Podcast: The Tech Unifying Search, Feed, Ads, and Promotions for Marketplaces
Neil: Welcome to the tech talks daily podcast, where you can learn and be inspired by real-world examples of how technology is transforming businesses and reshaping industries in a language everyone can understand. Here is your host Neil C Hughes. Welcome back to the tech talks daily podcast. Now confession time:
Neil: Being out in a field in Glastonbury for five days and sitting out in the sun today, I’m recording today’s podcast with a slightly sunburnt face. So who knows what today’s guest thought when he saw me on camera, his name is Andrew Yates and he appeared on my radar. When I originally read about their backstory, which involved Andrew and Dan meeting at Pinterest, where they helped build ad systems at Facebook and Google and realized.
Neil: They began to think bigger. So they started Promoted.ai to match every buyer with every seller across every app. And they’ve been on an amazing journey, which has taken them through Y Combinator, not to mention being the core function of some of the biggest companies in tech, which are Google, Facebook, Amazon, et cetera, all wash down with a great story. But enough from me, buckle up and hold on tight as I beam your ears all the way to San Francisco, where Andrew Yates is waiting to share his story.
Neil: So a massive welcome to the show. Can you tell our listeners a little about who you are and what you do?
Introduction to Promoted.ai: Selling Discovery
Andrew: Hi. Neil. I’m Andrew Yates, I’m CEO and founder of Promoted.ai. Promoted.ai, we optimized marketplaces. For example, if you have ever used Airbnb or Amazon, we sort search and feed and promote the best listings at the top to increase revenue.
Neil: And all those services you just mentioned, people know all about, but what I try and do on this podcast is let people have a peek behind the curtain and look under the hood.
Neil: So today I invited you on the podcast to learn more about engineering and optimizing marketplaces. So just to set the scene, can you tell me more about how data engineering is the core function of some of the biggest companies in tech, such as Google, Facebook, and Amazon? Well, so many others you can name too, but can you expand on that for me?
Andrew: Yeah, absolutely. For search or presentation, when you are selling discovery, really all you have is that data you have, the measurement you are selling: did someone engage with it? Did seeing this thing cause somebody to take some action later? etc, and that’s the business that runs Silicon Valley.
Andrew: That’s the core business of Google. That’s the core business of Facebook. That’s the core business of Amazon and retailers. You just have a screen and some pixels. Someone did something and you log it, and somehow that transforms that into trillion dollar market.
Importance of Measurement in Commercial Media
Andrew: To get more concrete into this, it’s being able to maximize the efficiency of these marketplaces or eCommerce apps or social media. The first step of anything is measuring it correctly. And on one side, that’s a lot of data. That’s everything you looked at, did you engage with it? There’s a lot of streaming data making that available. Lots of infrastructures involved with that, then there’s the uncertainty part of it, and this is where it starts to get interesting where if you’ve ever run an ad campaign, maybe if you had small business or for work, there’s a question of did this ad cause somebody to take an action.
Andrew: Now you’ve added uncertainty into your data pipeline, now you have to deal with these things like, what if you don’t have a way to join records, but you have to guess, and that really magnifies the complexity of what these data pipelines have to do, and they still have to be real-time. And if you are in the advertising business, they’re auditable. If you get the Wall Street Journal if you’re in the United States, or I guess anywhere, they love publishing when some major ad network or Facebook or somebody misinterprets or somehow miscomputes a metric. That’s data infrastructure and data engineering, and it’s the order of billions of dollars, and it’s a big deal, so that’s the philosophy of data engineering and processing.
Andrew: And this is all separate by the way, from like the data privacy issues. I kind of wanna separate all of that privacy consideration, which is also very important. But I think it sucks up all the oxygen in the room regarding the technical discussion of what the technology is doing, and what the challenges are versus the challenges of just collecting all this data, aggregating, joining it, and dealing with uncertainty.
Optimizing What You Correctly Measure
Andrew: And then once you have this measurement, then you can either sell it, in terms of discovery, impressions, clicks, and conversions, and you can also optimize against it. So that’s getting into the other piece of uncertainty, which is uncertainty about the future: did things happen in the past? If you can accurately measure them, it helps you predict what’s likely to happen in the future. And that’s what all of these recommendation engines and search engines are automatically learning about. This was a good result for this query, or this was a good entry to put into a feed for this user that measurement is driving the optimization side. So these are the two things that we focus on, and then the promotion side and the ad tech side, that’s like a layer on top of the maximization, which is what if you wanna sell the ability to be discovered.
