Launch HN: Promi (YC S24) – AI-powered ecommerce discounts
2 by pmoot | 0 comments on Hacker News.
We’re Peter and Jiaxin, and we’re building Promi ( https://ift.tt/CbhB1OX ). Promi uses AI to optimize retail ecommerce discounts across products and customers (think new customer discounts, clearance sales, holiday sales, etc.). Here’s a quick video overview: https://www.youtube.com/watch?v=SHTw9VH8bCw Discounts have traditionally been a bit of the ‘wild west’ of pricing. Optimization techniques at even the largest merchants are heavily manual. Product level discounting decisions are distributed among operations or category managers, and store-wide discounts are often set by marketers. Typically they rely on order history, look at competitor discounts, or find prior discount performance to inform their decision. But there’s not a lot of science behind choosing the discount, and teams don’t have the time or means to optimize it at a granular level - i.e. by product or customer groups. We believe AI can better solve this problem by setting the most appropriate discount value, varying that discount across products, and personalizing that discount across users in order to achieve various goals. AI models can leverage more data (e.g. item conversion rate, profit margin, customer referral URL, device type) and update more frequently than is realistically possible to do manually. Our approach will also allow us to generate discounts for relatively small merchants. We use models (layering traditional NLP models and custom LLMs) to build a large-scale knowledge graph to gather similar products across merchants in order to build profiles around different clusters of products. Those profiles help us build solutions catering towards subscale shops which traditionally do not have an optimal pricing strategy. Our first AI product focuses on liquidating inventory, and uses a store’s historic transaction and sales data to jump start training our model and generating discounts. Our model predicts the discount required to increase conversion rate by the proper amount to liquidate the inventory by the desired timeframe. We then monitor the conversion rate and (if we have statistical significance) make frequent adjustments as needed. We’ve got a full roadmap of new model approaches, including personalization and new objective functions (e.g. profit instead of liquidation) to fit more discount use cases. How we got here: Jiaxin and I are coming from Uber, where I led product for the discount team across Eats and Rides. We launched several analogous AI features at Uber and saw just how impactful they can be for structuring discounts. For example, we had issues with deploying our ML models for automated discounts in smaller markets because of the quantity of data required to train those models. We pivoted to a 'global model' that used data across countries to significantly reduce the amount of data required in any one country. That model performed even better than country-specific models, showing us that there were very reproduce-able trends in improving discount performance. If you run or know someone who runs a Shopify store, you can download and play around with our app here: https://ift.tt/nmZKzIL We’d love feedback, thoughts on other use cases for discounts + AI, questions, etc. Looking forward to hearing from the community!
We’re Peter and Jiaxin, and we’re building Promi ( https://ift.tt/CbhB1OX ). Promi uses AI to optimize retail ecommerce discounts across products and customers (think new customer discounts, clearance sales, holiday sales, etc.). Here’s a quick video overview: https://www.youtube.com/watch?v=SHTw9VH8bCw Discounts have traditionally been a bit of the ‘wild west’ of pricing. Optimization techniques at even the largest merchants are heavily manual. Product level discounting decisions are distributed among operations or category managers, and store-wide discounts are often set by marketers. Typically they rely on order history, look at competitor discounts, or find prior discount performance to inform their decision. But there’s not a lot of science behind choosing the discount, and teams don’t have the time or means to optimize it at a granular level - i.e. by product or customer groups. We believe AI can better solve this problem by setting the most appropriate discount value, varying that discount across products, and personalizing that discount across users in order to achieve various goals. AI models can leverage more data (e.g. item conversion rate, profit margin, customer referral URL, device type) and update more frequently than is realistically possible to do manually. Our approach will also allow us to generate discounts for relatively small merchants. We use models (layering traditional NLP models and custom LLMs) to build a large-scale knowledge graph to gather similar products across merchants in order to build profiles around different clusters of products. Those profiles help us build solutions catering towards subscale shops which traditionally do not have an optimal pricing strategy. Our first AI product focuses on liquidating inventory, and uses a store’s historic transaction and sales data to jump start training our model and generating discounts. Our model predicts the discount required to increase conversion rate by the proper amount to liquidate the inventory by the desired timeframe. We then monitor the conversion rate and (if we have statistical significance) make frequent adjustments as needed. We’ve got a full roadmap of new model approaches, including personalization and new objective functions (e.g. profit instead of liquidation) to fit more discount use cases. How we got here: Jiaxin and I are coming from Uber, where I led product for the discount team across Eats and Rides. We launched several analogous AI features at Uber and saw just how impactful they can be for structuring discounts. For example, we had issues with deploying our ML models for automated discounts in smaller markets because of the quantity of data required to train those models. We pivoted to a 'global model' that used data across countries to significantly reduce the amount of data required in any one country. That model performed even better than country-specific models, showing us that there were very reproduce-able trends in improving discount performance. If you run or know someone who runs a Shopify store, you can download and play around with our app here: https://ift.tt/nmZKzIL We’d love feedback, thoughts on other use cases for discounts + AI, questions, etc. Looking forward to hearing from the community! 0 https://ift.tt/IlPHE9s 2 Launch HN: Promi (YC S24) – AI-powered ecommerce discounts
2 by pmoot | 0 comments on Hacker News.
