Smart Search Ranking

Rank search results by what customers actually buy, not just how closely the title matches the query.

The problem it solves

Out-of-the-box platform search typically ranks on text match alone, so a lesser-known product with more matching keywords can outrank the item most shoppers actually want. Every mis-ranked result is a shopper doing extra scrolling, calling support, or simply leaving.

What it does for your business

Learns from real behavior

Trained on your own search clicks, carts, and orders, so ranking reflects what shoppers actually chose for a given query, not a fixed relevance formula.

Layers on your existing search

Your current search engine still retrieves the candidate results, the model simply re-ranks the top results before they're shown.

Improves over time

Every search session becomes new training signal, so ranking quality compounds the longer the storefront runs.

Proves the lift

Measured against held-out queries and tested live, so the improvement shows up as a conversion number, not an impression.

Where this moves the needle

Higher search conversion rate Higher click-through on top results Fewer search exits Fewer zero-click searches
How it works
A learning-to-rank model is trained on impression, click, cart, and order signals from your own search logs, with corrections for position bias, then layered on top of the results your existing search index already retrieves.
What we need from you
Search event tracking (query, results shown, position, clicks, and orders) and your product catalog. A few months of event history is generally enough to start.

See Smart Search Ranking on data like yours

A short walkthrough that compares current results against re-ranked results side by side.

Get Free Evaluation