Internal search for ecommerce & marketplaces
Internal search,
diagnosed and improved.
Ranking, relevance, query understanding, and search analytics — evaluated and refined, independent of your search vendor.
Experience with large-scale ecommerce search systems — Algolia, Elasticsearch, OpenSearch, Typesense, Luigi's Box.
Most teams assume their search works well enough
Until you examine real queries closely, ranking issues, weak query handling, and dead ends stay invisible. The search engine returns results — just not the right ones, often enough to matter.
Ranking misses intent
Results look plausible at a glance but don't match what the customer meant — especially on your most important queries.
Query handling breaks quietly
Synonyms, typos, and compound queries return poor or empty results — and no one is alerted.
Dead ends cause silent exits
Zero-result pages and no-click searches push people away, but typical analytics don't flag them.
No way to measure quality
Without structured evaluation, teams can't tell whether search is improving, degrading, or standing still.
Diagnostic patterns
What we find when we look closely
These patterns appear in most ecommerce search systems. They're rarely visible in dashboards.
Relevance
Results look right. They're not.
Top queries return plausible products, but bestsellers are buried and weak matches surface first. The search appears functional — the ranking is wrong.
Query interpretation
Queries break without anyone noticing
Synonyms missing. Compound queries split wrong. Attribute searches like “red dress size 38” treated as free text. No alerts, no fallback.
Ranking
Ranking rules nobody owns
Boosting rules layered over months. Conflicting weights across categories. Result order changes and no one evaluates the impact.
Zero results
Silent dead ends
Queries return nothing — no redirect, no suggestion, no signal. Users leave. It happens most on long-tail and misspelled queries.
Quick diagnostic
Test your search in 5 minutes
Run three simple checks that often reveal hidden ranking or query interpretation problems.
3 checks · 5 min · no setup
Diagnostic methodology
How we diagnose search
A structured process applied to every search system we evaluate.
Real queries
Analyze actual user queries — high-volume, high-intent, long-tail, and failure patterns.
Query interpretation
How the system reads the input. Tokenization, synonyms, compound handling, attribute mapping.
Result quality
Whether results match intent. Position accuracy, dead ends, coverage gaps.
Ranking logic
Boosting rules, attribute weights, merchandising overrides, and their cumulative effect.
Improvement roadmap
Prioritized, actionable changes your team can implement. No dependency on us.
Each step informs the next. The process repeats as the system evolves.
Search frameworks
Conceptual tools we use to analyze how search systems behave, where they fail, and how relevance should be evaluated.
Framework 01
Relevance evaluation
A structured method for measuring whether search results match user intent — beyond click-through rates and conversion proxies.
Inputs:
- •Representative query test sets
- •Human relevance judgments
- •Before/after result comparison
Framework 02
Query taxonomy
A classification system for query types. Each type has different failure modes and requires different ranking logic.
Types:
- •Navigational (exact product)
- •Attribute (color, size, material)
- •Exploratory (broad category)
- •Long-tail (rare or compound)
Framework 03
Ranking failure modes
A diagnostic checklist for identifying why result ordering breaks down. Used to trace ranking problems to their configuration root cause.
Common modes:
- •Boosting rule distortions
- •Synonym and tokenization gaps
- •Category or attribute bias
Deep, narrow expertise
We only do one thing: internal search for ecommerce. That focus means faster diagnosis, sharper recommendations, and less wasted scope.
Vendor-agnostic
Algolia, Elasticsearch, Luigi's Box, Doofinder, Coveo, Bloomreach, and others. The methodology applies regardless of your stack.
Experiment-driven
Every recommendation includes testable hypotheses and success metrics. Your team can prioritize and validate with controlled experiments.
Multilingual
Audits conducted in English, German, French, Spanish, Italian, Dutch, and Swedish. Query analysis in the language your customers actually use.
No lock-in
We deliver a diagnosis and a roadmap. Your team or vendor handles the build. No retainer, no ongoing dependency.
Search environments we've worked in
Catalog scale
10k–10M+ products
Query volume
High-traffic ecommerce
Ranking complexity
Boosting, weighting, rewriting
Platforms
Algolia, Elasticsearch, OpenSearch, Typesense
Languages
Multilingual European markets
Also Doofinder, Luigi's Box, Coveo, Bloomreach, and other platforms. The diagnostic methodology applies regardless of vendor.
What the diagnostic produces
A clear picture of where search is failing and a prioritized plan for what to fix.
Diagnosis
A clear account of where and how search is failing — broken down by query understanding, ranking behavior, coverage, and evaluation gaps.
Query analysis
Examples of problematic query patterns drawn from real traffic: misinterpreted compound queries, attribute failures, silent zero-result cases.
Ranking observations
An assessment of how ranking logic behaves across query types — where configuration is working and where it is distorting results.
Evaluation perspective
An honest view of what is and is not being measured, and what a minimal structured evaluation process would look like for the system.
Improvement roadmap
A prioritized list of changes — ordered by impact and feasibility — that your team or vendor can act on directly.
Example diagnosis
Ranking failure investigation
A simplified version of the diagnostic process used during a search audit. The query, the observed result behavior, and the likely root causes.
Query
black running shoes
Observed result behavior
Top-selling SKUs ranked below weak text matches. Position 1–3 occupied by low-conversion products with partial keyword overlap.
Identified root causes
- •Boosting rule on “new arrivals” overriding textual relevance score
- •Color attribute not indexed as a filterable field
- •No ranking validation against a representative query set
Different query. Same diagnostic structure.
From real audits
Failures we find repeatedly
Specific patterns that appear across search systems, regardless of vendor or catalog size.
- •Merchandising rules overriding textual relevance
- •Synonym lists masking deeper indexing gaps
- •Attribute queries parsed as free-text search
- •Compound queries returning partial or empty results
- •Zero-result queries with no fallback or redirect
- •Ranking configuration drifting without evaluation
- •Boosting rules conflicting across product categories
If search feels hard to evaluate, that's usually a signal
Results look acceptable. Confidence is low. Something feels off but there's no clear evidence yet.
That's exactly the kind of system we diagnose. We examine real queries, ranking behavior, and evaluation gaps — then give your team a clear picture of what's happening and what to fix first.