Keyword Search Is Dying. Is Your Catalog Ready for AI Agents? — PayPal

Hi, everyone. Um, welcome. I, I'm Nixon Dinh. I'm the Director of Product for Agentic Commerce at PayPal, and what that really means is I lead a lot of our efforts on how AI agents are reshaping shopping and payments. Um, and, you know, most of my career has been on commerce and payments, and right now I'm really excited about helping merchants navigate this shift, uh, towards, like, agent-driven commerce. And specifically, what we're gonna talk about today is, um, the catalog piece and it

being agentic-ready and sharing some of our learnings on this, on this journey.

So I think it's very important we just kinda level set a little bit. When we look at the evolution of commerce, we've seen it transform from, you know, in-store brick-and-mortar transactions to the advent of e-commerce, where this started off quite linear. Um, a user would simply search and buy from e-commerce sites. And then through, like, Internet 2.0, uh, we've seen that transform further into more complex, uh, channels that involve email, SMS, and, like now, social commerce. For us, like,

we see AI as the next evolution in that journey, and with any evolution, we know two things to be true. The first, like all change, it can seem like a threat to a business, um, but on the flip side of that, it can also be seen as a big opportunity. And the second thing that we know is that whenever we have seen consumer behavior change because of a new technology, commerce tends to evolve alongside with it. And so we are starting to see that now with agentic,

where not only is the new-- this is a new channel that's enabling, uh, merchants to engage with customers in a very deeply personalized way, but also how customers are searching and finding products is changing. And, uh, now is really the time that merchants are starting to look to invest on how to, to change with, with their customers and, and, uh, succeed in the agentic era.

So earlier, I touched on how the consumer behavior is starting to change. Um, but I just wanna dive deeper a little bit on this. So we, you know, when we first, uh... when we think about, uh, consumer shopping, that behavior up until today has been now what we're calling the search era. Um, it goes through a, a phase where, like, it's keywords. You're navigating a catalog. There's SEO strategies,

and this is what most businesses have optimized towards over the last decade. And while for many businesses this worked, this model put the burden on the shopper to really find what they needed.

Um, now what we're seeing is that we're moving into this phase called the intent era. So instead of typing keywords like, "I want school supplies," um, I might say something like, you know, "My daughter's starting school in September in fifth grade, and I need, uh, to, to shop back to school." And that's a very completely different interaction. Um, so you are now expressing your needs in your own words, and AI is now asked to meet you where you are.

And so the brands are coming to you instead of you having to go find them, and that's a very, very different interaction model. Um, and most businesses now are starting to find that they need to set themselves up for this interaction model.

Um, so

what we believe will happen next is, after we go through the intent era, what will have to happen is customers need to start trusting the recommendations that they're getting from the, from the agents. And once that trust starts to build, we do start to believe that they will start delegating more and more actions to the agents, and that's also where w- as PayPal, we're building towards is, um, h- continue to build on that trust and move into a world where humans start to delegate more and more of their tasks in commerce and shopping to their agents.

So although we talk about this delegation era, and we think it sounds like it's so far into the future, you might be asking, like, why this matters now. And so we have a couple of re- pretty big data points that we want to share to really underscore why this matters. The first is, uh, through a study at Bain & Co. Um, they project that by twenty-thirty, fifteen to twenty-five percent of all commerce will be executed by an agent. And furthermore, within a decade, agents are predicted to

outnumber humans online. And so these numbers don't just indicate that this is, like, not meaningful now. It is a very marginal shift that, you know, businesses can't ignore and that this is a very structural transformation happening in the entire e- entire e-commerce ecosystem.

So to go further, I think it's very good to understand why PayPal feels like we are bullish on this opportunity. Um,

as a industry, we expect, uh, to be some very tangible opportunities coming forward. We expect one point-- one trillion dollars of, uh, commerce to happen, uh, in US retail to be agentic, and then we expect that to be three to five trillion globally. And what we've have seen is that there are over two billion users, um, projected to use, like, AI to start their shopping journey over the next few years.

And so

when we think through this, what does this actually mean for businesses and merchants? When we talk to our customers and what we talk to our partners, what we've seen is six-- they-- that what they've re-reported is that over six hundred and ninety-three percent, uh, referral traffic from AI engines. And when we did consumer studies, we found that thirty-nine percent of US shoppers are already using AI for shopping today. Um, and I'm sure many of you have probably defaulted to that pattern today. And what we

have found also is that when you do start and interact with an agent first, businesses have tend to see f- up to four times higher conversion on a completion of an order because now they can get more rich and in-depth data on a product.

And so

with that all happening and the traffic moving, we wanna break this down further onto, like, what this really means acr- uh, uh, deeper for merchants. In the search era, this was very, like, SEO-based, and you might optimize for, like, a summer dress. In the intent era, you have to think through, "How do I be discoverable for where an agent is representing my, my business?" And then in the delegation era, we need to think through, uh, how I

can convert that demand safely and trusted-- in a trusted way.

And so the challenge becomes to think through is, like, as commerce moves to AI agents, how do you as a business be visible where the consumers interact?

The issue with this is that catalogs weren't really built for agents. Um, most merchants today, uh, maybe advertise on Google. Um, they have a catalog they syndicate to there or maybe to Meta or Facebook or other advertising platforms. But the spec wasn't built for agents. And so agents now, um, uh, c- you know, if you... Uh, it's not optimized for agents to discover products. Um, it's not as readable for agents as well, and they're really just built for humans search.

