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Click-through rate

As explained in The Art of AI Product Development

Click-through rate is the proportion of users or impressions that result in a click on a presented link, recommendation, or control.

Click-through rate (CTR) measures the share of users or impressions that produce a click on a presented link, recommendation, or control. It offers a practical signal of whether displayed content appears relevant enough to explore. section 4.4.2 section 7.2.3

CTR is useful but incomplete. A high recommendation CTR suggests relevance, while conversion rate more directly connects recommended items to purchases and revenue. Engagement signals such as dwell time, browsing time, and interactions add context. section 4.4.2

Use CTR to support ongoing evaluation and optimization:

  • Monitor whether users click top-ranked search results; low CTR can indicate that results do not match search intent. section 7.2.3
  • Compare recommendation strategies through A/B tests, while checking conversions and broader engagement rather than optimizing clicks alone. section 4.4.2
  • Track CTR continuously because new documents, changing user behavior, or evolving products can alter performance after launch. section 4.4.2 section 7.2.3

Low search CTR

If users repeatedly search for “feature comparison” but rarely click the returned results, the low CTR signals a likely relevance problem and points toward refining retrieval or ranking. section 7.2.3

How The Art of AI Product Development covers it

How deep it goes
Explains CTR as a monitoring signal and connects it to testing, relevance diagnosis, conversions, and engagement, but does not derive its calculation.
Examples
Uses product recommendations, ranked search results, repeated feature-comparison queries, and comparisons of filtering strategies as examples.
What you can do after
Evaluate recommendation and search performance, diagnose relevance problems, and compare changes using clicks alongside stronger outcome and engagement measures.
How it connects
Connects CTR with conversion rate, A/B testing, recommendation systems, search relevance, dwell time, bounce rate, and query-log analysis.

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This explainer was generated by Manning's knowledge graph from the text of The Art of AI Product Development. The section links point to the parts of the book it draws on.