Shelf Intelligence Reality Check: Why Only 9.1% of Retailers Are Ready

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Every retailer says they're investing in shelf
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intelligence, cameras, smart shelves, ESLs, computer vision.
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But IHL just dropped a number that should make every retail
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CIO, COO, and CFO very uncomfortable.
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Only nine point one percent of retailers have current computer vision deployed.
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The hardware is everywhere.
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The intelligence isn't.
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In this episode of Data Blades, Greg Buzek of IHL joins us to
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explain the whole tech stack, RFID, smart shelves, cameras, plus why the
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network underneath the shelf is the thing nobody's really talking about and why
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it may be the only thing that matters.
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Welcome to season two, episode seven, retail data junkies.
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This is the Retail Razor Data Blades, the podcast that slices through complex
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retail research to bring you sharp, actionable insights you can use today.
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I'm Ricardo Belmar.
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And I'm Casey Golden.
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Today, we're kicking off a new three-part AI in retail series.
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We're super excited about this.
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We're partnering with the team at IHL Group on three episodes built
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around 2026 Retail Transformation Study, How Retail Leaders Outperform.
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Which, if you weren't aware, is the largest annual survey of retail
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technology leaders in the industry.
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And to take us through the data, we've got one of the most respected
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analysts in the space and good friend of the show, Greg Buzek, president, founder,
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and chief AI orchestrator of IHL Group.
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In today's episode, we're talking about shelf intelligence, cameras,
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ESLs, computer vision, smart shelves.
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The technologies that have been on every retailer's
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investment list for three years.
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And here's the wrinkle in the data, though.
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Every retailer says they want this stack, but almost none have built the tech
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stack underneath to actually run it.
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Only nine point one percent have current computer vision, and only twelve
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point two percent have current ESLs.
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So today, we're digging into why that gap exists and what it's costing
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the retailers who don't close it.
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But before we dive in, let me tell you about our Retail Razor Podcast
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Network sponsor, Retail Club.
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Join two thousand retail leaders at the Retail Club AI Festival,
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September twenty-second to twenty-four in Huntington Beach.
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Dive deep into how AI is reshaping retail while soaking up the sun at
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a fully outdoor beachside venue.
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Decision-makers from retailers and brands can attend with free tickets
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Head to retailclub.com/retail-razor-podcast to
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learn more and get your ticket today.
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Thank you, Retail Club, for helping us bring you this podcast
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and all the podcasts in the Retail Razor Podcast Network.
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Now, we also have a quick favor to ask you, our audience.
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If you enjoy the show, hit us with a five-star rating and review.
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Please drop those short reviews on Apple Podcasts, Spotify, Goodpods,
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or wherever you're listening.
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And don't forget to like and subscribe to our YouTube channel
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so you never miss an episode.
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And check out the other shows in the Retail Razor Podcast Network,
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the Retail Razor Show, the OG, Retail Transformers, and Blade to Greatness.
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You'll find them all in your favorite podcast app and our YouTube channel.
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Now, let's kick off part one of this three-part series
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and welcome Greg Buzek, president and founder of IHL Group, to the show!
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Welcome to the Retail Razor Data Blades Show, Greg.
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We're excited to kick off this three-part series with you, dig into some of
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the AI and retail insights that you and the IHL team have been gathering,
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analyzing for months or almost a year or two now, just about, you've
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been really digging into this data.
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It's kind of hard for me to believe it's taken us this long to get you on
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the show and to have this discussion.
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It's really surprising.
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know, after everyone-- it's like this is
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made for you, and the work you're
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Yeah, da-data blades for
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Exactly.
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Exactly.
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So some of our Retail Razor Show fans may remember you from previous episodes
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where a completely different topic, we were talking about retail ROI and
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all the great work helping children around the world happening there.
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But today, we're really diving in on the data around AI in retail.
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It's great to have you here, Greg.
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Yeah.
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Well, hey, it's great to be with you, Ricardo and Casey.
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Hey, just real quick, one Retail ROI thing that's gonna be exciting is
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literally this week we will send out our grant that surpasses six million dollars
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total for the organization through Super Saturday, and people come into that.
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So we're real excited about that.
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Congratulations.
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That is fantastic.
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Excellent!
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And what a great way to kick off this Ai in Retail series by talking
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about a topic that is getting more and more attention these days as store ops
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moves to a m- a mode where everything is booming data-led and data-centric.
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An area where retail tech investment is having a massive impact.
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And of course, we're talking about shelf intelligence.
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And you've got a recently published study, your 2026 Retail Transformation study
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that we're gonna be diving into today.
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Yeah, the study, we looked at over four hundred brands
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through interviews there, and shelf intelligence was a big part of that.
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Not only do we have that as part of the core study how winners outperform,
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how retail leaders outperform, but we actually have separate shelf intelligence
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reports that breaks this down into great detail just for this as well.
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Fantastic.
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So let's dive into to this series.
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This is part one, and let's really talk about what's happening with
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shelf intelligence in stores.
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So, Greg what does it actually mean for a retail store in two thousand
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twenty-six to be implementing and deploying shelf intelligence?
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What does that tech stack actually look like?
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Well, shelf intelligence traditionally has just been the
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manager walking around and looking what's empty ty- sort of thing.
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However, you know, particularly in the last several years with COVID in
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particular, and then launching into everything from tariffs to wars and other
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things, we've had all these disruptions that have occurred in the supply chain.
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And really when you add to that the level of theft that has been happening
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at stores in certain regions the-- just knowing what you have on the shelf
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has become all the more critical, as well as the rise in alternate channels
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than just walking in the store and picking something off the shelf.
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When you're fulfilling from the store for a local delivery or a buy online
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and pickup in store or a ship from the warehouse there, or even ship,
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ship store to store, you need to know what you got, and you need to
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know when those trucks are coming in.
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So shelf intelligence has become all the more important in terms of profitability.
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And some of the tools now have... are, are necessary because, like you said the
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rise in shrink, you think you have it, but you don't really know that you have
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it unless you're able to verify that.
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And and so you can lose a lot of time and a lot of margin with labor
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walking around a store trying to find something that's supposed to be there.
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So, so what are some of the different tools you're seeing
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now that you're finding from recent studies that are actually-- that
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retailers are putting into stores that are helping them win this battle?
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Yeah, it's a variety of different technologies.
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The most consistent technologies are computer vision and RFID that
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we're seeing as well as electronic shelf labels that, that help with
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the pricing side of the equation.
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But then we're now seeing autonomous robots that are being
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used and all of that requires increased networking and bandwidth.
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So there's like a level at which you do all these things, where you've got
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to have the, the compute at the store level, in most cases, to sort through the
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data because the amount of data that's being processed, but then you got to
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have the network that can support that as well, both the-- in the local LAN
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as well as the WAN for things there.