Andrew: Our philosophy, first you need to have a really good concept of here’s what good matches are and here’s the value of it. And if you want to change that, here’s how much it costs. And as you said a few moments ago, the subjects have ads that do have a bad reputation around privacy, et cetera.
“Ads” as Seller Take-Rate Optimization
Neil: It does circle all the energy out in the room. It’s already well documented and talked about just about everywhere, so it is nice to have a different approach today. Before you came on, I was reading your belief that another way: Thinking of ads, as a way to raise prices on sellers on a much fairer basis. Can you tell me more about that?
Andrew: Yeah. Think of it as like Amazon, for example, I think this is a little bit different than a brand advertisement where you have a blog or your social media and you are just allowing people to put banner ads.
Andrew: This is more of like the abstraction of, for example, Amazon or Doordash where they’re doing paid promotions and it’s things that are already for sale in their marketplace. Therefore, the way to think of this from the seller’s perspective is that it's math, and it’s money in and it’s money out. Here’s how much I made in revenue, and here’s as much I had to pay in costs.
Andrew: Already, the marketplace’s core business is a fraction of sales on Amazon or Airbnb, or any of these e-commerce or marketplaces to take a fraction of the sale, plus like some sort of merchandising fee or et cetera. One way for the marketplace to become more profitable is just to say: before we were charging 20% of the sale, now it’s 25%, or now it’s an extra dollar fee. You just say: hey everyone, you need to pay more. Well, if it was so easy, they would just keep raising the prices forever, why not just make infinite money, right? Why not a hundred percent, right?
Andrew: Well because it’s inefficient. Some sellers are willing to pay higher and more fees to the marketplace. Other sellers will pay less. They’re not gonna tell you outright. They’re not gonna say, hey I raise my hand, and I would love to pay more. No, they want to pay the minimum amount to maximize their profits.
Andrew: So you can think of ads as a way to optimize for that take rate for sellers. Some sellers are effectively paying, let’s say 30% and other sellers are effectively paying, let’s say 20% and it’s not like you get to choose a select box. Like, I choose 20%, and I choose 30%. It’s more like, okay, you make trade-offs.
Raising Prices on Sellers Requires Delivering More Value to Sellers
Andrew: So if you pay a higher fraction to the marketplace, you need to get something back in return. What you get back in return is usually more volume, so you’re trading off. Variability is another one like you’re trading off margins for volume and prominence. And if you think of it that way, then when you see, let’s say Amazon, you see these numbers, I forget the latest number, but billions of dollars are generated by ads on Amazon. And then the rest of the business is just sort of a lost leader. I know, that’s funny to think about.
Andrew: I know it’s funny to think about, could they just do ads? Of course not. What’s happening here is that they’ve created their ad system like it’s profit optimization. Lo and behold, if you just attribute profits to the last layer of the profit optimizer, Sure, It looks like it’s all being driven by that, but if you take a step back and use some common sense, it’s like, no, no, no.
Andrew: This is the last optimizer for profits and attributing it to this system, so I think once you start zooming out a little bit to let’s say Amazon case or these other big marketplaces where you look at their ads business and it’s driving all of these quote-unquote huge profits, it’s more like that’s the whole business, and that’s just the last layer where they’re optimizing. Here’s how much profit you can pull out in the most efficient way.
Silos in Search, Ads, and Merchandising Engineering Teams
Neil: And if we were to take a look under the hood, can you tell me a bit more about how search and discovery teams are built at these large companies that we’re talking about?
Andrew: Typically there exists a search team. There’s an ads team and there’s a merchandising team and there’s a data team and maybe a data science team on the side. It’s organically built, and people have their areas. The challenge here though is you only have one marketplace and one e-commerce app, and these teams are competing for the same user’s attention.
Andrew: How these teams are built: frequently, you have a search team that’s really good at retrieval, and they’re really good at query relevance. You’ll have an ads team that’s really good at exact measurements, like data streaming, and getting those metrics in place. And then the merchandising team may have a lot of really fantastic, different formats and different ways of like controlling the product.
Andrew: But what you really want is all of those things combined into one function, and some companies are really good at this. Like Amazon or Facebook have been, and some other companies are still developing their expertise.
Neil: And as someone that’s right in the heart of this space, you’re probably seeing it continuously evolve. And it’s gonna continue to do that too. So I’m curious how do you see the future of commercial search and discovery tech and where it’s heading? Is there anything that stands out to you?
Optimizing Phone Screens is Increasingly Critical to Commerce
Andrew: It’s certainly becoming much more important because people are on their phones all of the time. Yeah. As retail becomes more and more abstract and electronic, and it’s just your phone deciding what goes on your phone screen is incredibly important. That’s your portal to people’s attention and their wallets. Ultimately, unlike maybe the past where it was retail, you walk into the store and own that specific location and you come into the store and the experience of it.
