Task-based relevance
Record what users expected and which results helped them act.
Guide
Turn weak results into a concrete improvement plan
A useful search review checks whether people can finish real tasks, not just whether a query returns something. Start with a test set, classify failures, and change one part of the system at a time.
01 / Scope
What Improving search quality covers
Failure categories
Separate missing data, wrong matching, poor ranking, access issues, and interface confusion.
Repeatable evaluation
Keep a stable query set so changes can be compared over time.
02 / Method
A clear path through the work.
- 01
Build a query set
Include frequent tasks, rare terms, ambiguous wording, and no-result cases.
- 02
Review outcomes
Have subject experts judge results and record failure reasons.
- 03
Improve and retest
Prioritize fixes, then compare the same tasks after each change.
03 / Outputs
Know what your project includes.
- Representative query set
- Search failure log
- Prioritized relevance backlog
Specific scope, compatibility, schedule, and commercial terms are confirmed in a project agreement.
04 / Questions
A few useful details.
Is zero results always a search bug?
No. The content may be absent, restricted, or named differently; diagnosis comes first.
Can relevance be automated fully?
Signals help, but representative human judgments remain useful for important tasks.
How often should we review?
Set a cadence based on content change rate and search volume.
Your next useful answer
Bring us your business question.
We’ll connect your information requirements to the right Nakkeb product, service, or implementation scope.