRO," either, because each of these levels still holds a purpose and pretending otherwise is how vendors lose credibility. No, CRO is not dead.

But its purpose has changed. There's more.

Sure, if something is broken, fix it. If a step in the core journey confuses everyone like a login, a checkout, a filter that doesn't work, then everyone passes through it, the fix helps or hurts them all in the same direction, and the right answer is one answer. Optimising the average isn't a failure of ambition there. An experimentation partner of ours put it well: you don't stop the research, you don't stop the UX work, and if you don't have people designing properly for their users you're finished as a business regardless of what any agent does on top.

But once the site is good enough; once you've fixed what's broken and the remaining UX gains are genuinely marginal, I think the question changes. It stops being how do we make this better for everyone and becomes which of the things we already have should this particular person see, and when, and should they see anything at all.

Essentially the argument of diminishing returns. Unless your site is broken, terrible UX, or hard to navigate; the best bet is dynamic, trading-related experiences which capitalise on serving the right content at the right time. Sure I'm biased, but this is my arc within this industry over the past 15 years. I've seen what's possible and I'd like to share it with the world. A TLDR;

There are different ways to optimise, but just note that I've seen first hand that scalability is the biggest constraint to success. Not just that, but doing the same as everyone else, particularly "moving deck chairs on the Titanic" won't get you very far unless the baseline is so low.

The opportunity is vast. It amplifies existing experiences by autonomously serving those only to where the experience is best seen and best felt, excluding where it's not. Because no one in the organisation owns it, or because we are so accustomed to the way things work currently; the opportunity is still sitting there.

My prospect's instinct was right. He should probably move effort away from optimising the average. Just not away from the website.

If you're interested in how CRO is evolving, and want to learn more about intent-based personalisation, get in contact with our team here.

Ask most ecommerce teams how they know what a visitor cares about, and you'll hear the same answers. Last product viewed. Most time spent. Recent purchases.

These are proxies. Not signals. Not intent. Not interest.

And yet, this is how most of the industry claims to "know" what their customers are interested in.

The reality? Ecommerce has spent a decade optimising for what people click, not what they care about. The assumption that engagement equals interest is the core flaw. Affinities should be a core capability in ecommerce but they’re largely missing, and worse, often faked with inaccurate signals.

This article unpacks what affinities really are, why current methods fall short, and how Made With Intent’s approach reframes what personalisation should actually mean.

The illusion: Mistaking engagement for affinity

Most common drivers for determining product affinity strategies:

• Last viewed
• Most viewed
• Longest viewed
• Previously purchased

This leads to wildy inconsistent outcomes and is essentially guesswork. For example, see the following two sessions:

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[Session 1]
───────────────────────────────────────────────────────────────────────
Product A → Product B → Product C → Product D → Product E  
              ↑                      ↑          
        (Longest Viewed)     (Previously Purchased)    

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[Session 2]
───────────────────────────────────────────────────────────────────────
Product F → Product G → Product B → Product H → Product I
                            ↑                     ↑
                       (Most Viewed)        (Last Viewed)


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The problems with these interpretations:

1. Last viewed ≠ Highest intent: Product I was viewed last, but only once and briefly. It’s a poor indicator of interest or conversion potential.‍
2. Most viewed = Curiosity, not commitment: Product B’s repeated views may reflect uncertainty, not preference. It could also be a comparison reference or an accidental revisit.‍
3. Longest viewed can be misleading: Product C was dwelled on, but that could reflect confusion, poor UX, or open-tab idling, not genuine interest.‍
4. PreviouspPurchase ≠ future intent: Just because Product D was purchased before doesn’t mean the user wants it again. Relevance might now be low.

Ultimately, engagement is a misleading approach as it works in both directions and remains open to interpretation.

Yet this is how almost every ecommerce platform infers "what a customer cares about." Here’s why I think this fails:

• Recency bias fools the system. Someone can hate-scroll a product page and look "interested" when they aren't.
• No context of intent. Clicking or viewing does not equal liking. Hovering does not equal wanting.
• Secondary behaviour pollutes the data. A shopper adding toothpaste after buying a fragrance does not mean they love toothpaste.
• Teams aren't even aligned. CRM, paid media, and onsite teams all use different definitions of "interest," based on whichever proxy suits their tool or process.

And that’s the core of the issue. What most ecommerce teams call 'affinity' is nothing more than an engagement proxy. It’s recency. It’s frequency. It’s volume. But it’s not interest. And it’s definitely not intent.