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And everybody's got a different view.
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I mean, there are robots that go up and down the stores where they
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process all that video and all that data in the robot itself.
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There are others that do it on the edge, and so they're pumping that
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over the local network To process.
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And then there are others that are doing it in the cloud, which
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is the most network intense.
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So those are some of the different technologies that are
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being deployed, and, and each situation's a little bit different.
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One that's becoming more and more controversial right now
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is electronic shelf labels.
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You know, it's funny during COVID, when you couldn't get people to
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work at, at the stores the unions didn't push back at shelf labels.
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And after twenty years of holding electronic shelf labels back,
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they didn't push back on that.
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And so all these retailers have deployed them, and they have them
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now, and that allows them to do price, price changes as necessary., Most
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any retailer I know is doing price, price changes at-- in the evening.
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They're just not pumping them down in real time there.
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But but the unions are now pushing back on those technologies, saying,
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"Oh, they're just changing it during the day or customer to customer type
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of thing." And so that's the one that's got some controversy to it.
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Uh, RFID is another one of those technologies that for twenty-five
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years started to grow, started to grow.
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But once COVID happened and we went to this proliferation of jour-- of journeys
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for customers, knowing what you have and where it is specifically in the
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store became all the more important.
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And that is one of the most compelling from a financial return technologies
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that we've seen, particularly for all those other journeys that have
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a store fulfillment process to them.
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Because knowing where it is and how-- and specifically that size eight dress
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is sitting in a dressing room rather than on a shelf, and you're shipping
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it from your store, the margin's gone if you can't find it, it's not on the
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rack, and you're just walking around.
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You've just paid somebody for an hour's worth of work that should've
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taken three minutes, and the end result of that is you don't have it.
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Even further, knowing you actually have it in the store when the
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transaction is occurring is all the more important as well.
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I don't know how many times, I couldn't possibly count, that I've
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offered to go in the back stockroom of a shoe department to look for the last pair
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Yeah.
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They won't let me.
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Yeah.
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It's, uh, it's really funny.
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That is one of the areas where RFID and even visual camera handheld
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intelligence is great, you know.
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Because RFID can say, "Hey, it's right here But you can actually hold up a
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camera scan that's got an engine where you just hold up the camera and they
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say, "Oh, it's that box right over there," that specific box that it's
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looking for, for that, for the shoes.
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Macy's was like one of the first ones to deploy RFID in that back room for
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shoes, and I think they saw close to fifty percent uptick in sales
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wouldn't doubt it.
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They were a terrible offender on my end.
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Yeah, exactly.
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So they, they knew where that was, and they were one of the first
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ones to deploy RFID at this level.
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But it's become-- It's gone down all the way, at Walmart now,
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down to items under a dollar that they're tagging with RFID tags.
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Just to, because you, like you said, you're fulfilling from
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store stock, you need to know where that stuff is at all times
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yeah, you gotta know the whole accurate inventory.
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Yeah.
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How much of what you're seeing here is based on computer vision versus
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RFID or are you starting to see a combination of the two in the best cases?
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Yeah, RFID is more popular.
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It's still relatively low.
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Overall, it's it's about twenty percent of retailers overall
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that are, are running RFID today.
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You see it more in fashion.
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And in fashion you actually see it through checkout as well.
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So you've got Uniqlo and Zara and others doing the RFID checkout.
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The beauty of that is you don't have as much interference
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with RFID in that environment.
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And when you are dealing with color and size, knowing which specific item
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you have is all the more important and where that is in the store.
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You see, you're seeing it more now, though, in the mass merchants
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and the consumer packaged goods.
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This is another one of these technologies that's a twenty, you know, twenty-five
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year, overnight sensation so to speak.
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In the sense that Walmart pushed RFID in the supply chain quite a bit in
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the early 2000s, and and it really didn't go anywhere until we got to
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COVID and then all of a sudden, as we started shipping from store and local
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delivery and everything else, the use case became very, very apparent.
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And further, as we saw the rise in theft happening at the stores, that real-time
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nature of knowing I actually do have this, has become all the more critical.
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And so when you think of that, RFID is one there.
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And then the computer vision is, is being added.
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It, it's mostly been added as an anti-theft component at self-checkout.
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That's what people think of mostly.
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But it's also being used at the shelves and looking at the shelves
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and, and optimizing the shelves.
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And that's everything from computer vision as just simply a, a picture.
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There are some solutions where they just take a picture every eight feet
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And they upload the pictures to actual, you know, real video that they're,
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that they're either using their CCTV cameras for or the robots that are going
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through the store do that The adoption and the acceptance of the autonomous
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robots has, has been greatly enhanced in the last two years with, uh, what
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we've seen at Sam's and other locations where people are just getting used to.
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I think Schnucks Market with the Simbe Ta-Tally was one of the first ones.
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But we now have robots as a service that are now being deployed where
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you don't necessarily need the robot every day, all day, but maybe three
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times a week you might wanna have the robot come through the store.
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So they just literally bring it in, set it up, and, you know, program
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it, and it just goes through things.
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So a lot of this has to do with compliance, not only shelf compliance
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in terms of space planning, but also the fact that you, particularly in
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mass merchants and supermarkets, you've got trade dollars that are coming
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down if you have an end cap that has on it what it's supposed to have.
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So the ability to monitor those compliance for trade dollars is,
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is usually the, the situation, the difference between being profitable or
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not profitable for a lot of retailers.
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And supermarkets, I always laugh.
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Everybody thinks supermarkets, it's about location, location, location,
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and about where people shop.
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And it's like, really?
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Yeah, yes and no, but actually most retailers make their money from the
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packaged goods companies and the brands that are selling through that, which kind
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of leads us into this whole retail media networks component of things as well.
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So when you have that real-time data about inventory, now you can
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actually plug that into what you're doing with the media networks for
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what you're pushing at the stores.
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There are some studies that show, like in fast food, for instance based on how
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much you have of any particular menu item, if you just add a little animation,
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like a little smoke coming off the burger type thing, you can increase the sales
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of those items by twenty-five percent.
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And so when you start factoring that in with your data being accurate at the
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store level, those can be incredible top-line movers in the profitability.
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Where are, where would you say we are so far?
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I mean, how many retailers are using computer vision?
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It's only, it's less than 10% overall that are using computer vision.
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And this is another one of those that gets to be a little controversial in
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the sense that is the computer vision looking at that person and storing
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their information or their image, or is it just looking for behaviors?
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How does that work?
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There are all different methods, and each, each vendor has their own.
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Those that focus on Europe, everything's anonymized there.
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But we're seeing more and more.
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In fact, our next one of our next webinars is gonna be on the value
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of facial recognition, and not only for, for loss preven- for
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theft, but also for slip and falls.
