Hi I’m Bilal, cofounder at https://www.clearbrain.com . ClearBrain is a new analytics platform that helps you rank which product behaviors cause vs correlate to conversion. Think Google PageRank, but for Analytics.
Our founding team worked on this problem for quite a few years while at Google and Optimizely. We contributed to Google Analytics to analyze historical behaviors in seconds, but observing historical trends merely produced noisy correlations. We built Optimizely to measure true cause and effect through A/B testing, but tests took 4-6 weeks on avg to reach significance, and so it would take years to measure the impact of every single page or feature in an app.
So we asked ourselves, could we estimate which in-app behaviors cause conversion, to complement (not replace) a traditional A/B test? We spent a year in R&D, and built ClearBrain as a self-serve “causal analytics” platform. All you have to do is specify a goal - signup, engagement, purchase - and ClearBrain ranks which behaviors are most likely to cause conversion.
Building this required a mix of real-time processing + auto ML + algorithm work. We connect to a company’s app data via Segment, and ingest their app events in real-time via Cloud Dataflow into a BigQuery backend. When a customer uses the ClearBrain UI to select a specific app event as their conversion goal, our backend will automatically run multiple observational studies to analyze how every other app event may cause that goal. This is done in parallel using SparkML, to analyze thousands of different events in minutes. (more on our algorithm here: https://blog.clearbrain.com/posts/introducing-causal-analyti...)
We’ve had beta customers like Chime Bank, InVision, and TravelBank use ClearBrain to estimate which behaviors and landing pages cause their users to convert, and in turn prioritize their actual growth and A/B testing efforts there.
We’re now releasing the product into general availability in partnership with Segment - available on a free self-serve basis today! We look forward to feedback from the HN community. :)
Hi Bilal, Thanks for the overview of the product.
This is a really important business problem to solve for many marketing teams. Just using this to prioritize A/B tests in itself pretty valuable. But one of the concerns around this approach is the un-reliability of causal analysis to estimate true effects. The link below refers to a study done at FB that shows observational studies could be erroneous in estimating effect sizes and in some cases, the direction of the effects. Do you think clearbrain's system is robust enough to estimate the true effects?
Thanks for the great feedback! Yes, some of these limitations expressed in the study are true in the case of ClearBrain - namely we are leveraging observational studies at this time as a prioritized ranking algorithm for which behaviors are most important, but the actual effect sizes themselves may be variable. We're working on improvements, as well as incorporating actual experiment data into our algorithm to make it more accurate over time.
Our founding team worked on this problem for quite a few years while at Google and Optimizely. We contributed to Google Analytics to analyze historical behaviors in seconds, but observing historical trends merely produced noisy correlations. We built Optimizely to measure true cause and effect through A/B testing, but tests took 4-6 weeks on avg to reach significance, and so it would take years to measure the impact of every single page or feature in an app.
So we asked ourselves, could we estimate which in-app behaviors cause conversion, to complement (not replace) a traditional A/B test? We spent a year in R&D, and built ClearBrain as a self-serve “causal analytics” platform. All you have to do is specify a goal - signup, engagement, purchase - and ClearBrain ranks which behaviors are most likely to cause conversion.
Building this required a mix of real-time processing + auto ML + algorithm work. We connect to a company’s app data via Segment, and ingest their app events in real-time via Cloud Dataflow into a BigQuery backend. When a customer uses the ClearBrain UI to select a specific app event as their conversion goal, our backend will automatically run multiple observational studies to analyze how every other app event may cause that goal. This is done in parallel using SparkML, to analyze thousands of different events in minutes. (more on our algorithm here: https://blog.clearbrain.com/posts/introducing-causal-analyti...)
We’ve had beta customers like Chime Bank, InVision, and TravelBank use ClearBrain to estimate which behaviors and landing pages cause their users to convert, and in turn prioritize their actual growth and A/B testing efforts there.
We’re now releasing the product into general availability in partnership with Segment - available on a free self-serve basis today! We look forward to feedback from the HN community. :)