What are Search Terms?
Search terms are raw queries from users, captured as they were typed (before full analysis). They power analytics, merchandising decisions, SEO planning, and quality tuning. (Compare with query terms, the tokens produced after analysis.)
How It Works (quick)
- Collect & clean: Log queries with timestamp, locale, device; dedupe bots; strip PII.
- Normalize for analysis: Case/diacritics folding, whitespace fixes (keep the raw too).
- Cluster: Group by intent using n-grams/embeddings; mark head/mid/long-tail.
- Map to pages: Route recurring intents to canonical categories/collections/PDPs.
- Close the loop: Create synonyms, add facets, fix zero-result holes.
Why It Matters in E-commerce
- Demand capture: Build pages and filters users actually want.
- Fewer dead ends: Spot and fix zero-result queries fast.
- Merchandising: Inform promotions and “best bets.”
Best Practices
- Privacy-first: Hash IPs, drop order IDs/emails, set retention windows.
- Locale-aware: Separate markets/languages; respect units and sizes.
- Governance: Quarterly reviews of top clusters; annotate seasonality.
- KPIs: Track zero-results, reformulations, CTR@k, conversion, and assisted revenue.
- Don’t cannibalize: Maintain a keyword → page/facet map.
Challenges
- Noise from bots/typos, ambiguous terms (“apple”), seasonality, and sparse long-tail data.
Examples
- Rising query “packable waterproof jacket” → create a collection page and synonym: “packable rain jacket.”
- Many “gtx trail runners” → add GTX ↔ GORE-TEX synonyms and a waterproof facet chip.
Summary
Treat search terms as market research in real time. Cluster, map, and act—then measure the impact on zero-results, CTR, and conversion.