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And what you're realizing is by building these, this network of data, you actually
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see these people come into other stores that are, you know, going on slip and
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falls, and they're, creating fraud.
283
00:16:47.130 --> 00:16:51.720
But what's interesting when you use it for loss prevention is when
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that comes in, it's like, "Hey, wait, that person's in the database
285
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for theft for something else."
286
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It's like n-now we can be proactive to prevent that theft
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from happening, or we could alert authorities if it was big enough to
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to challenge them as they come in.
289
00:17:09.040 --> 00:17:11.110
And that, that becomes a, a privacy thing.
290
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And the, the data is incredible though when you do it.
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74% reduction in your theft rates when you use facial recognition to find
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the two or three percent of the people that are, that are stealing versus
293
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the ninety-seven percent that are not.
294
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And that's a cultural thing.
295
00:17:28.890 --> 00:17:32.670
In China, it's just over there, it's accepted and it's everywhere.
296
00:17:33.170 --> 00:17:35.930
And here in the States it's accepted or not accepted.
297
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Yeah.
298
00:17:37.065 --> 00:17:37.505
I mean, a few
299
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I don't care.
300
00:17:38.080 --> 00:17:39.630
I'm gonna be-- I'm honest, I don't care,
301
00:17:40.265 --> 00:17:42.735
few bad actors kind of ruin it for everyone.
302
00:17:42.805 --> 00:17:45.395
I really don't want to be surveilled while I'm shopping.
303
00:17:46.570 --> 00:17:46.630
Yeah.
304
00:17:46.880 --> 00:17:47.180
Yeah.
305
00:17:47.630 --> 00:17:48.820
Well, let's look at it this way.
306
00:17:48.820 --> 00:17:50.820
Another way is r-red light cameras, you
307
00:17:50.830 --> 00:17:52.000
Yeah.
308
00:17:52.025 --> 00:17:52.975
Yeah, I'm not a fan of that either.
309
00:17:52.975 --> 00:17:53.095
If you
310
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with red light cameras are all of us that run red lights, you know.
311
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Or speed, you know.
312
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Yeah.
313
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Yes, I g-
314
00:18:00.340 --> 00:18:01.530
And so it's the same there.
315
00:18:01.530 --> 00:18:05.230
The only people that really have a problem with facial recognitions are the people
316
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that are doing something wrong, you
317
00:18:07.325 --> 00:18:07.625
Yeah.
318
00:18:07.725 --> 00:18:10.775
I mean, I can s- I can see both sides for sure.
319
00:18:11.295 --> 00:18:16.776
But it is having that data, big impact, but definitely has to have a
320
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thoughtful strategy on compliance and purpose and how they're communicating
321
00:18:20.926 --> 00:18:22.406
that to customers, for sure.
322
00:18:22.621 --> 00:18:27.231
Yeah, and I think the, uh, the vendors need to do a better
323
00:18:27.231 --> 00:18:30.941
job and the retailers would need to do a better job of when it's comes
324
00:18:30.941 --> 00:18:34.241
to facial recognition, explaining how the technology actually works.
325
00:18:34.951 --> 00:18:39.401
It's just like when your, you know, Your phone is not using a picture of your face.
326
00:18:40.091 --> 00:18:44.731
It's using measurements that come together that, that produce that.
327
00:18:44.961 --> 00:18:49.661
So, it-- where they may have video that stores somebody coming in and
328
00:18:49.661 --> 00:18:53.041
they can pull up that video based on the characteristics of that person.
329
00:18:53.501 --> 00:18:56.981
It is not a, a like they do in the movies where, oh, I got all these
330
00:18:56.981 --> 00:18:58.371
faces going and then, you know.
331
00:18:58.851 --> 00:18:59.991
And that's, that's the one.
332
00:18:59.991 --> 00:19:02.351
That's, that's not how, that's not how this works.
333
00:19:02.801 --> 00:19:05.591
It's too-- that's too, uh, process-intensive.
334
00:19:06.131 --> 00:19:10.641
It works by taking these algorithms and, and your face is put in, in it,
335
00:19:10.641 --> 00:19:16.371
and those measurements are done, and they're in a a range, and that's what
336
00:19:16.381 --> 00:19:21.231
allows it to work, and so it's just-- it's data, not your actual face.
337
00:19:23.038 --> 00:19:27.518
What does the data show about the business cases?
338
00:19:27.638 --> 00:19:32.068
When retailers invest in shelf intelligence, what
339
00:19:32.068 --> 00:19:33.948
do they expect to get back?
340
00:19:34.488 --> 00:19:38.798
I mean, if you're going in for an initial deployment, what does
341
00:19:38.898 --> 00:19:42.358
that ROI calculation look like?
342
00:19:43.308 --> 00:19:44.738
What's that business impact?
343
00:19:44.813 --> 00:19:49.973
Yeah, the return can range everywhere from three months up to about
344
00:19:49.973 --> 00:19:54.563
twelve to fourteen months, depending on the segment and depending on the business
345
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model of the vendor that is deploying it.
346
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And I think that's been one of the challenges that have held back some
347
00:20:00.353 --> 00:20:01.963
of these technologies for a long time.
348
00:20:02.443 --> 00:20:06.363
That traditional hey, you pay for it all up front type of
349
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thing it's held people back.
350
00:20:08.463 --> 00:20:12.903
The most ideal aspect of things is at least in the pilot stages or
351
00:20:12.903 --> 00:20:18.833
early stages, "Hey, I, I'll pay you as we see results of your solution,"
352
00:20:18.833 --> 00:20:20.343
is what the retailer wants to do.
353
00:20:20.343 --> 00:20:24.223
But that typically when they do see the results coming in, and it's like a revenue
354
00:20:24.223 --> 00:20:29.063
share based on the results is how it's paid for, they quickly move to a no.
355
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If it's working really good, they change that very, very quickly to,
356
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"Hey, I'll just pay the license fee, uh, now," and cap that because
357
00:20:37.503 --> 00:20:41.353
when the technology really, really works it's a win-win for everybody.
358
00:20:41.693 --> 00:20:43.043
And and we're seeing it.
359
00:20:43.073 --> 00:20:44.613
We're seeing it with these technologies.
360
00:20:44.663 --> 00:20:48.593
RFID is other than handheld devices for managers.
361
00:20:49.273 --> 00:20:54.203
We've never seen a technology with a stronger alignment with the people that
362
00:20:54.203 --> 00:20:57.723
are actually winning in their sales growth of greater than ten percent.
363
00:20:57.723 --> 00:21:01.673
So we, we define winners as those who increase their sales by ten percent a
364
00:21:01.673 --> 00:21:03.793
year or their profits ten percent a year.
365
00:21:04.303 --> 00:21:08.763
And RFID, the sales winners are over six and a half times more
366
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likely to be using RFID technology.
