Metrics

What it solves

Search is invisible until something goes wrong. A query that returns no results. A high-traffic product that nobody clicks on. A trending category that the catalog isn't serving well. Without visibility into how search is performing, these problems go unnoticed — and revenue walks out the door with them.

MXP Metrics brings search performance into view across four dimensions: how shoppers are searching, how results are converting, which products are driving revenue, and what's trending before it shows up in your analytics dashboards. It gives merchandisers and search operators the data they need to act — not just observe.

When to use it

• Daily search health check — verify zero-result rate is low and conversion funnel rates are stable
• Investigating a drop in revenue or conversion — find which queries or products are underperforming and why
• Identifying gaps for enrichment or merchandising rules — high-traffic queries with low view rates signal result quality problems; high-view, low-purchase products signal pricing or content issues
• Catching trends before they peak — trending queries and categories surface emerging shopper intent so you can act before demand outpaces your catalog's ability to serve it
• Evaluating the impact of a change — after updating merchandising rules, enrichment configuration, or linguistic overrides, check whether the relevant metrics improved

Key concepts

Search count — the total number of search queries executed in the selected time period. The baseline metric for search activity.

Zero-result rate — the percentage of searches that returned no products. A zero-result search is a complete failure of the search experience — the shopper saw nothing and likely left.

View rate — the percentage of searches after which the shopper clicked on at least one product. Measures result relevance: if shoppers search but don't click, the results aren't matching their intent.

Add-to-cart rate — the percentage of searches that resulted in at least one product being added to the cart. Measures commercial intent conversion.

Purchase rate — the percentage of searches that resulted in a completed purchase. The end-to-end conversion metric for search.

Revenue — total revenue attributed to search sessions in the selected period. Alongside purchase rate, this is the primary business impact metric.

Revenue rate — average revenue per search. Useful for comparing periods with different search volumes — a smaller number of higher-intent searches might generate more revenue per search than a period of high-volume, low-intent browsing.

Trend score — a measure of how rapidly a query or category is gaining search volume relative to its recent baseline. High trend scores flag emerging intent before it appears in standard volume rankings.

How it works

Metrics are organized into four views, each answering a different question.

Search performance over time

Tracks the full conversion funnel — search volume, zero-result rate, view rate, add-to-cart rate, purchase rate, and revenue — across a configurable date range.

Use this view to:

• Monitor day-over-day search health
• Spot anomalies (sudden zero-result spikes, conversion drops) and correlate them with catalog or configuration changes
• Compare performance across different time periods

Query performance

Shows per-query breakdowns of the full conversion funnel: how many times each query was searched, its zero-result rate, view rate, add-to-cart rate, purchase rate, and revenue generated.

Use this view to:

• Find high-traffic queries with poor conversion (high search count, low view rate) — candidates for merchandising rules or linguistic override fixes
• Identify zero-result queries — these need either enrichment or linguistic overrides to map the query to terms the catalog uses
• Find high-converting queries to understand what "good" looks like for your catalog

Product performance

Shows per-product metrics: how often each product appears in search results, how often it's viewed, added to cart, purchased, and how much revenue it drives.

Use this view to:

• Identify high-revenue products and verify they're appearing prominently in relevant searches
• Find products with high view counts but low purchase rates — these may have pricing, content, or attribute quality issues
• Find products with high search counts but low view rates — these may be ranked too low for their relevant queries

Trending queries and categories

Shows which search queries and product categories are gaining momentum — ranked by trend score, with the change in volume compared to the recent baseline.

Use this view to:

• Spot emerging demand before it peaks — a query trending upward is an opportunity to ensure the catalog can serve it well
• Identify trending categories where inventory or merchandising attention is needed
• Inform enrichment priorities — if a trending category has low attribute coverage, run enrichment on it before demand peaks

Quick example

A search operator opens Metrics on a Monday morning and notices the zero-result rate spiked to 0.9% on Saturday — up from a near-zero baseline all week. They switch to the Query Performance view and filter by zero-result rate descending. Three queries account for the spike, all variations of a product name announced in a press release that weekend.

The products exist in the catalog under a different name than what shoppers are searching for. The operator adds a synonym rule in Linguistic Overrides mapping the new name to the catalog term. By Monday afternoon, the zero-result rate is back to baseline and the three queries are now converting.

Related pages

• Discovery Rules — act on query performance insights by boosting, burying, or pinning products
• Linguistic Override — fix zero-result queries caused by vocabulary mismatches
• Attribute Enrichment — improve attribute coverage to serve trending categories and reduce zero results
• Evaluation — deeper search quality assessment beyond conversion metrics