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Traditional search is dead.
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Frankly, it just sucks lately.
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Sabrina McPherson of Publicis Sapient says the new goal is selection, getting
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the AI to recommend and pick your product
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And AI shops differently than we do.
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People buy on emotion.
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The machine wants clean, consistent data it can trust.
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Basically the wall of text that the rest of us close the laptop on
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Oh, I have so much to say.
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This episode of Retail Transformers recorded live from RetailClub
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AI Festival, Sabrina explains how to sell to both audiences
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And why shoppers aren't ready to hand their
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wallets to an agent just yet
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Welcome to Retail Transformers, candid conversations with the visionaries
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shaping the future of commerce.
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I'm Ricardo Belmar
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And I'm Casey Golden.
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Welcome back, Retail Razor fans.
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This is the podcast where we cut through the clutter to bring you
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the sharpest insights from the leaders redefining retail, retail
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careers, and the future of retailing
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This is season two, episode 13, and it comes to you recorded
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live and in person from the RetailClub AI Festival in Huntington Beach, where
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we were joined by Sabrina McPherson, senior vice president and practice
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lead for consumer products management consulting at Publicis Sapient.
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Sabrina has spent more than 20 years helping consumer product
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companies find new digital value pools and build what it takes to capture them.
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So of course, we talked agentic commerce
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Of course.
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Sabrina says the goal has moved from search to selection.
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To get recommended and picked by AI, brands and retailers have to share
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data, and brands have to explain what makes their product different
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in a language a machine can read.
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Price and attributes alone just won't get you there
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Then we get into designing commerce for two
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audiences at the same time.
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People who buy
Load full transcript
on emotion, that's me, and imagery, and AI agents that
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want consistent data they can trust.
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Plus, why a wedding dress and a dress for a wedding are two different
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things that AI just may not get
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And with Meta's new Muse agent racking up downloads, we
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asked how far shoppers will really trust a personal agent to buy for them.
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Sabrina's answer, trust gets earned one step at a time, and nobody wants
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an agent selling them the day-old eggs.
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But before we dive in, let me tell you about where the Retail Razor will
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be next, and that's Retail Agent Con.
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On Tuesday, November 17th from 11:00 AM to 3:00 PM Eastern, join me and more than
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700 of your retail peers at Retail Agent Con, a live virtual event from our good
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friends at IHL Group with ClearSight AI.
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It's an honest look at what AI agents are really doing today across
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merchandising, order management, inventory, and store operations, which
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ones are real, and what has to change in your org chart to make them stick.
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You'll hear from retail leaders like Glenn Ellis of Tractor Supply, Scot
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Seiger of David's Bridal, and Justin Emig of Artisan Lane Furniture, plus
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Dr. Janet Sherlock on getting agents and people working together, IHL's Greg
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Buzek and Jerry Sheldon, and yours truly.
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Registration is free for retailers at retailagentcon.com.
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That's four hours of real-world insight you can use heading into NRF's big show.
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Register today.
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Thank you to Retail Agent Con for making this episode of
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Retail Transformers possible.
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Now let's welcome Sabrina McPherson of Publicis
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Sapient to Retail Transformers
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And we're back in the Retail Razor Tent, and
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this time we're joined by Sabrina McPherson from Publicis Sapient.
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Sabrina, thanks for joining us.
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Sabrina McPherson: Thank you for having me.
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Yeah,
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absolutely.
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So we're gonna talk, of course, because we're at RetailClub AI Festival we're
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gonna talk about agentic commerce.
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What else is there to talk about these days?
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and one of the things that we really wanted to start by asking you is,
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there's a lot of consideration around how retailers and, and brands have to
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align their product data, particularly
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around discovery by all the AI systems.
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And it…
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there's a lot of data involved, updating a lot of product schemas and things.
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So I, I guess that there's sort of a joint goal of both the retail and
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the brands want to be surfaced by AI.
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So I wanted to ask, what do you think needs to happen first for, for retailers
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and brands to agree on how to do that?
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Yeah.
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No, I hear you.
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It's, it's a common thing that we're actually in the middle of dealing
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with with most of our clients.
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I would say, first off though, the, honestly, the goal has changed, right?
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So it used to be always about search, right?
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Right.
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Right.
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And
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now it's selection.
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So it's what do you have to do to get your product recommended and
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selected by the machine, by the AI?
