Launch HN: Promi (YC S24) – AI-powered ecommerce discounts
10 comments
An actually cool use case of AI. Congrats on the launch.
Some 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?
Some 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?
Thank you!
1) Yes, personalization might have a bit of an experience tradeoff. We can try to mitigate this with messaging like "flash discount" or "just for you". But we also want to make it optional for merchants. In my experience there's still a lot of improvements from other things too like dynamically adjusting discounts and varying the discounts across products
2) One of the takeaways from my time at Uber was that certain predictors of discount efficiency held pretty constant across markets. A couple were conversion rate (if more ppl were going to convert without the discount, it's less efficient to give the discount) and profit margin. We're betting that we can train a model to generalize these trends across stores to create a bump in performance.
3) We'll be kicking off our first case study with a customer in a couple weeks. At Uber, just varying the discount across merchants on Uber Eats improved the profitability of the discount by 40% (mostly because we were able to take advantage of differences in commission rates across merchants).
1) Yes, personalization might have a bit of an experience tradeoff. We can try to mitigate this with messaging like "flash discount" or "just for you". But we also want to make it optional for merchants. In my experience there's still a lot of improvements from other things too like dynamically adjusting discounts and varying the discounts across products
2) One of the takeaways from my time at Uber was that certain predictors of discount efficiency held pretty constant across markets. A couple were conversion rate (if more ppl were going to convert without the discount, it's less efficient to give the discount) and profit margin. We're betting that we can train a model to generalize these trends across stores to create a bump in performance.
3) We'll be kicking off our first case study with a customer in a couple weeks. At Uber, just varying the discount across merchants on Uber Eats improved the profitability of the discount by 40% (mostly because we were able to take advantage of differences in commission rates across merchants).
Very cool. Saw you were H ‘10 - go crimson! Best of luck!
I have a few dumb questions for people who really understand how this stuff works:
- 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.
- 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.
1) What we're really doing with this first product is predicting how price impacts conversion rate. That's been a relatively simple thing to measure in my experiences. It's more difficult to do things like predict a customer's probability of buying based on their order history.
2) Yes so we don't have a case study comparing us to manually setting discounts, but the task gets pretty time consuming quickly if you want to update the discount daily (or more frequently) and personalize the discount (which is one of the features we're planning on adding).
2) Yes so we don't have a case study comparing us to manually setting discounts, but the task gets pretty time consuming quickly if you want to update the discount daily (or more frequently) and personalize the discount (which is one of the features we're planning on adding).
Re: small merchants. Thats what I dont get. The companies that need this are the big ones not the small merchants with relatively few products they wish to discount. They’re better off investing time and effort to making more sales not nickeling and diming with discounts
> They’re better off investing time and effort to making more sales not nickeling and diming with discounts
But discounts are one way to get more sales! There are plenty of mom-and-pop merchants that have to compete with large retailers and would benefit from a sophisticated discount system to drive higher volumes, but they just don't have the resources or the experience to implement it themselves.
Source: I know the owners of a brick and mortar garden store that struggles with this.
But discounts are one way to get more sales! There are plenty of mom-and-pop merchants that have to compete with large retailers and would benefit from a sophisticated discount system to drive higher volumes, but they just don't have the resources or the experience to implement it themselves.
Source: I know the owners of a brick and mortar garden store that struggles with this.
Your global model approach from Uber is clever. Have you considered how to communicate this benefit to smaller merchants who might be skeptical about having enough data?
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?
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?
We probably haven't done a great job communicating this to smaller merchants yet.
Re personalization - We'd plan to make it an optional field for merchants to use, so if they are worried they don't necessarily need to enable personalization. But I personally think a fair bit can be explained with on-site copy like "flash sale".
The 1% was meant to already bake in some flexibility for smaller merchants haha. But if we get that feedback we might consider more of a tiered model.
Re personalization - We'd plan to make it an optional field for merchants to use, so if they are worried they don't necessarily need to enable personalization. But I personally think a fair bit can be explained with on-site copy like "flash sale".
The 1% was meant to already bake in some flexibility for smaller merchants haha. But if we get that feedback we might consider more of a tiered model.
- Where are you getting the data to train the model? You need initial historic data of different products including pricing information and inventory. I am assuming you are buying scraped data. Don't have an opinion just curious. The words data and AI feels to be lacking context.
- Will you also be looking into real time competitor pricing to make discounts more competitive?
- Will you also be looking into real time competitor pricing to make discounts more competitive?
When a shop installs our app we're able to pull this data from Shopify for that shop.
We aren't looking into competitor discounts, but might be something for the future!
We aren't looking into competitor discounts, but might be something for the future!
This is a perfect example of automated price management.
I think you need to better communicate the causality between applying your model and increased profits to justify your 1% commission.
I think you need to better communicate the causality between applying your model and increased profits to justify your 1% commission.
Good feedback. Ideally we have a few case studies actually showing the impact we generate, we just have to complete a few of those first.