To be fair, it’s not like the industry ever had this easy. Affinity has never really been an out-of-the-box capability for ecommerce teams.

You could try to cobble it together by blending last viewed, most viewed, time spent, but it meant building custom rules, manually interpreting engagement and hoping it told the right story. Most teams never had the tools to move beyond that.

And even when teams do try to build affinity models themselves, it rarely scales. Every time you want to understand affinity for a new attribute, whether it’s price, brand, category or anything, you’re forced to define rules, retrain models, or manually stitch data together.

The result? A fragile process that breaks the moment something changes. That’s why most teams default back to blunt proxies like recency. They’re simple and work ‘well enough', even if they’re wrong.

The low ceiling of engagement proxies

Let’s be clear. This stuff does work. Kind of.

Last viewed is better than nothing. Most viewed does something. This is why the industry keeps doing it.

But it's a ceiling, not a scalable solution.

It's effective, but not to the same degree. You're essentially marking your own homework.

The real opportunity isn't about fixing something broken. It's about lifting the ceiling entirely.

Less noise and cleaner signals. More precise targeting without over-discounting or over-messaging. Alignment across teams instead of different, conflicting definitions of “interest".

That’s why we define an affinity not just on what a visitor looked at, but on what contributed to their intent.

If a visitor browses three pairs of shoes at different price points, the traditional model might recommend the one they spent the most time on. Our model identifies which of those shoes actually built purchase intent. Because time spent isn't the same as value contributed.

Imagine visiting a health and beauty store. You spend five minutes looking at shampoo, toothpaste, and a razor. But the real reason you came in was for a fragrance, you checked that out first and decided quickly. Then you browsed around for other products.

The typical ecommerce system thinks you're deeply passionate about toothpaste. Ours knows the fragrance mattered most.

Why actual affinity data matters to online retailers

The real power here is prioritisation. When you use affinity based on contribution to intent, you stop drowning in noisy data. You can weight engagement by what actually mattered. What contributed. What moved someone forward. Not just what they clicked.

This isn’t just about more data. It’s about clarity. About knowing which signals matter—and which are just noise.

Getting this right isn't just about better product recommendations. It’s about:

• Cleaner data on what your visitors actually care about.
• More appropriate personalisation. Less irrelevant spam.
• Consistent messaging across CRM, onsite, and paid.

It’s also about unlocking higher-margin tactics. Look at how Seasalt Cornwall applied affinity data to drive an 89 percent conversion uplift with affinity-based discounts. Or how they increased conversion by 8 percent with homepage personalisation.

Both use cases speak to one truth: when you understand what people care about, you sell better. And you sell smarter.

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I believe in the (near) future, intent-based affinities will be the new baseline. A foundation for any business serious about personalising at scale.

When paired with real-time intent, it unlocks a fundamentally more appropriate, more effective way of serving visitors.

In five years, retailers will look back at recency-driven personalisation the way we now look at irrelevant banner ads or spammy pop-ups. Crude. Inappropriate. Obsolete. Affinity without intent will feel as outdated as demographic targeting does today.

If your tools can’t tell you what a visitor truly cares about, not just what they clicked, then you're flying blind.

If you're not using real-time affinity and intent signals, you're not personalising. You're approximating.

This is the next evolution of ecommerce. And it's already happening. Take a look at Made With Intent if you don’t believe me.

If you’ve read our original piece on Predictions, Not Proxies, you’ll already understand why ecommerce teams need to move beyond outdated signals like traffic source and funnel stage.

To recap, it’s time for a mindset shift in ecommerce. To go from using surface-level proxies to using real-time intent predictions based on actual behaviour. This follow-up brings data to back up that claim.

What is the quality of a visitor? What are their preferences? And are they progressing towards a purchase?

These are critical questions in ecommerce. They underpin everything from targeting and messaging to optimisation and conversion. And yet, most teams still rely on proxies to answer them. Proxies that are easy to measure, but misleading. Easy to action, but often off the mark.

Proxies became popular not because they were particularly predictive, but because they were easy. They were what was available. And in the absence of better tools, convenience often beat accuracy.

In this article, we explore three key areas where Intent-proxy metrics lead to incorrect assumptions about our visitors.

• Visitor Quality: Why source-based assumptions break down the deeper a visitor engages
• Visitor Preference: Why relying on add-to-cart ignores 6.6x more signals of interest
• Visitor Progression: Why real journeys aren’t linear and what that means for timing

It shows where proxies lead teams astray, what gets missed, and what becomes possible when you see the real story underneath.