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And so the whole conversation has shifted, right?
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Originally, it was traditional search.
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Obviously, we all know that's dead.
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But it went to AEO to disaster.
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Have you tried lately?
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Yeah.
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I just, it's a nightmare.
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And anyone who's-- It was funny 'cause I, I was talking about this a year
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ago, and people were still in denial that traditional search is dead.
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Yeah.
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Here we are a year later- and we're.. .and we're not even talking about search.
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We're talking about selection.
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Mm-hmm.
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And so I think what's happening now is you have brands and retailers discussing,
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"Okay, let's share data. Let's talk about the data that we can build to
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move us towards selection," because we're both at the same, same agenda.
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Both sides of the equation want a purchase, right?
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Mm-hmm.
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to be selecting the right product that allows there not to be a return.
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And so I think what, what that means is really for brands, the first stage
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of just having the basics done around the product data layer just becomes
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more increasingly important, right?
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Right.
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Right.
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Not just to get searched or discovered.
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It's really because the-- you really need the AI to understand the differentiation
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of your product versus other products.
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And then you need the data from the retailer to inform what the
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human is searching for, the needs, what are they really clicking
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on, all of that jazz, right?
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Yeah.
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So it's about the combination of the enough information behind the
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products from the brand side to inform sort of a differentiation.
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It's not just price.
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It's not just attributes anymore, right?
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It's a lot more that's required- Right … to move, to be able to get to a selection.
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And, and there's a sense there's the more detail, the better.
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Yes.
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Yes.
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Absolutely.
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And it's not just about have the detail.
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It's also consistent data, that detail that basically the AI can trust, right?
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And context, like so much context around the brand that like,
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a lot… ChatGPT does not know the difference between Ralph Lauren Purple
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Label, Blue Label, Right … double RL.
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Like, it has no under- concept of the house.
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There's that… it's not on the website, right?
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It's definitely, not on reddit.
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I know.
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No, absolutely, and it's- it's usually scrapping from the same places, right?
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Yeah.
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A- and I think it's this thing, too, around it's not, the- it's
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not just marketers that need to- brand marketers that need to be
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speaking to the retailers anymore.
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Like,
this is about commerce execution.
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Right.
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Right?
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Yeah.
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Like, so it's- Yeah … it's not that marketing isn't involved, of course,
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but getting those products sort of data layer in place that- so that the AI can
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recommend or pick you, that's an ex- that's commerce execution, next level now.
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You know, GEO and AEO and like mach- your, your site being
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machine-readable, it's like the lowest hanging we have right now.
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Yeah.
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it's not launching a new channel necessarily.
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Just some basics.
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Yeah.
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And I can say like how many brands that I've done analysis on that
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haven't done anything yet, and we're less than, we're less than s-
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we're like 30, 45 days from holiday.
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I know.
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You know, and, we don't have a commerce solution for the GEO and
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AEO and all of this new sales channel.
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We're usually just plug into a TikTok shop, or now there's
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an Instagram shop, right?
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Like it was a simple channel integration, and with this we
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need like a full tech stack.
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That's right.
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Like a new stack of software that does this
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because have a soup to nuts yet.
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Yeah.
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And so a lot of people, brands that I'm speaking to, they don't wanna sign
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seven contracts …when everything's so new and half …the stuff everybody's
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looking
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at is, pretty much vaporware.
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Well, and it's all like- changing- … point solutions… every week.
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Right?
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It's
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it's like if you want a specific thing, go buy this technology.
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Right
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… Sabrina McPherson: that's not… the problem.
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It's not
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of people are waiting because they don't
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wanna get into those contracts.
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They don't know which vendor to choose.
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The vendors are changing on a weekly narrative.
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But at same time, everything's moving so
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fast it's like you're losing more and more the more you wait
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yes, right.
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That's right.
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So you gotta something.
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You gotta do something
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Yeah.
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Yes.
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Yeah Agree.
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do you feel like commerce experiences need to start
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supporting both the human shopper
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and,
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aI agents?
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Oh, for sure.
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So right now it's absolutely two audiences, right?
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And I think that's also the big shift, and that, that takes work.
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So I think humans and AI obviously value different things, right?
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And you have to design commerce experiences for both audiences.
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Yeah.
