Thought leadership
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:
[Session 1]
───────────────────────────────────────────────────────────────────────
Product A → Product B → Product C → Product D → Product E
↑ ↑
(Longest Viewed) (Previously Purchased)
[Session 2]
───────────────────────────────────────────────────────────────────────
Product F → Product G → Product B → Product H → Product I
↑ ↑
(Most Viewed) (Last Viewed)
The problems with these interpretations:
- Last viewed ≠ Highest intent: Product I was viewed last, but only once and briefly. It’s a poor indicator of interest or conversion potential.
- 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.
- Longest viewed can be misleading: Product C was dwelled on, but that could reflect confusion, poor UX, or open-tab idling, not genuine interest.
- 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.
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.
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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.
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.
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.
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.