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Overview of Experimentation
Overview of Experimentation

How to design and implement an A/B test in Candu.

Lauren Cumming avatar
Written by Lauren Cumming
Updated this week

Have a hunch on how to improve your product's performance? With Candu, you can bring your ideas to life and create new product experiences, run experiments, and analyze the results without engaging your engineering team. πŸ™Œ

Intro to Experimentation & A/B Testing

Experimentation and A/B testing are the secret ingredients that drive successful product optimization. Rather than relying solely on gut feelings or educated guesses, product teams leverage these approaches to make well-informed decisions that lead to real, data-backed results.

What is Experimentation?

Experimentation is the strategic method of testing hypotheses to gather insights and validate assumptions. By setting up controlled experiments, you can accurately measure the impact of changes on critical performance metrics. This data-driven approach allows you to make confident choices, enabling continuous progress and innovation.

What is A/B Testing?

A/B testing, sometimes called split testing, is a specific type of experimentation that compares two or more variants (A and B) to determine the most effective option. By randomly presenting different versions to users and measuring their interactions, A/B testing reveals which variant drives more favorable outcomes, such as increased conversions or higher activation rates.

Adopting a culture of experimentation in your team is a great way to fuel iteration and innovation, keep you agile and adaptive, and ensure continuous progress toward achieving your goals. πŸš€

Running A/B Tests with Candu

We built Experimentation to allow you to launch new experiences in your product quickly and independently. Whether in-line experiences or captivating overlays, you can explore different options and identify the best-performing UX style that resonates with your users.

Our experimentation feature allows you to effectively:

  • Create different versions of experiences and A/B test these against each other and a control group

Using the editor to create multiple versions of an A/B test.
  • Target specific segments of users and control the distribution of each content version across different audiences

The settings pages for setting up an A/B test.
  • Control a progressive rollout setting so you can launch to users over time, minimize risks, and launch with confidence

Showing how to distribute an audience and set up a progressive rollout.
  • Monitor results in real time to gather insights from the get-go and understand what is and isn't working. We take care of all the analysis for you and monitor conversion goals against your user events.

  • Identify the winning variant and quickly make it live to drive growth for your product over time.

Overview of A/B Experiments (Video)

Check out our 6-minute overview of A/B Experiments:

Learn more about how to set up your first A/B test, analyze the results and review best practices here:

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