Andrew: So it’s like taking some of the ideas from old world retail and moving them to phone and some of them translate well and some of them are totally new. I don’t think the core philosophy of measuring people’s attention and doing this economic trade-off between the seller’s interest, the buyer’s interest, and the platform or the marketplace interest. I think philosophically that’ll continue to remain the same. The trend that I see is, especially recently, that people are much more interested in that unified optimization. Like you can’t get away with free money for growth forever. When is it going to be profitable? And that’s when the trend that we’re seeing is people are a lot more serious now about whether or not this is going to be a sustainable business.
Andrew: And not only that, but users are buyers, households are much more serious about whether or not it’s something that they really need to buy. For sellers, it’s like, do we have to raise our prices? How and what are we gonna do? Ads are too expensive, and our costs are going up.
Andrew: We’re really serious, so I think the gravity of being able to solve these sorts of profitability economic equations has become much more important recently. Whereas I’d say a year ago, it was like a peaked bubble time and money is free, so you can not optimize. It’s pretty easy when you have like these three-sided optimizers, let’s just focus on one of these sides. Growth and Maximum GMV. Let’s say the chickens have come to roost and people are much more serious about profits at scale as opposed to just scale at scale.
Ads Engineering at Facebook, Google, and Pinterest
Neil: We will have people listening from a variety of backgrounds, so for those people outside of the space, could you just offer a very brief overview on how ad engineering works behind the scenes, and also for the ad engineers listening, are there any tips or advice that you would offer on how to design great ad marketplaces?
Andrew: We have a unique perspective that there’s a whole multi-decade area of ad tech that we really don’t have any experience with or use like real-time bidding or like banner ads, web ads. I know very little about this, frankly, and not that interested. Our perspectives here are what if you work at Facebook, Google, or Pinterest. Dan and I, my co-founder, were EMs together at Pinterest in Pinterest ads. It’s a different philosophy in the sense that you’re thinking in terms of the entire experience, like the entire app versus the real-time bidding and transacting on consumer data.
Andrew: It’s much more focused on search and discovery plus, it’s like you’re solving recommenders without the ads. Forget the ads part, it’s just search and discovery for a marketplace. You also have to be accountable for measuring it correctly, communicating those metrics to sellers and it’s other people’s money and you have to communicate your decisions and dollars.
“Ads” as Adversarial Search and Discovery with Other People’s Money
Andrew: So from a technology perspective here, the big difference between ads engineering from search and discovery and ordinary search and discovery is that it’s other people’s money and all of your decisions are denominated in dollars and there are trade-offs. It’s not just maximizing, it’s like you have this, and you try to maximize one part, but that minimizes another part, and you have to do this balancing.
Andrew: From an engineering perspective, the philosophy of taking the learnings from Facebook ads engineering, Google ads engineering, or Pinterest ads engineering which has much more emphasis on correct measure versus directionally correct. Like A/B experimentation where every data point needs to be correct. Because people measure it, and that’s what you sell.
Andrew: On the other hand, it’s not just the presentation of what you show. It’s not just you showing the right thing. It’s how much did it cost the price? How much did showing this do? Was it a profitable decision? Not just a good decision and that these two layers of complexity. It’s our superpower at Promoted for being able to do such a fantastic job of search and discovery because the data bar is higher, so we have better quality data, and we can do better quality optimization. That’s longing the short of it, but it’s also like how you can power performance ads at top companies.
Promoted’s Plan to Match Every Buyer and Seller Across Every App
Neil: So I guess the big question is how is Promoted.ai planning to better match every buyer with every seller across all marketplaces on the web or mobile, et cetera. Can you expand on that and how you’re doing that? Rather than say, hey, we have a network, and join our network.
Andrew: That’s not what we’ve been doing. In fact, we don’t have an ad network today. What we have are fantastic top marketplaces that we help optimize and measure as infrastructures. Our philosophy is you need to have your own house in order before you can start opening up your house to others. And that starts with measuring everything happening all in your current app, like getting the right data, then maximizing your own conversions and sales.
Andrew: Also, doing your own promotions internally and only when you can do it for yourself, then you can open up and start taking your inventory and put it elsewhere, or bring in inventory from elsewhere. Because that way, this is incrementally an improvement on top of the best that you could already be doing.