We’re Peter and Jiaxin, and we’re building Promi ( https://ift.tt/CbhB1OX ). Promi uses AI to optimize retail ecommerce discounts across products and customers (think new customer discounts, clearance sales, holiday sales, etc.). Here’s a quick video overview: https://www.youtube.com/watch?v=SHTw9VH8bCw Discounts have traditionally been a bit of the ‘wild west’ of pricing. Optimization techniques at even the largest merchants are heavily manual. Product level discounting decisions are distributed among operations or category managers, and store-wide discounts are often set by marketers. Typically they rely on order history, look at competitor discounts, or find prior discount performance to inform their decision. But there’s not a lot of science behind choosing the discount, and teams don’t have the time or means to optimize it at a granular level - i.e. by product or customer groups. We believe AI can better solve this problem by setting the most appropriate discount value, varying that discount across products, and personalizing that discount across users in order to achieve various goals. AI models can leverage more data (e.g. item conversion rate, profit margin, customer referral URL, device type) and update more frequently than is realistically possible to do manually. Our approach will also allow us to generate discounts for relatively small merchants. We use models (layering traditional NLP models and custom LLMs) to build a large-scale knowledge graph to gather similar products across merchants in order to build profiles around different clusters of products. Those profiles help us build solutions catering towards subscale shops which traditionally do not have an optimal pricing strategy. Our first AI product focuses on liquidating inventory, and uses a store’s historic transaction and sales data to jump start training our model and generating discounts. Our model predicts the discount required to increase conversion rate by the proper amount to liquidate the inventory by the desired timeframe. We then monitor the conversion rate and (if we have statistical significance) make frequent adjustments as needed. We’ve got a full roadmap of new model approaches, including personalization and new objective functions (e.g. profit instead of liquidation) to fit more discount use cases. How we got here: Jiaxin and I are coming from Uber, where I led product for the discount team across Eats and Rides. We launched several analogous AI features at Uber and saw just how impactful they can be for structuring discounts. For example, we had issues with deploying our ML models for automated discounts in smaller markets because of the quantity of data required to train those models. We pivoted to a 'global model' that used data across countries to significantly reduce the amount of data required in any one country. That model performed even better than country-specific models, showing us that there were very reproduce-able trends in improving discount performance. If you run or know someone who runs a Shopify store, you can download and play around with our app here: https://ift.tt/nmZKzIL We’d love feedback, thoughts on other use cases for discounts + AI, questions, etc. Looking forward to hearing from the community!
We’re Peter and Jiaxin, and we’re building Promi ( https://ift.tt/CbhB1OX ). Promi uses AI to optimize retail ecommerce discounts across products and customers (think new customer discounts, clearance sales, holiday sales, etc.). Here’s a quick video overview: https://www.youtube.com/watch?v=SHTw9VH8bCw Discounts have traditionally been a bit of the ‘wild west’ of pricing. Optimization techniques at even the largest merchants are heavily manual. Product level discounting decisions are distributed among operations or category managers, and store-wide discounts are often set by marketers. Typically they rely on order history, look at competitor discounts, or find prior discount performance to inform their decision. But there’s not a lot of science behind choosing the discount, and teams don’t have the time or means to optimize it at a granular level - i.e. by product or customer groups. We believe AI can better solve this problem by setting the most appropriate discount value, varying that discount across products, and personalizing that discount across users in order to achieve various goals. AI models can leverage more data (e.g. item conversion rate, profit margin, customer referral URL, device type) and update more frequently than is realistically possible to do manually. Our approach will also allow us to generate discounts for relatively small merchants. We use models (layering traditional NLP models and custom LLMs) to build a large-scale knowledge graph to gather similar products across merchants in order to build profiles around different clusters of products. Those profiles help us build solutions catering towards subscale shops which traditionally do not have an optimal pricing strategy. Our first AI product focuses on liquidating inventory, and uses a store’s historic transaction and sales data to jump start training our model and generating discounts. Our model predicts the discount required to increase conversion rate by the proper amount to liquidate the inventory by the desired timeframe. We then monitor the conversion rate and (if we have statistical significance) make frequent adjustments as needed. We’ve got a full roadmap of new model approaches, including personalization and new objective functions (e.g. profit instead of liquidation) to fit more discount use cases. How we got here: Jiaxin and I are coming from Uber, where I led product for the discount team across Eats and Rides. We launched several analogous AI features at Uber and saw just how impactful they can be for structuring discounts. For example, we had issues with deploying our ML models for automated discounts in smaller markets because of the quantity of data required to train those models. We pivoted to a 'global model' that used data across countries to significantly reduce the amount of data required in any one country. That model performed even better than country-specific models, showing us that there were very reproduce-able trends in improving discount performance. If you run or know someone who runs a Shopify store, you can download and play around with our app here: https://ift.tt/nmZKzIL We’d love feedback, thoughts on other use cases for discounts + AI, questions, etc. Looking forward to hearing from the community! 0 https://ift.tt/IlPHE9s 2 Launch HN: Promi (YC S24) – AI-powered ecommerce discounts
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