Platform

Developers

Resources

Pricing

Click-Through Rate (CTR)

Click-through rate (CTR) is a metric that measures the number of clicks advertisers receive on their ads per number of impressions. It's a ratio that shows how often people who see an ad end up clicking on it. An ad's CTR is calculated by dividing the number of clicks that an ad gets, by the number of times the ad is shown (impressions).

For example, if an ad was shown 100 times and it received 1 click, then the CTR would be 1%. If an ad was shown 100 times and it received 10 clicks, then the CTR would be 10%.

In the context of a website, CTR could refer to the ratio of users who click on a specific link to the number of total users who view a page, email, or advertisement. For instance, if a user reloads a page multiple times but clicks only once, this corresponds to a 100% CTR (1 out of 1).

Similarly, a user who loads a page once but clicks multiple times on a button should only count as 1 out of 1. This also solves for cases where users see an important button such as "Sign-up" multiple times a day, and we would still consider it a success if they click just once.

It's important to note that while a high CTR is often a positive signal, it's not always the case. For example, in experimentation, ratio metrics like CTR can sometimes be misleading. It's possible to see an increase in CTR alongside a net decrease in total clicks.

This situation can occur if the number of unique users viewing a button (denominator) decreases. As a best practice, it's recommended to track the numerator and denominator as independent metrics when monitoring ratio indicators.

Join the #1 Community for Product Experimentation

Connect with like-minded product leaders, data scientists, and engineers to share the latest in product experimentation.

Try Statsig Today

Get started for free. Add your whole team!

What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering
We use cookies to ensure you get the best experience on our website.
Privacy Policy