A revenue leak is a specific, fixable point on the path to purchase where intent is lost. The visitor wanted to buy, or was close to it, and something in the experience turned that intent into an exit. Detection is the work of finding those points systematically rather than by intuition, and of ranking them by what each one costs.
This guide walks through how that detection actually works: where leaks hide in the funnel, how they are found, why every finding must carry evidence, and how they are ordered so the roadmap sorts itself.
Leaks live at funnel stages, so detection follows the funnel
The path to purchase is a sequence of thresholds, and each threshold sheds visitors. Detection is organized the same way, stage by stage, because a leak's location tells you both how to find it and roughly what it costs.
Landing and homepage. The first threshold is relevance. A visitor who cannot tell what you sell, or who lands on a slow or cluttered page, leaves before the funnel begins. Leaks here are expensive because they discard traffic you already paid for.
Collection and category. The browsing stage. Poor filtering, weak sort defaults, missing product information, and endless scroll all quietly suppress the click into a product page.
Product detail. The consideration stage, and the richest source of leaks. Absent or thin reviews, unclear shipping and returns, weak imagery, and buried variant selection each shave the add-to-cart rate.
Cart. The commitment stage. This is where surprise costs, unclear totals, and a missing path back to shopping cause the sharpest, most measurable drop-offs.
Checkout. The final and most sensitive threshold. Forced account creation, too many fields, limited payment options, and any late-appearing cost are the classic causes of abandonment here.
Costs that appear only at checkout are the single most cited reason carts are abandoned. A detector that walks the funnel finds this leak precisely where it lives, at the moment the total changes.
Two detection methods, deliberately separated
Robust detection uses two complementary passes, and keeping them separate is what makes the output trustworthy.
Deterministic rules
The first pass is a set of explicit checks that either pass or fail: is there a visible shipping estimate before checkout, does the product page expose reviews, is there an express payment option, does the checkout force registration. Rules are fast, reproducible, and never hallucinate. Their limit is that they only find what they were written to look for.
AI reasoning
The second pass reads the captured experience the way a skilled reviewer would, catching context a rule cannot encode: a value proposition that is technically present but confusing, a trust signal that exists but is undermined by surrounding copy, a mobile layout that is fine in isolation but hostile in flow. The reasoning layer classifies and explains; it does not invent numbers. Pricing is handled downstream by deterministic arithmetic on your real inputs, which is what keeps the figures defensible.
Every finding carries evidence, or it does not exist
The fastest way to lose a customer's trust is a finding they cannot verify. So detection is bound by a hard rule: a finding that cannot point to the specific page, capture, or funnel step it came from is discarded, not shown. Evidence is not decoration. It is the difference between a diagnosis and an opinion, and it is what lets a team act without re-auditing the claim themselves.
Common leak types, in the order they usually appear
Across storefronts, a recurring set of leaks accounts for most recoverable revenue: surprise costs at checkout, forced account creation, missing or weak social proof on product pages, unclear shipping and returns, slow mobile pages, and a cart that offers no confident path forward. None of these is exotic. The value of systematic detection is not novelty; it is knowing which of these is live on your store right now and which is worth the most.
Surface all-in cost before the checkout step
Recovers a share of the ~48% who abandon on surprise costShow shipping, taxes, and any fees on the cart page, before the visitor commits to checkout. The leak is not the cost itself; it is the surprise. Removing the surprise, not the cost, is what recovers the revenue.
Prioritization is revenue, not severity
Detection typically surfaces more issues than any team can ship in a quarter. The ordering decides the return. Ranking by severity forces subjective arguments; ranking by revenue ends them. Each leak is priced from your traffic, your average order value, and conservative uplift ranges, and the list sorts by modelled money at stake. The largest number is the first project, and the argument about what to do next simply dissolves.
This is also where the confidence ladder does its work. A freshly detected leak is priced as an Estimate. Connect your first-party analytics and the estimate is either Validated against your real funnel drop-off or revised. The ordering you act on is the validated one.
Validate before you build
Detection tells you where the money is leaking. It does not relieve you of the discipline of confirming a fix before committing engineering to it. The highest-value leaks are worth a cheap validation first.
Removing forced account creation at checkout increases mobile completion rate
Add a guest checkout path and route half of mobile sessions to it. Compare completion rates over a fixed window against the forced-registration control. A clear lift confirms the leak was real and sizes the recovery before you make the change permanent.
Detection, evidence, revenue ordering, then validation. That sequence turns a long list of possible problems into a short list of funded, provable projects.