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[00:00:27] Marcus Johnson: Hey gang, it's Friday, June 12th. [00:00:30] Rachel, Grace, and listeners, welcome to Behind the Numbers, an EMARKETER podcast made possible by Rocks. I'm Marcus, and joining me for today's conversation we have two analysts. One of them lives on the east of America-

[00:00:41] Rachel Wolff: That's true ...

[00:00:42] Marcus Johnson: Rachel Wolff.

[00:00:42] Rachel Wolff: That is me. I do live on the east.

[00:00:45] Rachel Wolff: Thanks for having me.

[00:00:46] Marcus Johnson: No one's ever said it like that before. I wish I hadn't. Uh, living in the west, Grace Harmon.

[00:00:51] Grace Harmon: Hey Marcus, thanks for having me.

[00:00:52] Marcus Johnson: Of course. Today's fact.

[00:00:58] Marcus Johnson: The Veryovkina [00:01:00] Cave in Abkhazia, in Georgia, is the world's deepest known cave. How many Statue of Liberties deep do you think it is? Or Eiffel Towers? I'll take any structure. Very is the answer. It's an impossible guess. 7,200 feet for our friends across the pond. That's about 2,200 meters. Uh, it narrowly edges out second place, which is in the same area, um, which is just by 42 feet, [00:01:30] so it's very, very close, first and second.

[00:01:32] Marcus Johnson: So for context, the tallest building in the world is the Burj Khalifa in Dubai. That's 2,700 feet. El Capitan in Yosemite National Park, that thing that, that the Free Solo movie's about, is 3,000 feet. So this cave is around two and a half times as deep as each of those. It would be seven Eiffel Towers or 23 Statue of Liberties.

[00:01:57] Rachel Wolff: That's very impressive. Pretty deep. Yeah.

[00:01:59] Marcus Johnson: Why would [00:02:00] you go in there? Three days it takes you to get in to the bottom, and three days to return to the surface. It's fascinating, 'cause we talk a lot, M- M- uh, Melissa, uh, Garside of Visual Capitalist was explaining that we scale mountains, and that's something that, you know, you see movies about, and there's stories about them.

[00:02:17] Marcus Johnson: Um, but going into the deepest ca- uh, caves in the world is, is kind of, uh, uh, one of planet Earth's kind of last frontiers. It's fascinating. Seven of the 10 deepest caves reach more than a mile underground. Four of the deepest are clustered in the same, uh, [00:02:30] karst mountain region in Abkhazia, Georgia, um, where the soluble karst rock creates ideal conditions, uh, for deep cave systems.

[00:02:39] Marcus Johnson: Um-

[00:02:40] Grace Harmon: I'm good at sea level.

[00:02:43] Marcus Johnson: Same.

[00:02:44] Rachel Wolff: Yeah, every news story I see about a cave is people getting trapped, so I think- I know ... I will stay out of the very deep ones.

[00:02:51] Marcus Johnson: Yes. Well, you don't want to go in Mexico's Cheve Cave. Um, that exploration started in February, and they are attempting to be-- They think [00:03:00] that might be the deepest one of all.

[00:03:01] Marcus Johnson: They haven't found that out yet. How do you... I don't know.

[00:03:05] Rachel Wolff: Yeah, I was gonna ask, how do you measure? Do you bring a, a very long ruler with you? A

[00:03:10] Marcus Johnson: tape measure. Hold the other end. Uh, anyway, today's real topic: what you need to know about the current state of AI shopping assistants.

[00:03:25] Marcus Johnson: All right. So Rachel, you've recently written a piece about this. I have. [00:03:30] And, uh, we wanted to talk about it, but we'll start with some of the news about AI shopping assistants. Uh, a piece from Forbes contributor Jason Goldberg back in March titled Why OpenAI's Checkout Retreat Spells Trouble For Its Commerce Strategy.

[00:03:45] Marcus Johnson: So OpenAI launched, uh, instant checkout, uh, through ChatGPT at the end of September Uh, last year, but it's closed down, uh, early March. Uh, Rachel, why didn't ChatGPT's instant checkout take off? [00:04:00]

[00:04:00] Rachel Wolff: I think there are a few reasons. One is that fundamentally OpenAI underestimated the amount of work that it would take to build a functional, a functioning e-commerce marketplace.

