Heatmaps That Show Revenue, Not Just Clicks

01 /The Concept

Standard website heatmaps are static screenshots that color-code which page elements get the most clicks. They tell you where people look. They don't tell you whether any of it translates into revenue.

Heatmap built a tool that ties visual analytics directly to revenue. On its heatmaps, every page element is colored by how much money it generates – total revenue attributed, revenue per session, click-to-purchase conversion rate, and average order value driven by that element.

The engine behind this is an AI system that reconstructs user journey paths, associates them with purchase events, and distributes revenue credit across the page elements each buyer interacted with. The same system generates animated session replays showing exactly how purchasing visitors navigate a site – and how those paths differ from visitors who leave without buying.

On top of the heatmap visualization, the platform generates specific optimization recommendations: which elements to move, resize, or change based on their revenue contribution.

Integration takes a few minutes. Heatmap offers prebuilt connectors for Shopify, WooCommerce, BigCommerce, Magento, Salesforce Commerce Cloud, and Wix.

Pricing scales with store revenue. Shops generating under $1M annually pay $69/month (billed annually). A $15M-per-year store pays $359/month. Stores using Heatmap report a 23% increase in first-time purchases from new visitors and a 7.81% lift in average revenue per session.

Heatmap has raised $4M in seed funding after signing 500 store clients – and claims to have spent $0 on customer acquisition to get there. (The homepage says "trusted by 1,000+ ecommerce brands," so the precise figure is somewhat ambiguous)

02 /Why It Matters

At least two other startups worth noting have taken AI-driven approaches to e-commerce conversion optimization.

Bloom ([related review](/review/44-k-konversii-v-pokupku)) built an AI system for selecting the highest-converting product images. It raised $4.3M and claims proper image selection can increase purchase conversion by 20–44%.

Helium ([related review](/review/chto-meshaet-emu-zaplatit)) built a platform for A/B testing mobile app paywall screens without engineering involvement. It is currently in Y Combinator.

A notable feature of both platforms is that they don't just optimize for average users – they can personalize the experience for known visitors, showing each person the images and layouts most likely to convert them specifically. That's the natural direction for Heatmap's evolution as well: rather than optimizing a page's layout for the aggregate visitor, show each known user the element arrangement most likely to produce their purchase.

This kind of per-user optimization seems likely to become a baseline expectation for any serious e-commerce optimization platform.

Heatmap's best tagline: "You can't pay your bills with clicks, so we built Heatmap." That's a sharper way of stating a general principle: always optimize for the right metric. Clicks are easy to measure, but they can be deeply misleading as a proxy for revenue. A highly clicked element might have terrible purchase conversion; a quietly ignored element might drive disproportionate spend.

The same logic extends beyond e-commerce. Social media engagement metrics – likes, shares, comments – are notoriously poor predictors of actual sales from a given post. Which raises an obvious product idea: a social post heatmap that colors content not by engagement, but by downstream revenue attribution.

A related manifestation of the "right metrics" principle appears in the sales compensation platform SetSail ([related review](/review/prjaniki-dlja-massovyh-prodazh)). Rather than paying reps only on closed deals, it tracks which sequences of rep behaviors historically lead to wins – and rewards reps for those behaviors in real time, not just at the close. SetSail raised $37M and was acquired by ZoomInfo in June.

Another example: Voyantis ([related review](/review/predskazyvat-stalo-vozmozhno)) replaces cost-per-click bidding with LTV-based bidding – predicting the long-term customer value likely to come from each ad impression and bidding accordingly. It raised $20M.

03 /Opportunities

The central direction here is building platforms that track the right metrics – wherever people are currently using convenient but imprecise proxies.

This applies across a wide surface: e-commerce sites, mobile apps, B2B sales processes, content marketing, social media activity – anywhere that a simpler metric is being optimized because the more meaningful one is harder to measure.

The entry angle: find a domain where engagement metrics are still widely treated as success signals but correlate poorly with actual revenue – social media posts, B2B content, email campaigns, in-store displays. Calculate the real metric with AI, surface it in real time, and build the recommendation layer on top. Heatmap's model translates cleanly into each of these contexts.