Demo: https://youtu.be/BCYNCqb4fUc, Sales Video: https://www.youtube.com/watch?v=WiO1S7RBn-o

All the big tech companies send personalized discounts - Uber, DoorDash, Google, etc. In fact, I was the product lead overseeing discounts at Uber, so if you’ve gotten a promotion on Uber Rides or Eats, that was our tech. These personalization models often generate >30% more revenue vs. non-personalized discounts (cost-neutral that is), so this is a hugely impactful product.

It’s no surprise then that other merchants want to follow suit. Merchants don’t want to waste discounts on customers who would have purchased anyway. Frankly it’s not a new idea to offer a software solution to personalize discounts - plenty of other startups have entered this space with a similar product.

The biggest problem with personalizing discounts for mid-size and smaller companies has been that traditionally you rely on ‘explore’ data - data from randomly sending out discounts to a portion of the user base. But this has a lot of problems: merchants need to be large, collecting this data is expensive, training data really should be fresh (so explores should constantly be running), and if you want to try a different discount structure (e.g. BOGO instead of 20% off) you’ll need to run a new explore with the new structure.

So what does Promi do differently? We train on regular traffic and simplify the problem by just focusing on conversion rate. If we can accurately predict who is unlikely to convert and which products are unlikely to be bought, we can issue discounts without the fear of burning money on an order that would have happened anyway. One of my major takeaways from my time at Uber was that our model was mostly targeting users who had a low likelihood of converting in a given week. Quantifying how much more likely they were to convert when given a discount via explores was helpful, but not as impactful as understanding starting conversion rate.

Side note - It’s been a bit interesting launching an AI company during this hype cycle that isn’t actually using the latest and greatest LLMs. We believe more traditional machine learning still has a lot of value to add. I don’t want to say we won’t use LLMs down the road (there may be some interesting applications for developing additional features), but starting this way has worked out well for us.

There have been plenty of other challenges (as with any startup). We’ve had to figure out how to automate integrations when so many websites have custom code. We’ve had to make the model work without rich user data since the majority of website visitors aren’t logged in. A quick note in this one - we can use first party cookies to more or less track the view and transaction history, but we’ve found that one big predictor of conversion is traffic source: whether a visitor is coming from ads, email, direct traffic, google search, etc. That traffic source isn’t something as valuable at Uber (since everyone uses the app), so it’s been a bit of a tradeoff in the types of features that are most impactful.

Our model seems to be working well! We have case studies on our website showing the typical revenue and profit lift we see. We currently have tiered pricing with different quotas for the amount of revenue managed by Promi discounts.

I’d love to get thoughts from the machine learning experts in this community, though full disclosure I’m the non-technical founder. Let us know what you think!