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and I'm literally gonna talk about AI like it's a human, because it is-
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it is like a human.
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Yeah.
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So but real humans, right, still value the brand, experience, how it makes them
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feel- Mm-hmm … the imagery, the emotion that makes them want a product, right?
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But the AI, right, the machine, values really good, consistent
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data that it can trust.
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It wants walls of text.
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Yeah.
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Yes.
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Yes
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Where we close our laptop and put our phone in our pocket
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When we see a… when we see a wall of text.
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Right.
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Basically.
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but so, so you have, you have two audiences and they value different things.
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Yeah.
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And I think the opportunity right now is for companies to basically,
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you, you still need the human to want your brand, but you need to find a
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way to translate the product, the, the differentiation of your product
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and your brand into language- Mm-hmm.
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That the machine can read.
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Point blank.
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Yeah.
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Like, it sounds like i- obviously easier said than done,
but that's that
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whole tricky thing of like, what is the product data that I need, right?
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Right.
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How do I talk about- Right … what, what data do I put in about a wedding
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dress- that makes, the AI understand this wedding dress beyond just its price, and
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that it's a dress, and it's for weddings.
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And it's available here.
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Yeah.
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And it's white or it's cream.
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Right.
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Exactly, yeah.
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All, all the different ways you could describe it,
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that the AI might be looking for based on what the original
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search prompt was, right?
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Right.
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It has to be able to show up in, in that list but
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I think like even super random, but like wedding dress and dress
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for wedding, so not the same thing.
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Question, yeah
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Yes.
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And like the weather, right?
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Like- what- what- do wear for different seasons?
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Right.
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What kind of wedding is it?
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Right.
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I mean, all of that context, because it's intent-based search now
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Yeah
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… it all plays in more.
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Yeah.
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was never part of the product catalog.
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Yeah
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That's right.
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Well, and so I want to come back to, you, you, touched on a minute
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ago th- this trust element.
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Yeah.
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So in a sense, you're also looking for the machine, the AI, to trust-
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exactly … your information.
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Right.
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But not just on your website, right?
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You, you… Don't you also really want and need, I guess I should say you
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need other people- Yes … talking about your product, and you, need
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that to also be in alignment with how you're describing your product.
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Yeah,
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No, absolutely.
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So it's, it's two layers of trust, right?
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--> 00:11:09.867
We need the humans to trust the AI- Mm-hmm … and, and really believe that the AI
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is not just pushing a product, right?
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So, like, if you're going to Amazon and you're talking to chat, right?
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Mm-hmm.
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it's not gonna just push
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turns out.
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right?
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Right.
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Right.
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Whatever someone's paid for.
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Right.
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number one.
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And then the second thing is you need to trust-- the AI needs to trust.
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Again, AI is like a human.
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The AI needs to trust the data 'cause it's not gonna recommend your product
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if it feels that it's not consistent, if it's getting different signals
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across different channels- Mm-hmm
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if it doesn't think the inventory is accurate, right?
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Yeah.
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And so you-- this whole having clean data and consistent data, it's, it's huge.
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It's not even just about what data to have.
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It just needs to say the same thing across many different areas- Yeah … that are
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being, know Yeah … scraped by the aI.
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Yeah.
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And I coming back, to trust part, in the last, call it 10 days or so,
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right, there, there's new personal agent, obsession I guess I'll say
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out there when Meta released Muse, and they're not the only one, right?
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But these personal agents that it seems like a lot of people are
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experimenting with that if we go by the download numbers in the app stores.
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But at, at what level do you think consumers are really going to trust
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these agents to go out buy for them?
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Yeah.
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So I think it's, it-- we have a long to go, right?
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What is so hard that we need an agent to go buy it for us.
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I think, I think the use case first, we need to, like, narrow in on-
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Really understand use case
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I know
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Exactly.
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There's also that
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there's like function, utility, fun.
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00:12:29.221
What is this product category?
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I can say like, I need a list for certain complex product categories, but, like,
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I think we have to really nail down what is the merchandise category that the
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agents will have the autonomy to shop for.
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Yeah, I'd like some commoditized category
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… Casey Golden: that's not… that's hard to shop- and not fun..
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00:12:47.066 --> 00:12:47.521
Yeah.
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right
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like, or like toilet paper.
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Right.