367
00:21:11.673 --> 00:21:13.523
Computer vision is another one of those.
368
00:21:13.523 --> 00:21:14.293
It's a little less.
369
00:21:14.333 --> 00:21:19.433
It's in the two to three timers range, but still extremely strong
370
00:21:19.433 --> 00:21:21.633
results when, when that's done.
371
00:21:21.663 --> 00:21:25.313
But like I said, you can't just put these technologies in by themselves.
372
00:21:25.353 --> 00:21:30.063
You have to have the network, you have to have the edge devices to process that.
373
00:21:30.193 --> 00:21:36.383
So for instance, like RFID, you don't care other than the tag is still there.
374
00:21:36.573 --> 00:21:36.833
mm-hmm
375
00:21:37.343 --> 00:21:38.583
You don't need to process that.
376
00:21:39.134 --> 00:21:40.474
Other than the tag is still there.
377
00:21:40.474 --> 00:21:45.394
You need to process what's moves, and if that's moved, where has it moved to?
378
00:21:45.824 --> 00:21:51.924
So how do you sort through a read that has, ten thousand items in it and you
379
00:21:51.924 --> 00:21:54.134
only care about the fifty that have moved?
380
00:21:54.644 --> 00:21:56.004
Where does that processing get done?
381
00:21:56.894 --> 00:21:58.354
And does it get done in the device?
382
00:21:58.354 --> 00:22:00.644
Does it get done in the a-access point?
383
00:22:01.334 --> 00:22:02.944
Does it get done at an edge server?
384
00:22:03.274 --> 00:22:07.274
Every retailer looks at that differently, and that, that is a calculation that
385
00:22:07.274 --> 00:22:08.654
you have to take into consideration.
386
00:22:09.054 --> 00:22:13.274
As well, do you-- would you want just a handheld where somebody has to be
387
00:22:13.274 --> 00:22:15.294
consistent in what they're doing?
388
00:22:15.294 --> 00:22:18.074
They make the first time they do a really great job, and the second time
389
00:22:18.074 --> 00:22:21.414
they're just kind of doing one of these, just keep kind of giving it away.
390
00:22:21.874 --> 00:22:24.344
Or do you want to have perpetual RFID?
391
00:22:24.394 --> 00:22:28.474
We've done some case studies where it's, it's less than three months, the, the
392
00:22:28.474 --> 00:22:33.244
perpetual RFID return because you know exactly where the product is in the
393
00:22:33.244 --> 00:22:37.644
store, and thus you've saved all that labor in finding it, where it's not good
394
00:22:37.644 --> 00:22:39.194
enough that you knew where it was Sunday.
395
00:22:40.104 --> 00:22:41.654
Do you know where it is right now?
396
00:22:42.304 --> 00:22:46.664
And you can find, oh, somebody put it back on the wrong rack in, in that environment.
397
00:22:46.694 --> 00:22:50.974
So, every, every use case is different, every retailer is different, and
398
00:22:50.974 --> 00:22:54.394
you've got to work through those calculations, but the data for
399
00:22:54.434 --> 00:22:56.504
all of these is extremely strong.
400
00:22:56.684 --> 00:23:00.124
And even shelf labels now with the tariffs, the tariffs
401
00:23:00.124 --> 00:23:01.594
are on, the tariffs off.
402
00:23:01.944 --> 00:23:04.384
We can get the merchandise, can't get the merchandise.
403
00:23:04.774 --> 00:23:06.364
There's a blockage, there.
404
00:23:06.364 --> 00:23:12.734
There's a drought in the Panama Canal, so you know, there's the Strait of Hormuz.
405
00:23:12.734 --> 00:23:18.904
All-- You've got all of these different things that are at play and the
406
00:23:18.904 --> 00:23:24.464
ability to change those prices in real time to take advantage of that.
407
00:23:24.464 --> 00:23:28.084
And I want to say real time, once again, overnight, to take advantage
408
00:23:28.084 --> 00:23:30.484
of what's going on is a big, big deal.
409
00:23:30.549 --> 00:23:30.859
Yeah.
410
00:23:31.079 --> 00:23:31.399
Yeah.
411
00:23:31.749 --> 00:23:36.019
Are there-- Do you have a sense from the data, like what are people expecting?
412
00:23:36.329 --> 00:23:37.729
What are their top expectations?
413
00:23:37.729 --> 00:23:41.479
Is it-- Are they trying to see improvements in customer satisfaction?
414
00:23:41.479 --> 00:23:43.319
Is it just the inventory accuracy?
415
00:23:43.319 --> 00:23:44.769
Are they looking for a tie to revenue?
416
00:23:44.809 --> 00:23:45.589
All the above?
417
00:23:46.124 --> 00:23:46.614
Yeah.
418
00:23:46.694 --> 00:23:49.944
Yeah, that customer satisfaction's always gonna be number one.
419
00:23:50.214 --> 00:23:52.934
Just that the fact that they can have on the shelf what the customer wants
420
00:23:52.934 --> 00:23:54.104
to buy when they want to buy it.
421
00:23:54.789 --> 00:23:55.649
That's number one.
422
00:23:55.689 --> 00:24:00.469
Because th- literally that is the single biggest criteria
423
00:24:00.469 --> 00:24:02.569
that retailers get measured on.
424
00:24:03.039 --> 00:24:06.549
You go-- I mean, let, let's face it, it's too easy to buy online now.
425
00:24:07.239 --> 00:24:10.329
You go to stores because you expect that store to have what
426
00:24:10.329 --> 00:24:12.299
you wanna buy when you go there.
427
00:24:12.839 --> 00:24:17.029
And we've done some research with Amazon Prime members who actually
428
00:24:17.029 --> 00:24:18.769
go to physical stores to shop.
429
00:24:19.499 --> 00:24:24.869
And so basically, I think it was at the time seventy-four percent of US
430
00:24:24.869 --> 00:24:29.209
households had Amazon Prime at the time, but that's over ninety percent
431
00:24:29.209 --> 00:24:31.659
of those with incomes over $100,000.
432
00:24:32.289 --> 00:24:36.349
And those people go to stores because they expect you to have it, and if
433
00:24:36.349 --> 00:24:41.509
you don't, they just whip out the phone and buy it with express delivery
434
00:24:41.899 --> 00:24:45.299
there, and they often don't look at that retailer again for that item.
435
00:24:45.799 --> 00:24:49.259
And that becomes a death spiral if you don't, if you don't have it.
436
00:24:49.259 --> 00:24:51.949
So that customer satisfaction piece is critical.
437
00:24:52.429 --> 00:24:53.459
So that's number one.
438
00:24:53.939 --> 00:24:59.119
But it's also lowering the labor cost, lowering the actual cost of operations.