Andrew: So our strategy here has been to work with the absolute best marketplace’s best engineering teams. Get them to the same standardized level of sophistication and quality. Now you have a standardized interface for measurement and optimization that you can start networking together on the backend to do these cross-promotions. That’s our ultimate vision here.
Promoted and Y Combinator Winter 2021
Neil: And before you came on the podcast today, I was doing a little bit of research and I noticed that Promoted.ai got into Y Combinator. So I’ve gotta ask: What was that experience like? Were there any big takeaways from that?
Andrew: Yeah. I highly recommend Y Combinator. Generally. It’s like, should I go to Harvard? And if you have to ask, then you should probably go for it. If you know better — like you actually wanna go to CMU or Tsinghua or something, then yeah, go to where you want. You don’t need to go to YC.
Andrew: But otherwise, it’s a good default place to go if you don’t know any better. A great community and a lot of support. I think their biggest advantage is that they accelerate you past the crappiest and most fragile part of your business, and also help you avoid some of the lamest mistakes that magnify. It’s like kind of your Genesis, these sorts of early mistakes just stick around forever.
Andrew: They help you smooth that out. It’s expensive, I do wanna call out that. It’s not free, although they upped their other offer recently, which is awesome.
How Do I Get into Y Combinator?
Andrew: Hey, Should I join Y Combinator? I’m sure that’s a pretty popular question. Yes. And: “How do I get into Y Combinator?” is usually the follow-up question. “What’s your advice?” Say fewer words… and have a convincing story about why you specifically would be successful. If you don’t have that answer, you should make one. If you don’t know the answer, then they’re not gonna know, and that’s not good. Right? Have an answer ready.
Y Combinator During COVID
Andrew: And then from my experience personally, we did it in peak COVID, so we were winter 2021. No one really enjoyed COVID, I don’t think, and we didn’t enjoy it. The experience was different. It was extremely efficient, but it wasn’t fun. What you did is that you just sat in a chair and faced your computer for a year and you didn’t move and you just worked.
Andrew: No parties, no networking. If you need to meet someone for raising money, you send an email and they do a zoom call and they’re like: How much do you want? You say, I want X and I’m like, okay or no, and no coffee chat. There are advantages to this, and there are disadvantages to this. My experience with Y Combinator won’t be replicated and definitely hope it won’t ever be because that was the peak lockdown period, and it was a weird time.
Andrew: There were special advantages and disadvantages to that. That being said, Y Combinator is now opened up and it’s in person now, and you can meet people and they’re going back to some of their classes and saying that they can meet you in person now, which is great.
Andrew: By the way, by the time this goes out, we’ll have already announced. I was just talking to TechCrunch right before this, and we’re gonna announce our continuation of funding from Y Combinator. Y Combinator invested another several million dollars into our company.
Andrew: So that’s another thing about Y Combinator is that it’s not just a program and that’s the end, Y Combinator logo forever. No. It’s forever. It’s a part of your life. You’re part of the community, and that means part of the community. It doesn’t end at just their seed stage or demo day. It’s a continuation forever.
What’s Next for Promoted: Keep Doubling Down on Top Customers and Get More Like Them
Neil: Wow. That’s a fantastic story. And you’ve made it through, and you enjoy the benefits now and with a post-pandemic world. So I’ve gotta ask what’s next for Promoted.ai. Where’d you go from here?
Andrew: Yeah, the biggest is to just keep doubling down on our current top customers and getting more of them. There was an interview the other day. They asked when did you know the product worked? And I’m like yesterday, we’re at double-digit improvements for Outschool and Hipcamp.
Andrew: We’ve got some fantastic other partners that are some public and some not yet public, but we’re never satisfied. We want more, and our biggest challenge and something that makes us unique in comparison to lots of like ML in a box and easy mode to get started, like lots of ads mode get started. There’s Algolia out there for Search.
The Race of Selling Optimization to Top Engineering Companies
Andrew: We really focus on top marketplaces that are already scaled or have engineering teams, so that means the bar is really high and if you are kind of like, oh yeah, we got double digits, you know, we’re great, and be happy forever. No, no, no, no, no. That was good yesterday, but today, we need another percent improvement. And the day after that we need another percent improvement and the nature of this business with the data infrastructure and machine learning is multiplicative.
Andrew: Right? I’m adding this extra component, but it interacts with every other component as well. So voila, you’ve just created much more complexity for smaller and smaller gains. So that’s the race that we have is taking what we currently have and continuing to invest in making it even better.
