bmahmood

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 (YC W12) | Product Engineer | https://amplitude.com/

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.

Tech: JavaScript, React, CSS, Python, Node, Postgres, GraphQL

Apply @ https://boards.greenhouse.io/amplitude/jobs/4551252002

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.

Aww thank you! Our design team is pretty cool. :)

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. :)

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.

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. :)

Apologies! Looks like the site was mid-update when you noted the 404s. It's back live now :)

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.

So weird! Apologies - hope this link below works:

https://angel.co/clearbrain/jobs/177711-machine-learning-eng...

ClearBrain (YC W18) | Machine Learning Engineer | San Francisco, CA | Onsite | https://clearbrain.com

ClearBrain (YC W18) is a startup building the first self-serve predictive analytics platform. We help companies automatically predict and analyze when their users are most likely to convert or purchase. Think a supercharged Google Analytics, based on internal tools our team built at Google, Netflix, and Uber. Fortune 1000 companies use ClearBrain to deliver billions of user-predictions every week and drive double-digit lift in their digital campaigns.

We're a deeply technical team (we were the first engineers on Google Ads and Optimizely), and are backed by early investors in Dropbox and AdMob. We're hiring for machine learning engineers to lead on new cutting-edge products we'll building. We work in Go, Python, Node, Scala, Spark in the backend and React, Javascript, Firebase in the frontend.

For more about the role: https://angel.co/clearbrain/jobs/177711-machine-learning-eng....

If interested, please reach out to grant [at] clearbrain.com - we look forward to hearing from you!

Very excited to see Segment launch their Developer Center in conjunction with this Growth Stack overview. Our company (ClearBrain) was one of the early technology partners to build into their Development Center, and it's been a transformative impact on our business.

As an analytics company, one of the first hurdles to getting a customer successful is onboarding their data. Every customer has heteregenous schemas and then you need to wait weeks to collect enough data to find reasonable results. Segment made this dead simple by providing an API spec with a standardized schema that just took a couple days for us to integrate with. Once integrated, you gain access to 1000s of mutual companies using Segment, who can stream you their data in exactly the same format (a huge win for analytics efforts and data normalization).

Highly recommend other companies to consider integrating with Segment. The ease of integration and access to a platform serving thousands of customers is especially helpful for startups.

ClearBrain (YC W18) | Machine Learning Engineer | San Francisco, CA | Onsite | https://clearbrain.com

ClearBrain (YC W18) is a startup building the first self-serve predictive analytics platform. We help companies automatically predict and analyze when their users are most likely to convert or purchase. Think a supercharged Google Analytics, based on internal tools our team built at Google, Netflix, and Uber. Fortune 1000 companies use ClearBrain to deliver billions of user-predictions every week and drive double-digit lift in their digital campaigns.

We're a deeply technical team (we were the first engineers on Google Ads and Optimizely), and are backed by early investors in Dropbox and AdMob. We're hiring for machine learning engineers to lead on new cutting-edge products we'll building. We work in Go, Python, Node, Scala, Spark in the backend and React, Javascript, Firebase in the frontend.

To learn more about who we are and our engineering culture, check out: https://www.keyvalues.com/clearbrain

For more about the role: https://angel.co/clearbrain/jobs/177711-machine-learning-eng...

If interested, please reach out to grant [at] clearbrain.com - we look forward to hearing from you!