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Welcome back to the Klaviyo Data Science podcast! This episode, we dive into…
We’ve discussed A/B testing multiple times on this podcast, for good reason. But there’s an important angle we have yet to cover: in the life of a researcher or marketer, there’s no such thing as an A/B test. There’s an entire system of A/B tests run for specific purposes over time. What is the best way to construct a system of A/B tests to help you learn, improve, and grow over time? How does that translate into tenets to hold while building software to help people run A/B tests? We’ve brought on three members of the data science team at Klaviyo, and you’ll hear about A/B tests in a variety of ways, including:
Check out the full show notes on Medium for more information!
By Klaviyo Data Science Team5
55 ratings
Welcome back to the Klaviyo Data Science podcast! This episode, we dive into…
We’ve discussed A/B testing multiple times on this podcast, for good reason. But there’s an important angle we have yet to cover: in the life of a researcher or marketer, there’s no such thing as an A/B test. There’s an entire system of A/B tests run for specific purposes over time. What is the best way to construct a system of A/B tests to help you learn, improve, and grow over time? How does that translate into tenets to hold while building software to help people run A/B tests? We’ve brought on three members of the data science team at Klaviyo, and you’ll hear about A/B tests in a variety of ways, including:
Check out the full show notes on Medium for more information!