[00:04:09] Rachel Wolff: I mean, they lacked a lot of kind of basic func- functionality such as, you know, how to calculate and collect sales tax, which if you think about shopping online is such an integral part of the experience. And same thing with, you know, if you wanted to apply a coupon code, if you wanted to connect your loyalty program.

[00:04:24] Rachel Wolff: All of these things weren't really built into the interface as it was released. And [00:04:30] for that reason, people just weren't interested in using it. So Walmart said that in-chat checkouts within ChatGPT converted three times worse than its own website. And so when you see that, if you're Walmart, you kinda think, "Well, why am I spending all of this money and time and resources to develop this thing that doesn't work as well as my own website when I could be putting it into more useful AI initiatives or, you know- Mm-hmm

[00:04:57] Rachel Wolff: the rest of my business?"

[00:04:58] Marcus Johnson: Did they rush it? [00:05:00] Is that fair to say? 'Cause this p- Forbes piece was saying, "At launch, Instant Checkout supported only single item purchases from US Etsy sellers. Multi-item carts, no, uh, not yet. Promotional codes, no. Shipping promises, no." Is it fair to say that, um, this isn't, like, a referendum necessarily on Instant Checkout or AI, uh, shopping and buying, but more, um, a model that was rushed out the door when the company had other priorities?

[00:05:24] Rachel Wolff: Yeah, I think that's fair to say, and I think you could even draw that out to talk about other AI [00:05:30] platforms and their shopping features as well. And, you know, m- probably talk about this more later, but a lot of these companies have announced these big splashy features, but for the most part, they're not really available to shoppers.

[00:05:41] Rachel Wolff: And so there is this disconnect between what companies are saying and what they're actually doing. And I think in the case of OpenAI and ChatGPT, it does take a lot of resources to build a functioning e-commerce marketplace, which is essentially what Instant Checkout would've been, and they just didn't feel like they could do it at [00:06:00] this moment in time.

[00:06:00] Grace Harmon: Yeah, I mean, on a broader level, you know, talking about Google, it's announcing features faster than it's launching them, so it's kind of confusing for consumers or businesses to know what's actually available and active at any given time. Yeah. And then I think that it's really hard for retailers to set up shop.

[00:06:16] Grace Harmon: A lot of the holdup for merchants selling through these AI tools is on the firm side, not on the retailer side.

[00:06:21] Marcus Johnson: So is it fair to say, then, J- and Jason, uh, Mr. Goldberg of, um, Forbes was, was saying, "OpenAI's retreat from Instant [00:06:30] Checkout tells us" Almost nothing about whether consumers want AI agents to help them buy things.

[00:06:35] Marcus Johnson: It tells us a great deal about what happens when the most distracted tech company tries to build a commerce platform on the side. You can't draw meaningful conclusions about consumer demand for agentic commerce from an experience that was never complete enough to test. The product wasn't beta, it was pre-alpha.

[00:06:49] Marcus Johnson: Is that fair then? Uh, uh, are you, after doing the research, Rachel, have you found that, um, we're just early days as opposed to consumers don't have any interest in this at all?

[00:06:59] Rachel Wolff: I [00:07:00] think that there is definitely interest on the consumer side in figuring out what these tools can do for you, right, from the shopping experience.

[00:07:07] Rachel Wolff: I don't know that it extends all the way out to letting it complete a purchase on your behalf. But I think-

[00:07:12] Marcus Johnson: Mm-hmm ...

[00:07:12] Rachel Wolff: you know, the fact that Instant Checkout converted so much worse than Walmart's own website tells you that, yes, it was a, OpenAI kind of overshot a little bit. But for the time being- Mm-hmm ... at this moment in time, shoppers just don't want this kind of functionality necessarily from these platforms.

[00:07:28] Rachel Wolff: They're more comfortable with [00:07:30] transacting on retailers themselves. They're not willing to hand over their credit card information to these platforms. And so I think there is a little bit of, you know, yes, the technology isn't there yet, but the consumer isn't there yet either.

[00:07:41] Marcus Johnson: It's interesting to see what route they'll take, um- This note saying, uh, from the piece, "OpenAI seems to be betting, at least for now, that its role in commerce is closer to Google's.