Andrew: But then that also increases your cost a lot, so from our side, it’s like this forever goal of adding more data, which increases our costs, which then we have to increase our infrastructure, but then increases latency. So we have to reduce the latency. And then that’s the main thing for us: is to just keep that loop going to make the absolute best optimizer, and then once you have a really fantastic optimizer and the data infrastructure to support it. Then there are lots of great things you can do, like ads or discount promotions. Like how do you optimize, how do I know if I was in slot 10? And if I was in slot one, that’s gonna increase my sales, and by how much and how much is that worth?
Andrew: Well, you just think about all the pieces into that, right? You need to know what the value is. If you didn’t do it, you need to measure the value for all of it. And then you need to predict all of the conversion optimizations, and then you need to measure the user experience and put at the top and maybe degrade the user experience of what that’s worth and does the price you’re willing to pay to exceed that.
Andrew: Like just keep hitting that loop, so keep investing in our core product for the top customers, and then bringing on even more similar top customers, and then eventually it’s that cross promotions vision of us actually powering all of the greatest marketplaces in the world. That’s a pretty interesting book of inventory if you’re an ads business, isn’t it?
Talking about Favorite Music and Outro
Neil: It really is, and it’s a story I’ll be following very closely and it’d be great to stay in touch with you and get you back on maybe next year and find out how that journey’s going. But before I do let you go, I’m gonna have a bit of fun with you. Now. I always like to ask my guests to leave a personal note of inspiration with everyone listening. Now that can either be a song choice that we can add to our Spotify playlist, a song that inspires you or helps you get your head in the zone, or a book that can add to our Amazon wish list. What are you gonna leave everyone listening with today?
Andrew: You know what I think, I think I looked at your Spotify list and I’m gonna be that guy and kind of blow up your Spotify list, but something doesn’t quite fit.
Neil: I had a guy a few weeks ago, put on pirate metal, so you’re gonna have to go there, man.
Andrew: Pirate metal. you have to, there you go. Yeah. I really like Cowgirl by the Underworld.
Neil: Oh, that’s a good tune.
Andrew: Yeah. Like that early nineties. Yeah, UK, electronica scene. There’s just something about that era. It’s like, wow, Computers! They’re amazing. And you have like these memes of the kid with spiky hair on the computer and big old sea monitor and like the nineties hacker I’m in.
Andrew: but it was just like, wow, the internet and you can be in your house anywhere in the world and be nobody just like for me, frankly, I’m just a kid from the Midwest, you know? but you could be somebody if you are really good at this and that song is just that reminder of wow, this magical world.
Andrew: And it turns out that actually making things work is a lot of work and it’s a job, but that song kind of hearkens back to a more innocent time of when you sit down at the computer to write code and that’s a cool thing.
Andrew: You find your sunglasses and your trench code or something. I’m like, yeah, they’re gonna go out to the rave next door, the matrix sort of thing. The funny thing is that I live in San Francisco and if you want to do that, you can, but you don’t. It’s ridiculous. You mean, first of all, I need to see my computer screen and sunglasses aren’t gonna work and like going to the rave, man, I’m getting old. Right?
Andrew: You did it a couple of times and then it’s like that’s what it is, and then I’m gonna stay at home with my wife, and Netflix and chill is kind of what I’m really passionate about these days. Anyways, cowgirl by the underworld, reliving the 16 to 25-year-old you about the dreams of being in technology.
Neil: I absolutely love that. I’ll be listening to that as soon as we finish this call. You’re never too old to rave definitely. But before you go for anyone listening, just want to find out more information about promoted ai, dig a little bit deeper on any of the topics we talked about, reach your team or contact you even. What’s the best starting point for everything.
Andrew: Our website: promoted.ai.
Neil: Awesome. Well, I had those links to the show notes so people can find you. Love sharing with you. Great story! And I love how you’ve simplified everything. Put it in a language that everyone can understand, which is something I try and do on here. But I meant what said a few moments ago, we’ll stay in touch and it’d be great to see how your journey continues to evolve and maybe get you on later in the air or next year. Thanks for sharing that today.
Andrew: Well, thank you, Neil. It was a pleasure.
Neil: I know we’ve only scratched the surface of this topic, but I really enjoy today’s conversation.
Neil: And if there is anybody else listening in the industry, you’ve got different insights or any story that you’d like to share with me, or even if you’ve just got a few questions, remember to email me at techblogwriter.co, Twitter, Instagram, LinkedIn, just @ me or C Hugh, send me a DM. Don’t just hit the follow button.
Neil: Let me know if you’ve listened to the show. What you like, what you don’t like, whatever it might be. And now we’ll keep this conversation going. But other than that, it’s time for me to get outta here and I’ll return again tomorrow with another great guest. So thank you for listening as always. And until next time.