Visitor Quality: The Proxy vs The Prediction

One of the most ingrained habits in ecommerce is defining visitor quality by how they arrive. PPC traffic is high intent. Social traffic is low intent. Mobile users convert worse. Returning visitors convert better.

These assumptions are so common they’ve become unquestioned. But they’re all based on aggregate averages. And averages flatten nuance.

When we analysed session-level intent across millions of visits, we saw a different story. Yes, there are differences at the top of the funnel. But as users engage more deeply, the source matters less. What matters is what they do now.

The difference between ‘high’ and ‘low’ quality traffic almost disappears when we look at visitors by their 46th event, rather than their 1st.

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Our data shows that the gap between “low” quality traffic and “high” quality traffic closes the deeper they engage. Due to lower intent visitors progressively dropping off over the course of a journey, the remaining visitors will have a naturally higher intent.

We looked at how quality changes over time. Early on, yes, traffic source matters. But by the 30th, 40th, 50th event, it flattens out. The intent is shaped more by what visitors do than where they came from.

Social traffic looks low intent at first glance, but the ones who engage actually build really strong purchase intent.

But knowing which visitors have potential is only half the story. To personalise effectively, you also need to understand what they actually care about.

Visitor Preference: The Proxy vs The Prediction

It’s easy to think we know what visitors want. Add-to-cart events, product views and recency are the typical signals we treat as indicators of preference. But they’re all blunt. They assume interest based on the most trackable action, not the most telling one.

When we analysed onsite behaviour, we saw that affinity builds well before someone clicks ‘add to cart’. And in many cases, people never reach that point, even when they’re highly interested.

In our data, we identify a product affinity in 6.6x more visitors than we see actually add to cart.

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Tracking the movement of a visitor’s intent to add to cart reliably indicates their affinities to products and attributes.

Only a small number of online shoppers add to cart, but many more show product interest through how they browse. Through scrolls, hesitations, returns and comparisons, we can see strong signals of affinity well before any CTA click.

In fact, we see product affinity in over 6 times more sessions than we see add-to-cart events. That’s a huge chunk of opportunity that goes unnoticed if you’re stuck with proxies.

Put another way, if you’re only reacting to add to cart events, you’re often too late to really influence what matters. You’ve missed the moment they started to care.

And once you understand what they want, there’s one final question: are they getting closer to buying, or drifting away?

Visitor Progression: The Proxy vs The Prediction

Most ecommerce sites still treat the typical page funnel as a reliable guide. Homepage to PLP to PDP to cart to checkout. And on paper, it works. But real journeys don’t follow that script. They loop. They stall. They rewind.

And yet, many personalisation and performance decisions still hinge on page depth. Someone in checkout must be ready to buy. Someone on PDP must be evaluating. Someone who’s viewed 10 pages must be high intent.

Not quite.

Intent declines in over 65% of journeys at some point. It’s the norm, not the exception.

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It’s very common for visitor journeys to fluctuate as they engage. This graph demonstrates that the rate of visitors that indicated a drop in intent to purchase at some point increases the longer they shop. On average, 65% of visitors will lose intent at some point, with converting visitors showing a clear divergence from the typical visitor.

We found that intent doesn’t just rise as sessions go on. It’s not that people always leave with less intent, but that there are points within most sessions where the intent dips. That fluctuation is what matters.

Even among converters, a good chunk of them show a dip somewhere mid-journey. So if you’re only acting on high-intent signals, you’re missing the nuance.

If ecommerce journeys are this non-linear and you want to optimise experiences as much as possible, then real-time prediction isn’t a luxury. It’s a necessity.

The Real Opportunity

The previous Predictions, Not Proxies article made the case for change. I hope this one validates it, and shows what happens when you make it.

The truth is, ecommerce teams aren’t misreading intent because they’re careless. They’re misreading it because proxies were the only thing available for a long time. They were measurable. They were familiar. And they made things feel predictable.

But customer behaviour isn’t predictable. Not through proxies. Not in the way we’d like it to be as ecommerce teams. It’s dynamic, contextual and deeply individual.

And that’s the good news. Because once you stop relying on proxies, and start responding to predictions, everything sharpens. Personalisation becomes meaningful. Experiences become appropriate. And performance follows.

You can’t scale personalisation on proxies. But you can scale it by predicting intent.