[00:07:51] Marcus Johnson: Influence the shopping journey, monetize through ads and affiliate referrals, and let the merchants handle the messy part." Grace, do you think that's a fair assessment of how [00:08:00] OpenAI, after taking a step back, sees their role in, in shopping?

[00:08:03] Grace Harmon: I think there's a lot of challenges of putting together AI with all the intricacies of e-commerce business.

[00:08:09] Grace Harmon: It has lacked to do really diverse experimentation and testing with its ad business, and I do think that extends into- Mm-hmm ... um, what it's been willing to try in e-commerce.

[00:08:19] Marcus Johnson: Yeah. They're a company that needs money, and, uh, focusing on advertising and trying to monetize all of those weekly users, those 900 million odd, um, is much more of [00:08:30] a priority.

[00:08:30] Marcus Johnson: Uh, and so maybe focusing on that first and then coming back to the commerce piece at some point, or putting more effort into it.

[00:08:36] Grace Harmon: I think that it's also figuring out a much broader issue, which is whether it needs to be focusing on those consumers or on enterprises right now. So, you know, does it push- Mm

[00:08:44] Grace Harmon: into having a side quest of figuring out a really strong e-commerce business, or does it go into focusing on having really strong coding tools? So I think it's not just, you know, one business at a time. It's trying to figure out, like, five.

[00:08:57] Marcus Johnson: Yes. Yes, indeed. Rach, we're [00:09:00] talking a lot about, uh, OpenAI and the instant checkout.

[00:09:02] Marcus Johnson: Uh, your research was on, uh, what you call third-party AI shopping assistants, and ChatGPT falls into that, into that bucket. What do you mean by what are third-party AI shopping assistants? What are some other ones? What are some that don't fall into that bucket?

[00:09:16] Rachel Wolff: Yeah, so my report was really focused on, um, these shopping tools that are offered by AI platforms.

[00:09:24] Rachel Wolff: So ChatGPT, Google, which has several, include Gemini, AI Mode, so on and so [00:09:30] forth, um, and also Microsoft Copilot. Those were the three that I focused on for my report. But you could broaden that out a little bit and talk about, uh, Perplexity, for example, which also has shopping tools. Uh, Meta AI has, you know, they're sort of dipping into the space as well.

[00:09:44] Rachel Wolff: So essentially, you know, third-party AI shopping assistants are these platforms that don't offer products directly to customers but facilitate The purchase of them- Mm-hmm ... versus say, Amazon, I guess now it's Alexa, um, or [00:10:00] Alexa Plus, or Walmart's Sparky that are tools, AI tools offered specifically by retailers for shoppers on their sites.

[00:10:07] Marcus Johnson: So let's talk through some of those, uh, that you mentioned, ChatGPT, Google, and Microsoft Copilot that you looked at. What's one strength of each? Let's start with the strengths. Sure. What's one strength from each platform?

[00:10:20] Rachel Wolff: So in my, um, extensive testing of these platforms, I found that ChatGPT was pretty good at the research phase, right?

[00:10:27] Rachel Wolff: If you give it ... I was giving all [00:10:30] three of them more or less the same prompt, which was I'm looking for a bag that's work appropriate, that can fit a laptop in this particular price range, and having them sort of generate a list of recommendations and then kind of refining from there. Mm-hmm. And I found that, like, ChatGPT, in terms of the results, in terms of the interface that it would give you, was really good at walking you through the pros and cons, about laying everything out in a way that was pretty intuitive, um, having product images in the results.

[00:10:56] Rachel Wolff: So all of those things that really helped with- The initial [00:11:00] research phase, um, of the search. For Google, I found that where Google really excelled is they had the most up-to-date product and pricing data, and they also had the most set of features related to, I'm gonna call it, like finding the best price, right?

[00:11:15] Rachel Wolff: So tracking price over time, figuring out whether you c- Mm ... there are any coupons that you can apply, you know, with your loyalty benefits, uh, or membership benefits will apply to a particular purchase. And I think, you know, that was really where Google excelled. And for Copilot, it was [00:11:30] kind of in the organization of all of the shopping searches.

