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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 ���ϴ��¼�’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 ���ϴ��¼� if you don’t believe me.