# Retail ranking should optimize purchases, not just relevance

Markdown copy of https://realalpha.app/s/retail-ranking-should-optimize-purchases-not-just-relevance/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Zoe Weil](https://realalpha.app/people/zoe-weil/) · Zoe Weil is co-founder and CEO of Sequen.
- Host: [Sam Charrington](https://realalpha.app/people/sam-charrington/) · Host, The TWIML AI Podcast
- Show: The TWIML AI Podcast
- Episode: [Optimizing AI Recommendations for Revenue with Sequen Founder Zoe Weil](https://realalpha.app/v/MAABmDB51LY/), Oct 15, 2026
- Moment: 4:17 to 11:52 of the episode (8 min), playing on https://realalpha.app/s/retail-ranking-should-optimize-purchases-not-just-relevance/; the episode from there: https://realalpha.app/v/MAABmDB51LY/?t=257

## Quote

> “We are not at all trying to help people find things. We're trying to **influence their behavior** in a moment of time when they have high intent.”
> — [Zoe Weil](https://realalpha.app/people/zoe-weil/) at [8:15](https://realalpha.app/v/MAABmDB51LY/?t=495) · The TWIML AI Podcast

## Takeaways

1. Retailers can’t simply copy data-rich social platforms’ playbooks.
2. Scroll speed, hover time and backtracking reveal shopper intent.
3. Personalized rankings must react to live signals at very low latency.

## About this segment

Zoe Weil argues that social platforms can shape behavior because they have far more user data than retailers. Sequen instead uses live session signals to influence high-intent shoppers and serve rankings in under 50 milliseconds.

From “Optimizing AI Recommendations for Revenue with Sequen Founder Zoe Weil”, Oct 15, 2026. [Watch the full episode](https://realalpha.app/v/MAABmDB51LY/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[4:21](https://realalpha.app/v/MAABmDB51LY/?t=261) however in a commercial context you know modern recommender systems if you think about tiktok instagram and the like they're not really recommending content anymore and they're definitely not trying to help people find things instead they're bending the end user's will in subtle ways over time to make them want things that they otherwise wouldn't want right so that destination vacation, you know, the latest fashion that you might not need, you know, whatever it is that you can think of that people end up just, you know, buying on Instagram or TikTok. They do that not because Instagram and TikTok are, you know, really good at helping you find what you're looking for. No, they're very, very good at influencing your behavior. And the reason that they're able to do that is that billions of people use their apps and are frankly

[5:18](https://realalpha.app/v/MAABmDB51LY/?t=318) addicted to those apps. They open their apps, you know, 10 to 21 times a day. And when billions of people do that, you have an insane amount of data on them. And so you're able to create these incredibly immersive, I would even say addictive experiences that are hyper-personalized and dynamic and in fact the apps react in real time to uh the user's feedback right so even you know you sam if you what you see on the site or the app in the day throughout the day will vary based on your interactions with the app but the truth is that consumer companies regardless of how large they are, you know, regardless of how many customers they have. Wayfair is a good example. Priceline is another really good example. These are very large companies that have incredibly large user bases, do tens of billions of dollars of online transactions. But again, people don't

[6:19](https://realalpha.app/v/MAABmDB51LY/?t=379) use Wayfair the same way that they use Instagram or TikTok. And so regardless of how big you are as a consumer business, you know, you are going to be data starved. And so in an environment where you don't have as much data on the user, where people are not addicted to your app, how do you influence their behavior in a moment of high intent to increase their basket size, increase their average order value, to increase their engagement on your site? Again, these aren't like big changes so on wafer for example if if you're going to buy a hundred dollar couch it's not like sequin can come in and get you to buy a five hundred dollar couch and also a thousand dollar coffee table the point is As I can get people to buy $102, but I can do it consistently and at scale.

[7:17](https://realalpha.app/v/MAABmDB51LY/?t=437) And when you do that, that translates to forexing their target profit for the year in less than a month. Upsell, cross-sell, like we've been, you know, doing this from a data science perspective, pre-data science. You know, we've been doing this for a long, long time, trying to get people to buy more. what's different about the approach the tldr is as i was saying earlier and in fact for the entire history of this field of research people have been trying the wisdom has been if i can make it easier for people to help to find things then by extension i can get them to convert better that has been the prevailing wisdom forever every company that's ever existed in this domain has been trying to solve search and relevance from the perspective of search and relevance, make it easier for people to find things faster.

[8:18](https://realalpha.app/v/MAABmDB51LY/?t=498) That is not what Sequin does at all. We are not at all trying to help people find things. We're trying to influence their behavior in a moment of time when they have high intent. How do we do that? How do we go about doing that? Which is, I think, the meat of the matter. So we started the company with a black box solution that was powered by what we call large event models. So you can think of it as, first of all, it's not a large language model.

[8:44](https://realalpha.app/v/MAABmDB51LY/?t=524) We actually don't use large language models anywhere. That's not our business whatsoever. And we don't use generative AI either. And in any case, so we started with large event models. And the idea behind large event models is that the same way that large language models are able to generalize to text, including, you know, texts that they haven't seen before ever. Large event models are able to generalize to human behavior and streams of events all in real time.

[9:18](https://realalpha.app/v/MAABmDB51LY/?t=558) And creating something like that is incredibly difficult to do for a number of reasons. One, you need to be able to create extremely ultra-low latency systems that are able to keep track of data at a very, very fine-grained level, right? So I'm not only talking about clicks and conversions and the traditional good stuff, although we use that as well, but I'm talking about hovers, scrolls. I'm actually talking even deeper than that.

[9:47](https://realalpha.app/v/MAABmDB51LY/?t=587) The speed of a scroll, right? The dwell time on a hover. How many times a user goes back and forth. Creating systems that are able to digest data in, frankly, nanoseconds, transform this data also in nanoseconds, put it through a model, several layers of ranking models in milliseconds, and serve dynamic, re-ranked items to a user as they're browsing a site, all in under 50 milliseconds for thousands of requests per second, is incredibly difficult to do.

[10:27](https://realalpha.app/v/MAABmDB51LY/?t=627) In fact, we created what we refer to internally as sequin ranking clusters to be able to even make this feasible at all. And so there was a lot of work done there, not only from an algo perspective, but also from a data engineering perspective, feature engineering perspective, specifically for ranking, and a ton of MLops work and infrastructure work that was done. It's hard because you need to do three things that are very, very difficult to do on their own.

[11:00](https://realalpha.app/v/MAABmDB51LY/?t=660) You need to have high-fidelity, leakage-free data pipelines, which is a really difficult problem on its own. You need to have... Obviously, models that are personalized to every user and continue to improve on those models. And then you need to be able to serve them fast enough, such that rankings are able to react to live user feedback and signals. So building this system at all requires you to do these three things really, really, really, really well.

[11:32](https://realalpha.app/v/MAABmDB51LY/?t=692) And there are very few people on the planet that can do this for the explicit objective of generating outcomes for businesses. Right? And then, so that's one part of the problem that sequence solves.