[00:11:32] Rachel Wolff: They were the only one of the three to have a separate shopping tab that would collect all of these sort of commerce-related searches, which made it really easy to go back to see, you know, what it had recommended in the past, various searches that I had made for different types of products, and it was really helpful organizationally.

[00:11:50] Marcus Johnson: Grace, Rachel writes, uh, in a piece, "Though the platforms', uh, shopping features look alike, the quality of the user experience is where they diverge." Um, is [00:12:00] there one of these platforms that stands out to you more so than the others when it comes to shopping in AI?

[00:12:05] Grace Harmon: I think that Google stands out pretty strongly.

[00:12:07] Grace Harmon: For one thing, there's just that huge amount of trust, and it's the default search platform to begin with. Yep. Um, OpenAI, you know, it's, Rachel had mentioned this before, it's pretty clunky to be buying multiple items, um, and the information isn't always accurate, even if it's very good at presenting it.

[00:12:21] Grace Harmon: For Google, uh, I had mentioned before that it's kind of launching or announcing these features faster than it gets them out into, into people's hands. Yeah. Um, [00:12:30] but, you know, it offers virtual try-ons, things like that. So I think that they've done a lot more exploration into what they can be offering other than just, um, a listicle.

[00:12:38] Marcus Johnson: Uh, Rachel, where do these platforms struggle? What's the main drawback from each?

[00:12:42] Rachel Wolff: I mean, one thing that I found that's pretty much consistent for all of them is that they're very good at giving you results if you give them very specific parameters, right? If you say, "I have a certain price range," or, "I'm looking for X, Y, and Z features."

[00:12:55] Rachel Wolff: But I think where they all fall short is in that area of, like, product inspiration, [00:13:00] right? Something that social commerce is very good at is inspiring you to make a purchase or, you know, you find things based on your personal style that you then click on and buy. Whereas I feel like all three of these platforms are better at s- I'm gonna call them, like, need-based, where you have very specific check marks or, um- Features that you wanna hit, and it can tell you whether it does for each one.

[00:13:24] Rachel Wolff: Mm-hmm. Um, so I think that's kind of a, a weakness in a way for all of these assistants that limits the types of [00:13:30] use cases that you might have for these AI shopping assistants overall.

[00:13:34] Grace Harmon: So you can't really do, like, an unbranded search like you would with Pinterest or something like that.

[00:13:37] Rachel Wolff: Right. You can't really be inspired because you have to tell it- Yeah

[00:13:40] Rachel Wolff: what you're looking for, which is kind of- Yeah ... a drawback when you don't know what you're looking for. And- Mm-hmm ... a lot of things, like, let's say for apparel, I think a lot of those purchases are based on kind of intangibles, right? Like, you look at an image, and you decide whether to buy something based on, I don't know, maybe how it drapes on the model, or you like this particular cut over that cut, and [00:14:00] you can't really describe that necessarily to an AI assistant.

[00:14:03] Marcus Johnson: You write that AI platforms are widely used, uh, product research tools, but they still struggle with conversion. Uh, what's the number one reason why?

[00:14:13] Rachel Wolff: I think the issue, as I s- may have said earlier, is just the lack of trust, right? People aren't ready to give AI platforms the authority to shop on their behalf because these tools are untested.

[00:14:24] Rachel Wolff: Nobody really knows if they're gonna go and make unauthorized purchases on people's [00:14:30] behalves when you've just hand over the credit card data. But I think the other issue is that it's not only the payments piece. I think people just don't trust AI platforms' recommendations wholesale. As you can see from this chart on the screen, um, over 60% of AI users search for more information outside the AI after they've been recommended a specific product by an AI assistant or chatbot.

[00:14:53] Marcus Johnson: Oh, interesting. Mm.

[00:14:53] Rachel Wolff: And this data comes from a January report that, um, EMARKETER did with Publicis. So, you know, what this tells me, uh, [00:15:00] tells everybody, is that people need to do their research outside of what these chatbots are saying. And maybe that is on Google. Maybe you ask- you s- get something on AI mode, and then you promptly switch to a Google search to find out more.

[00:15:13] Rachel Wolff: Yeah. Or maybe you go to a retailer's website them- itself. But I think that's just a, a huge barrier.

[00:15:18] Marcus Johnson: I wanna talk a bit about what this looks like down the road. Uh, we have some forecasts.

