Stop Pressuring Every Visitor the Same Way

01 /The Concept

Made With Intent helps e-commerce retailers convert more site visitors into buyers – not by applying more pressure, but by actually understanding what kind of shopper just walked in the door.

The real play here is that buying anything more expensive than a pack of gum at the checkout counter is a multi-stage process. The funnel metaphor exists for a reason: shoppers browse broadly, narrow their options, waver, add things to their cart and then abandon it, reconsider – and eventually either buy or walk away. Each stage calls for a different nudge.

Despite this, most conversion tools built for online stores assume every visitor is already ready to buy right now. They fire the same intervention at everyone, converting only those who were already on the edge – and count only same-session purchases as success.

Made With Intent's AI engine takes a different approach. It analyzes more than 200 behavioral signals in real time – what a visitor views, where they click, how they move through the site – as well as everything that same visitor did on prior visits. From all of this, it assigns each visitor to a segment based on their current position in the buying journey. That segment can shift in real time as behavior evolves.

Armed with this information, store owners can serve different ads and offers to different segments – nudging each visitor forward in the way that's actually appropriate for where they are. And "forward" doesn't necessarily mean purchasing today. The startup claims that using the platform lifts store revenue by 9.4% over six months.

A "segment" is essentially a bundle of behavioral signals the AI has detected. The platform comes with a set of ready-made segments, but store owners can define custom ones for even more precise targeting.

Pre-built segments include:

- Fence-sitters – visitors who are genuinely close to buying but can't quite pull the trigger. A time-sensitive discount might be the final push they need.

- Window shoppers – visitors just browsing with no real intent yet. What they need is something unexpected – an unusual product or a particularly compelling offer that suddenly captures their attention.

- Mysterious drop-offs – visitors who showed strong intent toward a specific product but left the product page without adding it to cart. A timely piece of information or a targeted offer can bring their attention back.

Segmentation also extends beyond the store's own website. Segments can be automatically exported for ad retargeting – serving the right message to specific visitor types even after they've left the site and scrolled into their social media feed.

And because visitor segments reflect traffic quality, the platform also lets store owners draw conclusions about the composition of their incoming traffic – informing decisions about ad creative and channel allocation.

Getting started requires nothing more than adding a single script tag to the store's site. Segmentation begins immediately.

Made With Intent was founded this year and is currently in beta. It already has paying customers seeing real results – and on that momentum, the startup just raised its first £1.5M (approximately $1.88M).

02 /Why It Matters

Behavioral analysis on e-commerce sites is a growing space. Session AI – [covered here](/review/prodavat-mozhno-dazhe-neizvestno-komu) when it was still called ZineOne – built a platform that can infer what an anonymous visitor intends to buy within just five clicks. It has raised $43M.

The anonymity problem is significant: roughly 90% of online store visitors are unknown to the retailer – they never registered, or their session expired long ago. Without a profile or purchase history to draw on, retailers need platforms like Session AI to make sense of these visitors.

Neocom, [covered previously](/review/luchshe-pojmjosh-bolshe-prodash) in late 2023, also reads visitor behavior on retail sites – but with a different goal. It detects when a shopper gets confused or stuck, then deploys a digital assistant to ask targeted questions and surface the right product. It has raised $4.5M.

What these startups share with Made With Intent is the core AI capability: reading behavioral signals to understand individual intent. Though the same engine can be pointed at very different problems.

ForMotiv, [covered previously](/review/vygodnaja-informacija-iz-niotkuda) in early 2023, applies it to insurance application forms – tracking mouse movement, typing rhythm, and pauses to assess whether an applicant is genuinely interested or perhaps concealing something. It has raised $9.4M.

And Subsets, a Y Combinator alum [covered in January](/review/privychka-vazhnee-chem-polza), segments newsletter subscribers by their reading habits – then uses those segments to keep each reader engaged in the way that works for them.

But the deeper insight across all of these isn't just the behavioral analysis itself. It's what happens next: owners of digital products stop treating all visitors as identical, and start treating each one according to where they actually are. Targeted analysis enables targeted action – and the aggregate effect is substantially better than any blanket approach.

This is why Made With Intent describes itself as "Moneyball for e-commerce."

For those who don't know the reference: Moneyball is a book – and later a film – about the coach of the Oakland Athletics baseball team. The traditional strategy in baseball was to recruit individual stars with the best solo stats. But Oakland couldn't afford stars, and their existing ones had just left. The coach teamed up with a statistician who had built a mathematical framework for evaluating team performance. His argument: you don't need stars – you need the right combination of undervalued players, correctly positioned. The coach followed the advice, assembled a team of players others had written off, and went on to win an unprecedented twenty consecutive games, setting a league record.

The parallel to e-commerce is direct: stop chasing the visitors who look like star buyers and start understanding the full range of your audience.

03 /Opportunities

AI has made it possible to analyze user behavior in real time at a granularity that was simply unavailable before – hundreds of signals, millions of micro-events per session. In ForMotiv's case, that extends all the way to mouse trajectory and keystroke timing.

The result is rich, real-time micro-segmentation. And that segmentation can feed downstream decisions or trigger automated, segment-specific responses – all without human intervention.

These capabilities are still maturing, and their adoption is nowhere near universal – even in spaces where they clearly apply.

One path is to build platforms analogous to the ones already working – using Session AI, Made With Intent, or ForMotiv as a reference point for a particular vertical or use case. The more interesting question is where else this same approach should be applied: where are people currently treated as a homogeneous group with identical intent, when the reality is far more varied? The behavioral signals that distinguish them already exist in most digital products – they just haven't been read.

"Divide and personalize" might be the most underused strategy in digital products right now.