Experimentation and A/B Testing For Modern Data Teams With Eppo
Data Engineering Podcast
Are You Preventing These Types of Unintended Consequences?
When you're running in experiment, you think you're testing one thing. But you're actually testing not only your product idea but also the execution of it. Early stopping is something that gets a lot of tension in stat circles where most companies are using the frequentest t tesz test paranime for running experiments. They can be things like net subscriptions and retention activation which encourage people to purchase off the shelf. And these sort of tools will help you use those metrics more effectively.
Summary
A/B testing and experimentation are the most reliable way to determine whether a change to your product will have the desired effect on your business. Unfortunately, being able to design, deploy, and validate experiments is a complex process that requires a mix of technical capacity and organizational involvement which is hard to come by. Chetan Sharma founded Eppo to provide a system that organizations of every scale can use to reduce the burden of managing experiments so that you can focus on improving your business. In this episode he digs into the technical, statistical, and design requirements for running effective experiments and how he has architected the Eppo platform to make the process more accessible to business and data professionals.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Chetan Sharma about Eppo, a platform for building A/B experiments that are easier to manage
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Eppo is and the story behind it?
- What are some examples of the kinds of experiments that teams and organizations might want to conduct?
- What are the points of friction that
- What are the steps involved in designing, deploying, and analyzing the outcomes of an A/B experiment?
- What are some of the statistical errors that are common when conducting an experiment?
- What are the design and UX principles that you have focused on in Eppo to improve the workflow of building and analyzing experiments?
- Can you describe the system design of the Eppo platform?
- What are the services or capabilities external to Eppo that are required for it to be effective?
- What are the integration points for adding Eppo to an organization’s existing platform?
- Beyond the technical capabilities for running experiments there are a number of design requirements involved. Can you talk through some of the decisions that need to be made when deciding what to change and how to measure its impact?
- Another difficult element of managing experiments is understanding how they all interact with each other when running a large number of simultaneous tests. How does Eppo help with tracking the various experiments and the cohorts that are bucketed into each?
- What are some of the ideas or assumptions that you had about the technical and design aspects of running experiments that have been challenged or changed while building Eppo?
- What are the most interesting, innovative, or unexpected ways that you have seen Eppo used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Eppo?
- When is Eppo the wrong choice?
- What do you have planned for the future of Eppo?
Contact Info
- @chesharma87 on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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