[00:15:22] Grace Harmon: I would have something to add on to-

[00:15:23] Marcus Johnson: Please ... what Rachel said. Oh, please, please, please. Absolutely.

[00:15:25] Grace Harmon: Sure. So first off, I agree with everything that Rachel said.

[00:15:27] Grace Harmon: You know, people want to be in control of [00:15:30] checkout. They want to get their own, uh ... They want to find personalized ideas. They want to find new products maybe without having to give the details of, "I want this color, this price, this brand." Um, and they, they, like you said, they wanna handle the spending on their own.

[00:15:42] Grace Harmon: That trust element around autonomous purchases isn't there. Um, and then you had mentioned that there are still doubts around the recommendations themselves. You know, are they accurate? Are they generic? And that's pretty split. As you can see from this chart on the screen, about 31% of US adults trust AI somewhat or a lot to make [00:16:00] recommendations, 41% don't trust it, and 23% are neutral, according to YouGov.

[00:16:05] Grace Harmon: Mm. Um, so that really just doesn't speak to overwhelming confidence.

[00:16:09] Marcus Johnson: You're talking about the trust piece. It- so it's, are they accurate? Um, but also i- is, uh, part of this, is it, uh, is it paid for? Right? Like, is there, is it like a sponsored item that you're getting presented? Like, how do you, how do you solve for that?

[00:16:23] Marcus Johnson: Is that just the same with Google, you know, search results that you'll say sponsored on the ones that are sponsored and not on the ones that aren't? Like, [00:16:30] is that the only way by being transparent and labeling which ones are and aren't, is that the only way to get around it? Or do you think people eventually will say, "I don't really care as long as it gives me the best thing"?

[00:16:39] Rachel Wolff: Yeah, I mean, I think that's an interesting question that all these platforms have to kind of figure out. You know, I think ChatGPT and, and Google seem to be of the mind that if you clearly label what's an ad and what's not, then that kind of gets around the trust issue. Um- That

[00:16:54] Grace Harmon: that's good enough.

[00:16:55] Rachel Wolff: Right.

[00:16:56] Rachel Wolff: Yeah, so on the subject of advertising, two-thirds of consumers say they would [00:17:00] trust chatbot recommendations less if they saw ads, while 57% would trust the advertising brand less, and this is according to a survey by PartnerCentric.

[00:17:10] Marcus Johnson: So I want to talk a bit about where this goes next. Uh, uh, we'll start with where we are today.

[00:17:14] Marcus Johnson: Our forecasting team estimates that 80, 8-0, million Americans, so a quarter of people in the country, uh, are GenAI shopping users, meaning people who enter a prompt into a GenAI system to carry out any shopping-related tasks. Uh, shopping does not mean buying. [00:17:30] Uh, most people are shopping, looking. Uh, and this excludes AI-powered search summaries, so, um, think of AI overviews, things like that.

[00:17:37] Marcus Johnson: Uh, we think that number's gonna climb from about 23% today to about 30% in three years, so across the 100 million, uh, Americans mark. Um, that's not everybody by any stretch, but it is, um, a fair amount of people, and it's about half of GenAI users. So if you're a GenAI user, um, then there's a much greater chance obviously that you're gonna be a GenAI shopper.

[00:17:58] Marcus Johnson: Um, Grace, I'll start with [00:18:00] you for this one. How will we be using AI shopping assistance a year from now? What's, what's AI shopping gonna look like?

[00:18:06] Grace Harmon: I think the biggest part is just how user behavior changes and how strong integrations are. Um, if these traditional shopping habits stay really sticky, if these experiences stay really fragmented, I think AI is gonna stay a lot more of a research and discovery tool rather than turning into this personal shopper.

[00:18:23] Grace Harmon: Um, and I think that the next year we're just gonna see more of a test of whether consumers are willing to hand off more of the shopping journey [00:18:30] to AI, um, and also just how strong those services are. I also would go back to the stat you mentioned that you also should keep in mind that there's, you know, intentional and then unintentional AI use.

[00:18:41] Grace Harmon: There's a difference between me going to chatgpt.com and asking- Mm-hmm ... it to pick something out for me, and me putting in a Google search and then, and then interacting with an AI overview that is presented to me.