439
00:24:59.429 --> 00:25:02.199
How much are you wasting in safety stock because you don't
440
00:25:02.199 --> 00:25:03.289
know what's on the shelves?
441
00:25:03.319 --> 00:25:07.469
I mean, my joke has always been for people, if it says limited supply on
442
00:25:07.469 --> 00:25:12.769
the website, don't even bother going to the store because they don't know.
443
00:25:12.769 --> 00:25:14.969
They d- they, they just literally do not know
444
00:25:15.119 --> 00:25:15.369
yeah.
445
00:25:15.489 --> 00:25:15.779
that's what it is.
446
00:25:15.779 --> 00:25:17.339
code word for we have no idea.
447
00:25:17.859 --> 00:25:19.309
Yeah, and the chance is there.
448
00:25:19.539 --> 00:25:20.859
But even then I'll give you.
449
00:25:20.859 --> 00:25:23.949
I was at a home improvement retailer, and I had to get... We had our roof
450
00:25:24.159 --> 00:25:29.469
repaired, and we've got dogs a-and, they try to get all the nails up, but I
451
00:25:29.469 --> 00:25:33.309
wanted to buy one of those, the magnetic sweepers to get all the nails up.
452
00:25:33.889 --> 00:25:35.329
It said they had fifteen in stock.
453
00:25:35.469 --> 00:25:37.089
I go there, there's none on the shelf.
454
00:25:37.749 --> 00:25:42.059
And the guy goes, "There's-- they're here somewhere. They're here somewhere.
455
00:25:42.059 --> 00:25:43.459
They're up on a shelf somewhere."
456
00:25:43.899 --> 00:25:44.169
Mm.
457
00:25:44.279 --> 00:25:48.859
And he didn't have the ability to like, like ScanIt has got a, an image
458
00:25:48.859 --> 00:25:52.109
the image thing that you could just, you just hold it up and it just lights up
459
00:25:52.109 --> 00:25:54.539
and says it's that one right over there.
460
00:25:54.759 --> 00:25:55.839
Didn't have the ability to do it.
461
00:25:55.839 --> 00:25:58.849
So they're in stock, said fifteen, I left without it
462
00:25:59.194 --> 00:26:01.834
I would have stood there and be like, "I'll wait."
463
00:26:02.789 --> 00:26:05.449
Yeah, I went to the, I went to the other competitor.
464
00:26:05.769 --> 00:26:06.769
I went to the other competitor
465
00:26:06.974 --> 00:26:08.864
It just sounds like a training moment.
466
00:26:09.689 --> 00:26:09.989
Yeah.
467
00:26:11.199 --> 00:26:12.899
Or I've been... Laptops.
468
00:26:13.229 --> 00:26:16.989
I mean, my goodness, where I'm spending two grand, and it says it's in stock
469
00:26:16.989 --> 00:26:18.529
and the associate can't find it.
470
00:26:18.829 --> 00:26:22.839
And you spent fifteen, twenty minutes to do that, and you just, you whip out
471
00:26:22.839 --> 00:26:26.999
the phone and says, "I, I'm not dealing with this. I'm just gonna order it
472
00:26:26.999 --> 00:26:31.029
where I know I can get it delivered." So it's an existential, existential threat
473
00:26:31.499 --> 00:26:34.379
to not have accurate inventory there.
474
00:26:34.429 --> 00:26:36.979
But just the cost of each one of these interactions.
475
00:26:36.979 --> 00:26:39.859
That guy spent fifteen minutes going around for something
476
00:26:39.859 --> 00:26:41.269
that was like a $20 item
477
00:26:41.454 --> 00:26:41.874
Right.
478
00:26:42.254 --> 00:26:44.514
That around the store, and he never did find it.
479
00:26:45.034 --> 00:26:49.964
And so that's those are the challenges that this really starts to address.
480
00:26:50.214 --> 00:26:54.354
But at the end of the day, it's lowering costs and it's, it's better information.
481
00:26:54.404 --> 00:26:59.204
And then more and more importantly particularly in the branded merchandise,
482
00:26:59.204 --> 00:27:02.644
is that shelf intelligence to be able to sell that data back to the brands.
483
00:27:03.114 --> 00:27:07.504
Because that's one of the things that that, you know, your big brands that are
484
00:27:07.504 --> 00:27:10.674
selling stuff through retailers, they generally do not have that intelligence
485
00:27:10.744 --> 00:27:13.254
at the shelf level, at the store level.
486
00:27:13.684 --> 00:27:17.094
And understanding, "Hey, our sales are down." Well, are your sales down because
487
00:27:17.094 --> 00:27:18.964
the-- it's not being presented properly?
488
00:27:18.964 --> 00:27:21.424
Is the sales down because you don't have it?
489
00:27:21.914 --> 00:27:23.274
What, what are the sales down?
490
00:27:23.864 --> 00:27:27.654
And this gives them that ability to se-sell that data back to those brands.
491
00:27:29.355 --> 00:27:34.970
I'm becoming a, a bigger and bigger fan and frustrated,
492
00:27:34.980 --> 00:27:37.704
for buy online and pick up in store
493
00:27:38.454 --> 00:27:39.034
Mm-hmm.
494
00:27:39.054 --> 00:27:44.143
pick out online and have a basket ready for me when I
495
00:27:44.143 --> 00:27:47.223
come in- and come into the store and schedule that because I'm getting
496
00:27:47.413 --> 00:27:48.943
very frustrated shopping online.
497
00:27:49.573 --> 00:27:55.403
It, it's great to be able to make one trip a week into the city to go and make a
498
00:27:55.403 --> 00:28:01.123
return of like all this random stuff that I was like so did not like from Amazon.
499
00:28:01.173 --> 00:28:05.003
But I'm being disappointed and disappointed more by low-quality
500
00:28:05.003 --> 00:28:09.423
products that maybe were a little bit overpriced, right?
501
00:28:09.493 --> 00:28:13.783
Or I thought it was a really good deal and it was like too good to be true a deal.
502
00:28:14.313 --> 00:28:19.713
And now I'm finding that like I just want to go ahead and set up that day
503
00:28:19.713 --> 00:28:26.743
when I'm going into the city and just hit four stores to run those errands,
504
00:28:27.163 --> 00:28:29.243
but I want my basket waiting for me.
505
00:28:29.543 --> 00:28:30.743
These are the things I want.
506
00:28:31.173 --> 00:28:32.573
I don't care where you get them.
507
00:28:32.753 --> 00:28:37.073
Just get them there by like Thursday, and I'll be in there like day
508
00:28:37.073 --> 00:28:39.913
after tomorrow, and then I'll go through my basket and decide like
509
00:28:39.943 --> 00:28:41.373
what I want and what I don't want.
