Launch HN: Promi (YC S24) – AI-powered ecommerce discounts
Peter and Jiaxin are developing Promi, an AI platform to optimize retail e-commerce discounts, focusing on inventory liquidation and inviting feedback from Shopify store owners for improvement.
Peter and Jiaxin are developing Promi, an AI-driven platform designed to optimize retail e-commerce discounts tailored to products and customers. Traditional discounting methods are often manual and lack scientific backing, relying on historical data and competitor analysis. Promi aims to enhance this process by utilizing AI to determine optimal discount values, personalizing them for different user groups, and adjusting them frequently based on various data points such as conversion rates and profit margins. The platform is particularly beneficial for smaller merchants who typically struggle with effective pricing strategies. Promi's initial product focuses on inventory liquidation, using historical sales data to train its model and predict necessary discounts to boost conversion rates. The founders, with experience from Uber, have previously implemented similar AI features and learned to create a global model that leverages data across multiple markets to improve discount performance. They invite feedback and suggestions from the community, especially from Shopify store owners who can try their app.
- Promi uses AI to optimize e-commerce discounts for products and customers.
- The platform aims to improve discount strategies for both large and small retailers.
- Initial focus is on inventory liquidation, using historical sales data for model training.
- Founders have experience from Uber, where they implemented AI-driven discount features.
- Feedback and suggestions are welcomed from the community, particularly Shopify users.
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10 commentsSome questions:
1) I’m assuming by “personalizing discount across users”, you mean personalized one-time coupon codes? I wonder if the UX of seeing one price in regular Chrome and one in incognito would be upsetting. I also don’t know how price discrimination works but seems relevant?
2) I’d love to understand more about how for smaller retailers there’ll be enough data to make meaningful discount programs for a limited set of consumers? Will data from similar/multiple retailers be bucketed?
3) Any numbers/data on effectiveness so far?
- Is prediction based on historic transaction and sales data effective? I always assumed transaction and sales data in isolation didn't contain enough information to be effective predictors of buying behavior. Is that wrong?
- How much more effective is it than a human intuitively setting a discount? I can see large retailers saving time on having to set discounts for a large number of products. Just wondering if small merchants would just be better off doing it themselves.
I'm curious about the personalization aspect. How do you plan to balance the potential uplift with the risk of customer backlash if they feel manipulated?
The 1% commission seems steep for smaller merchants. Have you considered a tiered pricing model based on revenue or order volume?
- Will you also be looking into real time competitor pricing to make discounts more competitive?
I think you need to better communicate the causality between applying your model and increased profits to justify your 1% commission.
https://www.theatlantic.com/ideas/archive/2024/08/ai-price-a...
https://www.propublica.org/article/yieldstar-rent-increase-r...
Before you say, “Stripe:” listen, they’re too greedy too!
So it's surge pricing but masked as discount... but hey, it'll work for the consumption-driven world.
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