Shoppers search in their words. Your catalog uses different words. Someone loses revenue — and right now it's you.
Zero-result pages are the most direct, measurable form of lost ecommerce revenue. They happen when the vocabulary a shopper uses and the vocabulary in your catalog diverge — and your search engine has no way to bridge the gap. SearchSense Commerce closes that gap semantically, not with synonym tables.
Already trusted by industry leaders
72%
of ecommerce sites fail to support common query types
43%
avg conversion lift from AI search optimisation
5–15%
typical zero-result rate before semantic AI
Zero-result recovery
A detailed look at the problem, its structural cause, and the mechanism by which SearchSense Commerce solves it.
01
What's actually happening
A shopper searches 'comfy gym shoes'. Your product is titled 'Cushioned Athletic Trainers'. Keyword search sees no match and returns zero results. The shopper sees a dead end and leaves. At 50,000 visits/month with a 15% search rate and a 5% zero-result rate, that's 375 lost search sessions every month — each one a buyer with expressed purchase intent who found nothing.
02
The structural cause
Standard search engines match words to words. They have no concept of meaning. 'Comfy' and 'cushioned' are unrelated strings to a keyword engine — even though a human reader instantly understands they mean the same thing in context.
03
The mechanism — not just the claim
SearchSense Commerce uses vector-based semantic search. Every product in your catalog and every search query is converted into a mathematical representation of meaning. When a shopper searches 'comfy gym shoes', the system finds products whose meaning is closest — not products whose title strings match.
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Key capabilities
What SearchSense Commerce delivers for this use case.
Queries matched by meaning, not words — vocabulary gaps between shoppers and catalog copy closed automatically without synonym table maintenance.
Every query that returned no results or low-confidence results, tracked daily — with volume, trend, and estimated revenue impact per query.
High-volume zero-result queries surfaced as catalog gaps — identify which product categories or attributes are missing from your indexed data.
New products indexed within minutes of publication — zero-result pages caused by catalog lag eliminated from the moment a product goes live.
When confidence is genuinely low, the system surfaces the closest relevant alternatives rather than an empty page — no shopper ever sees a dead end.
See which products are being found through semantic matching vs exact title match — identify which product names are furthest from how shoppers actually search.