And then to compound that even more, the models that agents use are still, for the most part, a, a black box. Agents can use different varying models to make their decisions. Um, different models use different ranking and recommendations a-and, and, uh, algorithms to, to determine their recommendation. Uh, vector database retrieval is also not always deterministic. And because of all of this, how do you decide as a business what model do you, do you

optimize for? In the old SEO world, there was a clear strategy. In this new world, the strategies are still remain largely unclear.

And so what do you do when you don't know? You run experiments. And so we, we ran some experiments. We enriched, uh, some of our merchant product data, and we ran experiments to see how agents, uh, uh, searched, uh, and, and recommended products.

And before I get into the experiments that we ran, um, I think very good to level set with everyone how, like, the differences between keyword and semantic search. And then it, it trickles through to the things I've talked through earlier with keyword search being, you know, very matching the, the search that a user's entering in. So in a classic search, it might la-match literal words. Um, for example, I'm searching for blue running shoes, and you only get results that contain those words.

The good part about this, it's very precise, it's fast, it's predictable. But the downside is it misses intent. And so if you ever search footwear for jogging, it may not even return those shoes.

Uh, and that's only simply because the words don't match. Whereas with semantic search, it matches based off meaning. Um, what it does is it converts the query into vectors that create, um, meanings of intent. And so you might type something, "Oh, s- I'm searching for something comfortable for marathon training," and you might find your running shoes even though none of those words exist in the search query. Um, so the good part about the-- that is it broadens in

how you can search. But the downside is if you inject too much meaning, it can lose focus and hallucinate. So there is a balance of how much you want to enrich and how much you want to inject into your enrichment. So to sum it up, keyword search is quite precise. It's, it's very literal. Semantic search is just-- it's smart, but it's very fuzzy. And realistically, the real answer isn't an either/or. It's, it's combining both, which is exactly what our hybrid

approach does. Um, and that's kind of where we're headed next.

So now that we understand what semantic search is, it's important to highlight that there is no singular way to really enrich product data for semantic search. I think it's really important for you as a business to understand the kind m- kind of catalog data you already have. Um, do you have a thin catalog? Do you have a catalog of products that are more spec-heavy? Depending on where your business is today, you should think through what y-you might want to enrich. And so for the experiment though, we thought through different

patterns of enrichment. Um, and I won't go through all of them in detail, but th-things around, like, filling attributes, making sure there's description depth, um, make sure there's buyer context and trust signals and even some product identity.

I think the main takeaway from this slide is that enrichment isn't one size fits all. The right approach really depends on the catalog you're starting with, and that's the framing we'll build on next.

And so here's just an example of enrichment in action for us. Um- Example on where, we're starting, we have men's, uh, men's shoe model 8543 that's blue. And then the description will be, uh, blue running shoes, um, available in multiple sizes. There's an attribute that's blue. Keyword matches may be blue, running, or shoe. And then, um, the semant- for semantic there's, you know, very little context for that. When you enrich,

there, now we might have the title as a men's lightweight running shoe. Um, the description now more enrich, like it's cushioned, it's breathable. Uh, there are further attributes we've pumped into that as well. So there's like true to size, it's, it's, the material's mesh. Um, we also put in some reviews here as well, so e-e-enrich that with some review data. And now what the search sees is, um, it matches running, it matches training, it matches mesh. There's, there's a lot more that it matches. So with semantic, there's a lot of rich meaning that's put there,

uh, to kind of enhance the search.

And so here, the, the, uh, some of the examples we've used was attribute filling or buyer context or even, uh, the trust signals around the reviews as well.

And so ultimately at the end of the experiment, um, you know, our thesis was generally speaking, does enrichment help boost, uh, recommendations? And at, and it's called yes it does. Um, what we saw was an unenriched baseline, like when it went head-to-head, um, it just completely underperformed. We found that merchants with the thinnest catalogs tend to gain the most. Uh, merchants with weak product data also had the most room to improve.

Um, but I think what we needed to also understand is that it's more about content quality that drives the results, and it's not the schema that actually... and actually piling on, you know, unstructured data can actually dilute the signal as well. And so what do we mean by, like, unstructured data? Like we just don't wanna stuff the, the context of anything or any words we want. We don't wanna put any boilerplate messaging around, um, potential, like, you know, "Welcome to our store.

We're a family-owned business." Those things are irrelevant to an agent at times. And so how you enrich what you structure into the data really, really matters as well.

And so just general speaking, a sum up of our learnings was enrichment helps keyword search. Semantic retrieval requires a very different set of data structures. But there is a balance to how much you want to enrich, uh, to improve semantic retrieval. And while this is only one set of experiments, we did find that enriching just, yeah, head-to-head always improved recommendations. Um, what we had found in some cases when we

over-enriched, the agent tended to hallucinate more and it underperformed. Um, and we don't have a con-concrete recommendation for every business, but a good place to start is just assess your catalog and then figure out the right strategy for that.

Um, and so just to wrap up, you know, enrichment works, assess your catalog structure, and more text isn't better. Um, structured quality content, structured quality content is the one thing that wins out, and AI will reward content quality and not brands. Um, but it does optimize for other trust signals as well.

So thank you, everyone. Uh, if you want to learn more about PayPal's Agentic Commerce services, please visit our booth at P-eleven.