[00:18:51] Marcus Johnson: Yeah, people might not even realize they are AI users. Um, they're just going to the same old place, but it happens to be using AI.

[00:18:59] Marcus Johnson: Rachel, you, [00:19:00] you wrote that shoppers may be more comfortable interacting with retailers' own AI assistants because of trust, personalization, things like that. What does the landscape look like in a year? Is there a clear frontrunner emerging? How are people interacting with AI shopping?

[00:19:12] Rachel Wolff: Yeah, I mean, my prediction As you just said, is that, um, shoppers will just be more accustomed to interacting with what's already on retailer's websites, because those are the tools that can really deliver the best experience, right?

[00:19:26] Rachel Wolff: In terms of personalization, in terms of recommendations. Mm-hmm. And in [00:19:30] terms of delivering actionable insights. You're already on the retailer's website. You already have s- a certain amount of purchase intent, and so you can go from there. And I think, you know, to Grace's point, it ultimately comes down to whether these AI shopping assistants, third-party shopping assistants, actually improve the experience for the consumer, right?

[00:19:49] Rachel Wolff: If there is genuinely a reason to use these AI shopping assistants versus, again, just going to Google or just going straight to Amazon and using Rufus. Mm-hmm. [00:20:00] I think that will be the key. And certainly, all of these platforms are trying to a certain extent, but whether they'll be successful depends on whether they can deliver on their promises.

[00:20:08] Marcus Johnson: Let's end with what to do right now. Brands and retailers, what's, uh, your number one recommendation for them?

[00:20:15] Rachel Wolff: So I think brands, retailers, you have to test and learn, right? You have to think about how you're showing up on ChatGPT, how you're showing up on Google, how you're showing up on Copilot. But you also have to think about all these other platforms that people are now using- [00:20:30] Mm-hmm

[00:20:30] Rachel Wolff: um, for commerce related searches or, you know, just, um, searches in general. And so maybe that means looking at Quad and looking at Meta AI and looking at Perplexity and all these other platforms. And the technology moves fast, so you have to be prepared for any potential shifts, whether it's, you know, somebody launching in chat checkout, and then promptly ditching it a couple months later.

[00:20:52] Rachel Wolff: You have to be flexible to manage those changes.

[00:20:55] Marcus Johnson: Grace, anything to add?

[00:20:57] Grace Harmon: I think it's just a pretty daunting task right now to [00:21:00] meet shoppers where they are. I mean, you have to be ready for all of these zero click searches where people are getting information, um, and maybe experiencing some, some brand awareness in chat, but not clicking through.

[00:21:10] Grace Harmon: You know, making sure that product data and reviews are constantly available for AI systems to find and index. You know, keeping up with regular SEO and keywords for traditional search. I think it's a really daunting task right now. I think I'd have a hard time giving a single recommendation.

[00:21:23] Marcus Johnson: Well, if you do need, uh, more recommendations, Rachel's got you covered.

[00:21:26] Marcus Johnson: Uh, the full report, uh, she just put out is the State of [00:21:30] AI Shopping Assistants. PRO+ subscribers, head to EMARKETER.com. Uh, you can find it there, or there'll be a link in the show notes. That's all we've got time for for today's episode, though. Thank you so much to my guests for hanging out with me.

[00:21:39] Marcus Johnson: Thank you first to Rachel.

[00:21:41] Rachel Wolff: Thank you so much.

[00:21:42] Marcus Johnson: And of course, to Grace.

[00:21:43] Grace Harmon: Thank you, Marcus.

[00:21:44] Marcus Johnson: Yes, indeed. And to the whole production crew. Uh, Danny, for helping out on this one. Luigi, hanging in the background, taking credit for doing nothing. And everyone for listening in to Behind the Numbers: an EMARKETER Podcast, made possible by Rokt.

[00:21:54] Marcus Johnson: Thank you, guys, for listening in. Uh, subscribe and follow to hear about new episodes, and leave a rating and review if you can. Uh, it's the equivalent [00:22:00] of donating to a podcast. So if you guys have a few minutes for that, they mean the world to us. They really, really help us out. Um, we'll be back on Monday.

[00:22:05] Marcus Johnson: Until then, happiest of weekends.