510
00:28:42.273 --> 00:28:46.783
And I feel like that would just be such a service to the in-store associates
511
00:28:47.473 --> 00:28:52.303
because otherwise I'm just like asking them to hang out with me for 20 minutes
512
00:28:52.303 --> 00:28:56.883
to go look and find things I have like the web browser up because I
513
00:28:56.883 --> 00:28:57.953
don't know what this stuff is called.
514
00:28:57.953 --> 00:29:01.123
I'm like I thought this was cute, and this was cute, and this was cute." And
515
00:29:01.123 --> 00:29:02.093
they're like, "Oh, I don't have this.
516
00:29:02.133 --> 00:29:05.919
Oh, I don't have this." I'm like, why can't we just have that visibility, right?
517
00:29:06.199 --> 00:29:10.439
And I guess it all kind of come back, comes back down into this.
518
00:29:11.199 --> 00:29:12.039
Yeah.
519
00:29:12.524 --> 00:29:17.634
This problem so that we can solve a completely different problem.
520
00:29:18.569 --> 00:29:23.119
The issue that you're facing or explaining there is why do
521
00:29:23.119 --> 00:29:25.889
airlines over, oversell their seats?
522
00:29:26.484 --> 00:29:27.544
I have no clue.
523
00:29:27.844 --> 00:29:28.484
Why?
524
00:29:28.589 --> 00:29:31.219
they do it because people don't show up or they, the people
525
00:29:31.234 --> 00:29:32.974
Who doesn't show up for a flight?
526
00:29:32.974 --> 00:29:33.924
I never have i.
527
00:29:34.959 --> 00:29:35.469
the time.
528
00:29:35.999 --> 00:29:36.709
All the time.
529
00:29:37.179 --> 00:29:39.989
And it's more that somebody didn't make the connection, so they're not
530
00:29:39.989 --> 00:29:42.529
on the flight, so they o-oversell it.
531
00:29:42.529 --> 00:29:43.839
And that, that is the issue.
532
00:29:44.159 --> 00:29:48.060
The problem with to do what you do... Now, so the typical female shopper
533
00:29:48.949 --> 00:29:54.929
right now buying clothes online will buy three and return two because
534
00:29:54.929 --> 00:29:59.539
the sign the sizing issues and aspirational sizes and one, right?
535
00:29:59.539 --> 00:30:01.339
And you don't, you don't know what it is.
536
00:30:01.830 --> 00:30:06.369
And, and now some of the, the retailers are pushing back on that.
537
00:30:06.369 --> 00:30:11.919
If you wanted to do that at the store level, now they've actually held inventory
538
00:30:11.919 --> 00:30:15.149
that they could sell for you to show up.
539
00:30:15.279 --> 00:30:19.049
And if you don't show up, they've just taken inventory
540
00:30:19.049 --> 00:30:21.760
out of sales process for that.
541
00:30:21.809 --> 00:30:25.119
So it gets very, very difficult, particularly for clothing, when you only
542
00:30:25.159 --> 00:30:31.199
have so many of a particular size or particular color to be able to do that.
543
00:30:31.249 --> 00:30:36.449
There are these true fit technologies now that are in play that have greatly
544
00:30:36.449 --> 00:30:38.919
reduced returns as a result of that.
545
00:30:38.959 --> 00:30:41.919
But we're still sitting at about ninety percent returns for
546
00:30:41.919 --> 00:30:43.739
e-commerce on women's dresses.
547
00:30:43.769 --> 00:30:45.139
It's incredible
548
00:30:45.194 --> 00:30:45.314
We
549
00:30:45.379 --> 00:30:45.939
how inefficient
550
00:30:46.044 --> 00:30:50.434
we have a vanity sizing issue in the United States, and, I,
551
00:30:50.784 --> 00:30:57.024
I have no business being a size extra small at Free People when I have other
552
00:30:57.074 --> 00:31:03.164
things from two years ago, same shirt, that's a medium, and now I'm extra small.
553
00:31:03.294 --> 00:31:05.294
It's not my fault, and I shouldn't be punished.
554
00:31:05.774 --> 00:31:06.414
That's their fault.
555
00:31:07.424 --> 00:31:07.794
Yeah.
556
00:31:08.264 --> 00:31:10.744
That, that's the whole, that's the whole thing
557
00:31:10.744 --> 00:31:12.684
in, in fashion in particular.
558
00:31:13.254 --> 00:31:15.834
We have done that to the industry.
559
00:31:15.864 --> 00:31:18.284
It's just the same way we didn't-- you know, you could look at it in
560
00:31:18.284 --> 00:31:20.194
terms of offshoring our manufacturing.
561
00:31:21.014 --> 00:31:22.064
We've done this ourselves.
562
00:31:22.064 --> 00:31:25.124
We created this problem that we're dealing with today.
563
00:31:25.654 --> 00:31:30.124
And with the rise of e-commerce, this returns issue, we caused the
564
00:31:30.124 --> 00:31:35.604
problem by aspirational sizing, that there's no consistency whatsoever.
565
00:31:36.024 --> 00:31:39.394
And that's what-- that's why I think there's some wisdom in the, the guys
566
00:31:39.454 --> 00:31:42.874
like Steve Jobs that just, would always wear the black turtleneck and blue jeans.
567
00:31:42.874 --> 00:31:43.574
That's all they had.
568
00:31:44.364 --> 00:31:45.244
That's all they got because
569
00:31:45.264 --> 00:31:46.204
Men's sizing
570
00:31:46.384 --> 00:31:46.674
Yeah.
571
00:31:47.154 --> 00:31:47.754
Solves so many
572
00:31:47.834 --> 00:31:48.744
they never had to worry.
573
00:31:48.744 --> 00:31:49.384
They just get it.
574
00:31:49.434 --> 00:31:51.154
This is what I'm wearing every single day.
575
00:31:51.154 --> 00:31:53.024
I'm not using any brainpower on that
576
00:31:53.159 --> 00:31:55.659
So you guys didn't have to worry that much in the first place.
577
00:31:55.659 --> 00:32:00.289
A 34 is a 34, a 32 is a 32, a 52 is a 52, or a 42 is a 42.
578
00:32:00.319 --> 00:32:04.049
You guys' sizing has been so consistent over 100 years.
579
00:32:06.764 --> 00:32:06.854
Yeah.
580
00:32:07.409 --> 00:32:12.029
I know an 88-year-old whose suits are still the exact same size.
581
00:32:13.134 --> 00:32:13.544
Yeah.
582
00:32:13.684 --> 00:32:13.804
Yeah.
583
00:32:14.184 --> 00:32:14.554
Exactly.
584
00:32:15.354 --> 00:32:16.074
Exactly.
585
00:32:16.394 --> 00:32:18.604
But I mean, that's the big issue with fashion.
586
00:32:18.634 --> 00:32:22.684
The the issue with other items could... It's just a, a plain velocity thing.