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00:12:49.675 --> 00:12:50.672
like, but that's like easy
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00:12:50.692 --> 00:12:51.503
yeah, that's an easy one.
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yeah, get that off, get that off my
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plate
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button, right?
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So it's like, it's something hard that's not fun.
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And I think i'd like to… I don't know, I think I wanna make a whole
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list of, what is that product category?
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00:13:00.091 --> 00:13:02.384
You should ask, ask chat what it thinks.
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00:13:02.782 --> 00:13:05.485
but I, but realistically, right, there's also everyone's like, "Oh, the
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00:13:05.485 --> 00:13:08.764
trust spectrum from, like, we used to trust, like, AI to, to give us some
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00:13:08.765 --> 00:13:12.664
responses in chat or in, in search to we're gonna have AI purchase."
341
00:13:12.664 --> 00:13:13.962
Like, there's a huge spectrum there.
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00:13:13.989 --> 00:13:14.214
Yeah.
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00:13:14.256 --> 00:13:16.881
And y- I mean, we all know that trust doesn't go from here to there, right?
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There's no jumping five levels ahead on no Trust gained as you go.
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00:13:21.379 --> 00:13:25.826
And Right people are experimenting with, with all the new personal assistants, but
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00:13:25.841 --> 00:13:27.842
we all know things are gonna go wrong.
347
00:13:27.904 --> 00:13:29.346
So it's gonna go this way.
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People are gonna fiddle, and that's a very small part of the population as well.
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00:13:32.245 --> 00:13:34.147
And then they're gonna go back to being like- Yeah … "Eh, I don't
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00:13:34.158 --> 00:13:36.418
trust it. It's hallucinating. It's, like, telling me that this thing is
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00:13:36.419 --> 00:13:39.090
available, trying to buy something for me, and then I got a back, like a
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00:13:39.093 --> 00:13:40.734
pushback," We have a long way to go.
353
00:13:40.787 --> 00:13:41.120
Yeah.
354
00:13:41.935 --> 00:13:44.411
What do you think this means for, like, retail and brand
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00:13:44.415 --> 00:13:47.177
agents, versus the personal assistant?
356
00:13:47.196 --> 00:13:51.128
A lot of sites are starting to block other bots, and right?
357
00:13:51.184 --> 00:13:53.331
And that is a huge trust issue, right?
358
00:13:53.353 --> 00:13:58.524
So I think for a retailer or brand to get over the hurdle of a human not
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00:13:58.533 --> 00:14:01.735
thinking or thinking or not trusting that they're gonna actually bring
360
00:14:01.747 --> 00:14:04.834
back a selection of something that meets their needs versus pushing
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00:14:04.834 --> 00:14:06.711
something that someone paid for- Yeah.
362
00:14:06.758 --> 00:14:08.543
That's gonna take a long time, right?
363
00:14:08.544 --> 00:14:10.737
Like- Yeah … th- there's already so much distrust in
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00:14:10.737 --> 00:14:11.999
like, every- I mean, I, I know.
365
00:14:12.101 --> 00:14:12.831
I'm sure- Yeah … you guys feel it.
366
00:14:12.834 --> 00:14:14.902
Every time you go on, you're " like, oh, who paid for that to-
367
00:14:15.138 --> 00:14:17.279
surface- in my selection . Right.
368
00:14:17.476 --> 00:14:18.891
So if you have your own brand,
369
00:14:18.951 --> 00:14:22.541
Well, so So much, I mean, I have to go double check to make, see
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00:14:22.542 --> 00:14:25.825
if this is a dupe of something else, because because never heard the brand.
371
00:14:25.830 --> 00:14:26.467
Is this fake
372
00:14:26.473 --> 00:14:26.997
website?
373
00:14:27.301 --> 00:14:27.621
Right.
374
00:14:27.701 --> 00:14:29.733
Because, like, there's so many.
375
00:14:29.776 --> 00:14:31.516
I'm like, i'm trying to find the real one.
376
00:14:31.825 --> 00:14:32.386
Yeah Right.
377
00:14:32.387 --> 00:14:32.974
No, exactly!.
378
00:14:33.096 --> 00:14:33.743
I like it,
379
00:14:33.766 --> 00:14:35.091
but like is this the real one?
380
00:14:35.164 --> 00:14:35.384
Yeah.
