Click-through rate
Click-through rate is the proportion of displayed or offered items that receive a click.
Experimentation for Engineers
Click-through rate is the proportion of opportunities in which users click a presented item, such as an advertisement or article.
- How it is used
- Measures ad-serving quality and revenue-relevant performance while comparing click models.
- How deep it goes
- Defines CTR from binary outcomes, works through standard error and bootstrap uncertainty, and applies it to bandit selection.
- Examples
- Uses simulated click models at CTRs 0.0050 and 0.0070, ten binary click observations, and bootstrap resamples.
- What you can do after
- Evaluate/understand CTR comparisons, uncertainty, and live selection trade-offs among competing click models.
Read how Experimentation for Engineers explains click-through rate →
Machine Learning System Design
The proportion of users who click a displayed search result or other presented link.
- How it is used
- Uses clicks as a live behavioral signal for search ranking and as a proxy for slower purchase outcomes.
- How deep it goes
- Explains definition, online measurement, proxy reasoning, metric hierarchy, and experimentation benefits without detailing CTR calculation.
- Examples
- Uses search-result clicks, image purchases, and comparisons with mean reciprocal rank and normalized discounted cumulative gain.
- What you can do after
- Evaluate live search behavior and apply CTR as a faster proxy when purchase labels are sparse.
Read how Machine Learning System Design explains click-through rate →
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.
- How it is used
- Evaluates whether recommendations or search results attract enough clicks to support ongoing product optimization.
- 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.
Read how The Art of AI Product Development explains click-through rate →
AI-Powered Search
Click-through rate is the number of clicks received by a displayed result divided by the number of times that result was displayed.
- How it is used
- Uses observed search clicks to create initial relevance judgments for learning to rank.
- How deep it goes
- Defines the ratio, works through document aggregation, and diagnoses position bias before deferring to a corrective click model.
- Examples
- Uses session records, the query “transformers dark of the moon,” document-level CTRs, and CTR values grouped by rank.
- What you can do after
- Apply it via Python to calculate document CTRs and identify why raw click rates can misrepresent relevance.
Related concepts
- aggregate metrickind of · An aggregate metric is a numerical summary that combines measurements collected across multiple events, requests, components, or operations.
- study designmeasured by · Study design is the planned arrangement of procedures, conditions, measurements, and comparisons used to investigate a question and support valid conclusions.
- click modelused by · A click model is a predictive model of the likelihood that a user clicks a particular result, advertisement, or other presented item.
- metrickind of · A metric is a quantitative measure used to characterize or assess a property, behavior, performance, or outcome.
- proxy metricused by · A proxy metric is an observable, measurable quantity or variable used to approximate a target attribute, quality, or outcome that is difficult to measure directly.
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