587
00:32:23.044 --> 00:32:27.394
The stat that blew my mind and I was at a grocery event when I heard this.
588
00:32:27.394 --> 00:32:31.554
It says eighty percent of the SKUs in a grocery store sell less than one a week.
589
00:32:35.329 --> 00:32:36.089
That's
590
00:32:36.204 --> 00:32:38.434
you figure... Yeah.
591
00:32:38.564 --> 00:32:40.924
And it was mind-blowing to learn that.
592
00:32:41.574 --> 00:32:46.074
And so when you go to shelf intelligence you literally go on, "Okay, which of these
593
00:32:46.074 --> 00:32:50.854
things do we need to keep on the shelves? Which of these things you know, you
594
00:32:50.854 --> 00:32:54.434
need to have this on the shelf. We don't sell it very often, but when we do, that
595
00:32:54.484 --> 00:32:58.504
person's spending three hundred dollars because they're, they're buying this."
596
00:32:58.584 --> 00:33:00.724
You've got to work through all of that data.
597
00:33:01.294 --> 00:33:04.984
But, 'cause at the same time, you've got this incredible increase with
598
00:33:05.064 --> 00:33:08.074
buy on store, pick up in store, click and collect for groceries.
599
00:33:08.604 --> 00:33:09.534
You need space for that.
600
00:33:09.999 --> 00:33:10.419
Yeah.
601
00:33:10.494 --> 00:33:14.079
You can create that space by taking some items off the shelves.
602
00:33:14.219 --> 00:33:17.499
It, you know, for people that travel internationally, when you see a
603
00:33:17.499 --> 00:33:21.959
US supermarket, you're just like dumbfounded by the number of brands
604
00:33:22.379 --> 00:33:26.199
and the number of choices per category that are literally on the shelves.
605
00:33:26.529 --> 00:33:28.259
It's there's so much there.
606
00:33:28.259 --> 00:33:32.339
So you, you've got this remodeling and these store format changes that
607
00:33:32.339 --> 00:33:35.209
are occurring rapidly, and these technologies we're talking about shelf,
608
00:33:35.479 --> 00:33:40.569
Shelf intelligence is enabling some of those changes to occur because that
609
00:33:40.569 --> 00:33:45.019
intelligence is there on, on which items you really need to have in stock,
610
00:33:45.299 --> 00:33:49.149
which items can you still s- still have, but they're not on display.
611
00:33:49.259 --> 00:33:53.229
They're in back and they're in a, in a format that if somebody asks for it, you
612
00:33:53.229 --> 00:33:57.439
could pull it from the back or it could be part of an e-commerce order there.
613
00:33:57.589 --> 00:34:01.959
But I think gone are the days where I think it used to be the joke when I was at
614
00:34:01.959 --> 00:34:08.569
NCR that, that Meijer Stores had to have in stock what Mrs. Wexner had on her, Les
615
00:34:08.679 --> 00:34:11.169
Wexner's mom, had on her shopping list.
616
00:34:11.579 --> 00:34:16.399
Whatever she bought, they could never run out of, uh, every item
617
00:34:16.399 --> 00:34:17.419
that was on that shopping list.
618
00:34:17.989 --> 00:34:20.089
take-- I mean, the neighborhood should just kind of
619
00:34:20.089 --> 00:34:22.059
take all of our grocery lists, right?
620
00:34:22.809 --> 00:34:26.899
Like, when I want to make cookies, I want you to have flour, but I do not keep flour
621
00:34:26.899 --> 00:34:29.249
in my house because I barely ever use it.
622
00:34:29.389 --> 00:34:31.799
I just buy it, make the cookies, and throw the rest away.
623
00:34:31.869 --> 00:34:32.369
Sorry.
624
00:34:34.329 --> 00:34:36.559
But that's, you know, a big part of-- to, for me that I find
625
00:34:36.789 --> 00:34:40.269
al-always an interesting thing that I would think shelf intelligence can
626
00:34:40.719 --> 00:34:43.479
create an edge here is that with grocery, because of what you just said, right?
627
00:34:43.479 --> 00:34:46.909
If eighty percent of the items don't sell more than one a week, I mean, the first
628
00:34:46.909 --> 00:34:51.199
thought that occurs to me is there's a lot of specialty items in most supermarkets,
629
00:34:51.829 --> 00:34:53.249
which are things on the shelf that...
630
00:34:53.419 --> 00:34:56.759
A-and I buy a lot of these specialty things, but when I do, they're
631
00:34:56.759 --> 00:34:59.869
usually the kind of thing that whether it's a liquid in a jar or something
632
00:34:59.869 --> 00:35:03.469
that comes in a, like a tub or a container, it lasts a long time.
633
00:35:04.119 --> 00:35:08.239
It's-- Most of these are things that I-- you'd never need to buy on a weekly basis.
634
00:35:08.239 --> 00:35:09.459
You maybe are buying one a month,
635
00:35:10.449 --> 00:35:10.709
Yeah.
636
00:35:10.719 --> 00:35:11.859
or maybe even less frequently.
637
00:35:11.859 --> 00:35:15.309
So, but you're right that when I do need it, I expect that there's gonna
638
00:35:15.309 --> 00:35:17.039
be one there on the shelf, Right.
639
00:35:17.359 --> 00:35:17.639
But
640
00:35:17.789 --> 00:35:19.069
Like vanilla extract.
641
00:35:19.759 --> 00:35:23.509
Vanilla extract, you know, you, you expect them to have it.
642
00:35:24.019 --> 00:35:25.539
They may not have to have ten of
643
00:35:25.929 --> 00:35:26.709
should be in stock.
644
00:35:27.929 --> 00:35:28.069
Yeah.
645
00:35:28.759 --> 00:35:29.109
Yeah.
646
00:35:29.529 --> 00:35:29.749
Well,
647
00:35:30.439 --> 00:35:33.569
They may not need ten different varieties of the vanilla extract,
648
00:35:33.909 --> 00:35:34.729
Right.
649
00:35:34.869 --> 00:35:39.039
On the front-facing piece of that, if it frees up, "Hey, I've got
650
00:35:39.039 --> 00:35:43.569
a shelf now where I can put the whole orders in the back there," or I can do
651
00:35:43.569 --> 00:35:48.829
these micro-fulfillment centers, which are robotic fulfillment centers in the back
652
00:35:48.939 --> 00:35:51.019
that can manage can manage those orders.
653
00:35:51.079 --> 00:35:52.819
And we're seeing that more and more often.
654
00:35:52.839 --> 00:35:53.359
Yeah, I'm
655
00:35:53.559 --> 00:35:53.749
really
656
00:35:53.809 --> 00:35:57.179
with the rise of drone delivery too, you gotta
657
00:35:57.179 --> 00:35:58.939
have a place to stage those things
658
00:35:59.039 --> 00:35:59.339
And stuff.
