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Amplitude Experiment + Contentful — Unlock no-code A/B tests in Amplitude
Seamlessly deliver and optimize content-driven experiences with our new integration to Contentful. Growth teams can run no-code A/B tests on web experiences like messaging, page layout, CTAs and more—without needing to ask engineering for help.
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Eithiriel DeMerè | Language-Market Fit
Hunter
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Amplitude Experiment is a way to experiment, learn, and release new features and products in a few simple clicks. Scale experimentation to accelerate acquisition, engagement, and retention with one unified approach.
Contentful is a cloud-based, headless content management system (CMS) that separates content creation from presentation. It allows content to be delivered via APIs to any platform or device, enhancing flexibility and scalability.
Integrate Amplitude Experiment with Contentful:
The Contentful plugin enables growth teams to run no-code experiments to personalize web experiences. Teams configure their experiment using Amplitude, build the variants in Contentful using their existing content, and analyze the results in Amplitude.
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Digging this update. Makes me think we should change from Prismic to Contentful for a project.
A/B tests for web experience will be really helpful. Are these tests technology specific?
@amit_arora No, due to the headless nature of Contentful, we have customers using this integration on both web and mobile applications. In short, Amplitude's Experiment can run along-side Contentful in any application; web, mobile, or other!
It makes perfect sense to bring in A/B test capability in amplitude. That is some serious competition for VWO.
@fabian_maume Competition is key for making great products! You should see what's coming up next :)
Congratulations on the launch! 🎉 How does the Amplitude and Contentful integration ensure that A/B testing results are accurately interpreted without requiring extensive technical expertise?
Hi @vasily_kolosov1! Thanks so much for your comment.
Amplitude Experiment's analysis makes it easy for non-technical users to get the information they need to make a decision about an experiment with confidence. The experiment analysis includes a high-level recommendation about rolling out or rolling back the feature in the experiment based on the statistical significance and directionality of the result based on the goals and metrics defined when creating the experiment.
Further, while the test is running the experiment constantly monitors for data quality issues and surfaces any issues in our "data quality checklist" with an explanation and remediation recommendation for each issue.