381
00:14:35.384 --> 00:14:36.752
Or with, within a brand, right?
382
00:14:36.752 --> 00:14:39.257
Even if it's in the same brand, I'm gonna always just assume that they're
383
00:14:39.258 --> 00:14:41.755
trying to show me something that they're trying to offload because
384
00:14:41.757 --> 00:14:42.889
they have too much inventory of it.
385
00:14:42.943 --> 00:14:43.108
Right.
386
00:14:43.108 --> 00:14:44.573
Not because it's the right fit,
387
00:14:44.611 --> 00:14:46.152
we're too savvy of shoppers.
388
00:14:46.798 --> 00:14:48.591
But like, I'm gonna be like, "Well, okay,
389
00:14:48.592 --> 00:14:49.940
this is like the day old eggs.
390
00:14:50.126 --> 00:14:51.497
Like, you're trying to sell me day old
391
00:14:51.518 --> 00:14:51.981
eggs!
392
00:14:52.060 --> 00:14:52.606
Exactly.
393
00:14:52.649 --> 00:14:52.805
Yeah.
394
00:14:52.805 --> 00:14:54.801
because I want the best egg for my need
395
00:14:54.969 --> 00:14:55.231
Yeah.
396
00:14:55.286 --> 00:14:55.583
So..
397
00:14:55.764 --> 00:14:57.092
Well, and there's definitely one, like, I think it
398
00:14:57.092 --> 00:15:00.660
was maybe couple months ago that, someone, an influencer staged a
399
00:15:00.660 --> 00:15:04.692
direct-to-consumer, beauty product on website that was completely made up.
400
00:15:05.104 --> 00:15:07.915
But they created the entire site, they gave it all sorts of product
401
00:15:07.915 --> 00:15:11.822
descriptions, and within two weeks it started showing up in results on chatGPT.
402
00:15:11.918 --> 00:15:12.990
Oh, I mean…
403
00:15:13.029 --> 00:15:13.864
But it was a fake product.
404
00:15:13.864 --> 00:15:15.299
It didn't exist and it was getting recommended.
405
00:15:15.420 --> 00:15:16.033
Exactly.
406
00:15:16.099 --> 00:15:17.191
So yeah, there's, there's that.
407
00:15:17.706 --> 00:15:19.928
… well, Sabrina, thank you so much for, for joining us.
408
00:15:19.945 --> 00:15:21.162
This has been great conversation.
409
00:15:21.163 --> 00:15:23.895
We'll have to have you come back in the future to really dig into,
410
00:15:24.103 --> 00:15:25.477
as this is constantly changing.
411
00:15:25.752 --> 00:15:26.745
Exactly.
412
00:15:26.843 --> 00:15:27.952
Don't forget your sunscreen!
413
00:15:28.020 --> 00:15:28.033
That's right
414
00:15:28.388 --> 00:15:28.915
Sounds good.
415
00:15:28.924 --> 00:15:29.314
Thank You!
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00:15:29.330 --> 00:15:29.576
Right.
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00:15:29.613 --> 00:15:30.423
Thanks everybody
418
00:15:37.281 --> 00:15:39.904
I know you loved this episode, so drop us a five-star
419
00:15:39.905 --> 00:15:43.318
rating and review on Apple Podcasts, Spotify, or Goodpods.
420
00:15:43.354 --> 00:15:46.530
And if you're watching us on YouTube, like and subscribe before you go.
421
00:15:46.772 --> 00:15:47.740
I'm Casey Golden
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00:15:48.131 --> 00:15:51.017
Follow us on LinkedIn, Bluesky Threads, and Instagram,
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00:15:51.249 --> 00:15:54.051
and subscribe to our Substack for highlights and bonus content.
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00:15:54.364 --> 00:15:57.083
For transcripts and guest info, visit retailrazor.com.
425
00:15:57.183 --> 00:15:58.116
I'm Ricardo Belmar
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00:15:58.563 --> 00:16:01.176
Thanks for joining us on Retail Transformers, part of
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00:16:01.177 --> 00:16:02.854
the Retail Razor Podcast Network.
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00:16:03.307 --> 00:16:05.538
Until next time, stay sharp, be bold,
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00:16:05.597 --> 00:16:07.195
and keep transforming retail.
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00:16:07.251 --> 00:16:09.761
This is the Retail Razor Retail Transformers!