Definition

Search analytics is the practice of collecting and analyzing search behavior and outcomes. It turns search from a black box into an improvable system by showing what users search for, what they click, and where they struggle.

Why it matters

Find gaps: identify zero-result queries and missing content.
Tune ranking: measure whether changes improve satisfaction.
Validate intent: see which intents succeed and which fail.
Improve discoverability: detect content that is hard to find or not indexed.

How it works

Log queries + results + clicks -> metrics -> insights -> experiments -> iteration

Common metrics include: query volume, zero-result rate, click-through rate, time-to-first-click, reformulation rate, and conversions (task completion).

Practical example

If “inheritance tax Belgium spouse” has high volume but low clicks and many reformulations, that’s a signal to adjust relevance tuning, add semantic expansion, or create better content.

Common questions

Q: What’s the minimum analytics to start with?

A: Queries, result count, clicked result, and whether the user reformulated or abandoned.

Q: Can analytics be privacy-safe?

A: Yes. Use aggregation, hashing, retention limits, and avoid logging sensitive raw content when possible.

Query Intent - segment analytics by user goal
Relevance Tuning - use analytics to guide tuning
Content Discoverability - uncover indexing and linking gaps
Indexing Strategy - analytics informs what to index next

References

Manning, Raghavan & Schütze (2008), Introduction to Information Retrieval.