Cofounder of 13 Fund, and author of this investment thesis. As entrepreneurs who exited our startups last year, we wanted to give back to the communities that fostered us. So we set up this foundation to invest back in SF and NY with bi-annual grants.
For our first grant, we focused on small business development, specifically restaurant closures. We mined public data, talked to affected restaurants, as well as academics and politicians.
We found some expected causes (falling sales) as well as unexpected (labor flight).
Would be happy to answer questions about our methodology or findings!
Amplitude is a product intelligence platform, helping companies use self-serve analytics to make better product decisions. We're one of YC's Top 50 startups, already surpassing $100M in revenue this year serving customers like Twitter, Paypal, Atlassian, & Instacart.
We're now building a new team in our company to build the next $100M product, and looking for our first product engineer. This is an opportunity to work on a startup within a startup, building the foundation for a new frontend app and design system from scratch.
Likely unpopular opinion here, but it seems a little unfair to fault YC founders for believing "that the way to win was to hack the test", when the YC application itself would seemingly select for founders that exhibit this behavior with questions like "When have you most successfully hacked a non-computer system to your advantage?"
I'd posit the YC application in and of itself shares some of the facets that the article is critiquing in a "test". The fact there are paid services popping up to review YC applications reminds me of SAT Prep services.
Yep, you're correct that we're using observational studies via a regression to remove confounders and estimate treatment effects. Our confounders are synthetically generated based on the observable variables - we can only make projections of course on digital signals our customers send us (we only use first party data). We are working to incorporate actual experiment data into the algorithm over time as well, to get even closer to the true causal treatment effect.
Thanks for reaching out again! We're prioritizing support for Segment at this time, but hope to add other integrations next year. Our analytics product is completely free, so getting set up on our joint solution with Segment shouldn't be too expensive. :)
Hi Sean - great point! When I was at Optimizely working on their data science team, we found that on average a test needed 10K-20K unique visitors to reach significance.
We find that this rule of thumb extends similarly to our causal analytics platform. However, we have found that even low-traffic sites are able to get a boost if they are tracking more events on their website (increases the opportunities for signal). Also, our simulations run in minutes on all your historical, rather waiting for weeks for users to be exposed to the test, which speeds up time to insight. If we can not determine significance in our simulation though (due to either sample size or signal), we will designate the projection as a correlation.
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.
Thanks for the interest! (Cofounder of Clearbrain here).
The patent covers a combination of statistical techniques and engineering systems we built. The tricky part of this is the infrastructure needed to select confounding variables and estimate treatment effects for thousands of variables at scale in seconds. That was what we filed a patent on.
For our first grant, we focused on small business development, specifically restaurant closures. We mined public data, talked to affected restaurants, as well as academics and politicians.
We found some expected causes (falling sales) as well as unexpected (labor flight).
Would be happy to answer questions about our methodology or findings!