G2

Tony D.

Mid-Market (51-1000 emp.)

8/17/2023

"Excellent, focused product that makes accurate A/B testing faster and easier across the tech stack."

5/5

What do you like best about Causal?

1. Built-in statistical modeling that avoids common pitfalls in analyzing A/B tests.

2. Encourages structured, well-defined A/B tests.

3. Makes it easy to run tests that cross layers of the stack.

4. Provides easy integration with our data warehouse, even for event generation that is unrelated to a specific A/B test.

5. Installation of impression servers in our own cloud means that it has a very low impact on performance.

6. Causal team is fantastic and very responsive, including supporting our internal training and adoption. Review collected by and hosted on G2.com.

What do you dislike about Causal?

1. It requires a significant investment in training engineers and product managers to incorporate it into your workflows.

2. The UX isn't as smooth and polished yet as some it could be. Review collected by and hosted on G2.com.

What problems is Causal solving and how is that benefiting you?

We're an organization that continually experiments and runs quantitative A/B tests. Before Causal, we had 4 A/B testing platforms, some home-grown, some commercial, that were used in different product areas. Generally, these platforms required manual analysis of the results, and allowed common failure modes (e.g., using standard stats and peeking at the results early). In addition, we have a totally schema-less, documentation-free "reportable event" API that was used in conjunction with these tools to provide ancillary information, which also has to be analyzed manually (and is almost never cleaned up after the fact). Causal provides us with a single platform across the stack, statistical tools, and a documented mechanism for events, which allow us to move faster and with greater confidence in the accuracy of our tests. Review collected by and hosted on G2.com.