We've been open to working with customers on a trial basis also. This price is mostly based on industry comps for other optimization tools.
We've been open to working with customers on a trial basis also. This price is mostly based on industry comps for other optimization tools.
What is the feedback time delay on these pricings?
Do retails get/pass on to you hourly/second-ly aggregate data or what?
Do retails get/pass on to you hourly/second-ly aggregate data or what?
Right now the updates occur daily, we're planning on building a bit more intelligence into the update cadence over time (e.g. once we see we have a stat sig read on how the price change impacted conversion).
Retailers don't approve the discount changes, but they do provide guardrails like maximum discount value to avoid us carving into their margins too much. They can also log in and review / update discounts at any time in our app.
Retailers don't approve the discount changes, but they do provide guardrails like maximum discount value to avoid us carving into their margins too much. They can also log in and review / update discounts at any time in our app.
Not necessarily relevant for you, but expect legislation for algorithmic price collusion:
https://www.theatlantic.com/ideas/archive/2024/08/ai-price-a...
https://www.propublica.org/article/yieldstar-rent-increase-r...
https://www.theatlantic.com/ideas/archive/2024/08/ai-price-a...
https://www.propublica.org/article/yieldstar-rent-increase-r...
Looks very relevant. If the same service (e.g same instance of the model, or even different instances of the same model) is provided to more than one client, I'd guess a prosecutor might reach for it.
Easiest but most costly way this could be avoided is by creating new models for each client using the client's own specific data and keeping the data and models fully isolated for each client.
If derivative insights are gathered across all models, it'd have to be one-way informing e.g business decisions for the overall company rather than informing how the models themselves operate.
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edit: "We pivoted to a 'global model' that used data across countries to significantly reduce the amount of data required in any one country."
This might paint a bullseye on their back, but I'm a security and risk person, not a lawyer.
Easiest but most costly way this could be avoided is by creating new models for each client using the client's own specific data and keeping the data and models fully isolated for each client.
If derivative insights are gathered across all models, it'd have to be one-way informing e.g business decisions for the overall company rather than informing how the models themselves operate.
---
edit: "We pivoted to a 'global model' that used data across countries to significantly reduce the amount of data required in any one country."
This might paint a bullseye on their back, but I'm a security and risk person, not a lawyer.
There's a lot of existing legislation around the world about discounting as well.
In many countries to be able to discount you need to have sold an item at full price for a certain amount of time, and you can't discount for more than another amount of time (i.e. to prevent perpetual discounting).
In some countries you can't sell the same item at different prices for different customers. You can issue different discount codes to different customers, but those would likely need to be widely applicable so that it's not just different pricing in disguise.
Having worked in an ecommerce company, I'm excited by the prospect of better automated pricing tools and tools that can do things like target sell-through by a particular date. However as a consumer and keen advocate for consumer rights, I'm concerned about a future where every last penny is eeked out of consumers by automated systems designed to identify ways they can be exploited with pricing.
In many countries to be able to discount you need to have sold an item at full price for a certain amount of time, and you can't discount for more than another amount of time (i.e. to prevent perpetual discounting).
In some countries you can't sell the same item at different prices for different customers. You can issue different discount codes to different customers, but those would likely need to be widely applicable so that it's not just different pricing in disguise.
Having worked in an ecommerce company, I'm excited by the prospect of better automated pricing tools and tools that can do things like target sell-through by a particular date. However as a consumer and keen advocate for consumer rights, I'm concerned about a future where every last penny is eeked out of consumers by automated systems designed to identify ways they can be exploited with pricing.
I believe the term you're looking for is consumer surplus, and the economic concept they're enabling is effectively perfect price discrimination.
[deleted]
Is 1% too greedy?
Before you say, “Stripe:” listen, they’re too greedy too!
Before you say, “Stripe:” listen, they’re too greedy too!
> How we got here: Jiaxin and I are coming from Uber, where I led product for the discount team across Eats and Rides.
So it's surge pricing but masked as discount... but hey, it'll work for the consumption-driven world.
So it's surge pricing but masked as discount... but hey, it'll work for the consumption-driven world.
[flagged]
YC hasn't changed. This kind of thing happens with every major tech wave, and this is the majorest one in a long time.
Right now the waters are all muddy with hype. The sediment will eventually settle and we'll find out which lasting companies have emerged - same as dotcom eventually turned into Amazon etc.
(I/we didn't flag your post btw)
Right now the waters are all muddy with hype. The sediment will eventually settle and we'll find out which lasting companies have emerged - same as dotcom eventually turned into Amazon etc.
(I/we didn't flag your post btw)
In my experience there's also a ton of B2B demand for AI right now. I've been working with AI in this space for the past 4 years and the desire from merchants to use AI tools has really ramped up. Supply follows demand to a certain extent.
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://apps.shopify.com/promi-discounts
We’d love feedback, thoughts on other use cases for discounts + AI, questions, etc. Looking forward to hearing from the community!