Predict which users will upgrade or churn tomorrow.

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ClearBrain helps marketers predict which users will upgrade or churn on their app, before they do so. Identify high-probability users in minutes, and retarget them in your ads, emails, and AB tests without writing a single line of code. It’s GoogleML for marketers.


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Bilal MahmoodMakerHiring@bilalmahmood · CEO @ ClearBrain
Hi everyone I’m Bilal, cofounder of ClearBrain. Our team comes from Optimizely and Google where we spent over a decade building predictive marketing tools. We built ClearBrain to automate this entire process so any marketer can predict and retarget their high-risk users without having to write a single line of code. You can now simply connect to your user data in Segment or Redshift, and point-and-click to select any user action as a goal to predict. ClearBrain learns from your users past actions who will perform a future action, and automatically retarget those users in your Ads, Emails, and AB tests. One of our beta customers at InVision has said “ClearBrain has transformed how we approach remarketing, driving a 30% lift in CTR and an overall 7% increase in product engagement.” We hope to help others on ProductHunt, and look forward to hearing your feedback!
Dave Rogenmoser@dave_rogenmoser · Co-founder of Proof
Spoke with the ClearBrain guys the other day and they are wicked smart. Really looking forward to integrating with them.
Chandan LodhaHiring@chanfest22 · Co-Founder @ CoinTracker
Really awesome! I'm excited to try this out for our business — seems like it could add a ton of value.
Brian LuerssenPro@bluerssen · Co-Founder + CEO @ Draftbit
Love this. So critical.
Gregory Ugwi@ugwigr · Co-founder & CEO @Thinknum.
@bilalmahmood do you need to have a certain critical mass of users to train the algos first ?
Bilal MahmoodMakerHiring@bilalmahmood · CEO @ ClearBrain
@ugwigr Hi Gregory - great question! A general rule of thumb we've found is that for < 50K users ML solutions may be like trying to use a sledgehammer to put in a nail. A more nuanced answer is that it's dependent on the % of users that constitute the action you are predicting - if < 1% of users do the action in any given week, then there may not be enough signal in the data and thus the more users you have the better.