659
00:35:59.769 --> 00:36:04.359
I mean, even Sam's came out with a announcement this week saying they
660
00:36:04.359 --> 00:36:06.149
have delivery in less than an hour.
661
00:36:07.339 --> 00:36:07.649
Right.
662
00:36:08.009 --> 00:36:09.189
For Sam-- I mean, what are you bringing?
663
00:36:09.189 --> 00:36:11.409
You br-bring a whole pickup truck delivered to your
664
00:36:11.409 --> 00:36:12.649
house in less than an hour
665
00:36:13.709 --> 00:36:14.749
Yeah, that's impressive.
666
00:36:15.539 --> 00:36:17.159
But there are a lot of these things where I always
667
00:36:17.409 --> 00:36:17.669
animal.
668
00:36:18.329 --> 00:36:21.539
Especially with the MFC ID, I mean, all these
669
00:36:21.539 --> 00:36:25.749
items that I buy that infrequently at a grocery store, I know ahead
670
00:36:25.749 --> 00:36:27.039
of time when I'm gonna get them.
671
00:36:27.629 --> 00:36:30.609
So why isn't there an ability with it, whoever my favorite grocery
672
00:36:30.609 --> 00:36:33.899
store is, that I should be able to say, I'm going to go shopping, but
673
00:36:33.899 --> 00:36:36.829
here's my list of 10 things that I know are the ones that are oddballs."
674
00:36:37.939 --> 00:36:40.479
Just have them ready in a pickup area in the store when I get
675
00:36:40.479 --> 00:36:41.474
there if I send it ahead, right?
676
00:36:41.604 --> 00:36:42.284
Just those 10.
677
00:36:42.504 --> 00:36:44.914
I can-- I'll still go pick all the produce and all everything
678
00:36:44.914 --> 00:36:45.984
else, don't worry about that.
679
00:36:46.244 --> 00:36:49.934
But all these shelf items that I buy once in a blue moon, I
680
00:36:49.934 --> 00:36:50.914
need to know that it's there.
681
00:36:51.424 --> 00:36:55.014
So why can't I just send those items ahead and they're staged and ready for me?
682
00:36:55.014 --> 00:36:56.624
Don't even have to be out on a shelf, right?
683
00:36:56.624 --> 00:37:00.004
There can be a robot picking them in a back room somewhere that's
684
00:37:00.004 --> 00:37:03.214
just pre-bagged and ready for me to pick up to take to a checkout, along
685
00:37:03.294 --> 00:37:07.544
Or just making-- Or just pulling like your top like, 500 customers
686
00:37:07.544 --> 00:37:14.854
that shop on the most frequently and putting that as the part of the list.
687
00:37:15.134 --> 00:37:17.964
That these items must stay in stock because our customers
688
00:37:17.964 --> 00:37:18.864
come in getting them.
689
00:37:19.344 --> 00:37:19.644
I--
690
00:37:19.704 --> 00:37:20.464
the most common.
691
00:37:20.724 --> 00:37:22.494
Because they're, kind of the most common.
692
00:37:22.864 --> 00:37:28.618
I would Having a product sell less than, 80% sell less than once a week, like I--
693
00:37:28.618 --> 00:37:31.828
to me, that means like our assortment's too big, our assortment is off.
694
00:37:31.828 --> 00:37:33.818
We are so missing the mark.
695
00:37:35.228 --> 00:37:38.538
That's what it would read to me, not coming from grocery, that the
696
00:37:38.538 --> 00:37:40.318
whole thing needs to be re-assorted.
697
00:37:40.318 --> 00:37:46.888
But understanding those, that data and understanding the what that data says,
698
00:37:46.888 --> 00:37:50.208
I mean, I have to say, like I would have never, I would have never guessed that.
699
00:37:50.938 --> 00:37:51.218
Um,
700
00:37:51.353 --> 00:37:51.923
I think one
701
00:37:52.268 --> 00:37:53.578
my gosh, you're all fired.
702
00:37:54.963 --> 00:37:55.803
Yeah, I'm st- I
703
00:37:55.908 --> 00:37:56.388
failing.
704
00:37:56.923 --> 00:38:00.283
Another technology that plays in here is computerated ordering.
705
00:38:00.923 --> 00:38:05.663
And when you're able to do that and able to enforce that across things
706
00:38:05.663 --> 00:38:10.723
like vendor-managed inventory, that is a big issue when it comes to grocery.
707
00:38:10.723 --> 00:38:14.893
So for instance, your Coke and Pepsi, they're the ones that are handling the
708
00:38:14.893 --> 00:38:17.403
inventory when it comes to those products.
709
00:38:17.403 --> 00:38:21.053
Mondelez handling their stuff, you know, ice cream vendors
710
00:38:21.553 --> 00:38:22.733
that are handling their stuff.
711
00:38:22.853 --> 00:38:26.243
It's traditionally been keep the shelves full regardless of whether or
712
00:38:26.243 --> 00:38:28.243
not you have what's in stock, in stock.
713
00:38:28.303 --> 00:38:33.433
You just move, hey, if it's ice cream and we're out of vanilla, and
714
00:38:33.433 --> 00:38:37.633
I'm supposed to have five lanes of vanilla and instead I fill it in with
715
00:38:37.633 --> 00:38:40.603
strawberry and make it look full.
716
00:38:41.258 --> 00:38:44.618
But I don't have enough vanilla, all of a sudden I start losing sales.
717
00:38:44.658 --> 00:38:47.988
And because the vendor's managing that and the retailer doesn't know,
718
00:38:47.988 --> 00:38:51.688
they're losing sales as a result of it.
719
00:38:51.688 --> 00:38:54.928
And by forcing the computer-aided inventory and the space planning
720
00:38:54.998 --> 00:38:59.228
compliance now to those vendor-managed inventories, they're seeing a twenty
721
00:38:59.228 --> 00:39:04.358
percent increase in profitability in those categories because the right
722
00:39:04.448 --> 00:39:08.828
product is in the right shelf, or if they don't have it, they don't have it,
723
00:39:08.828 --> 00:39:10.348
so they're ordering the right amount.
724
00:39:10.848 --> 00:39:12.548
All of that stuff starts to flow.
725
00:39:12.598 --> 00:39:15.618
And, this is the crazy part, Cas- Casey, if you're coming from the fashion
726
00:39:15.618 --> 00:39:22.928
side, in the grocery side the goal is to sell through their inventory four
727
00:39:22.928 --> 00:39:26.268
times before they gotta pay for it.
728
00:39:26.268 --> 00:39:26.578
Okay?
729
00:39:26.578 --> 00:39:31.538
So they wanna sell four weeks of inventory of that item before they paid for
730
00:39:31.538 --> 00:39:36.458
the first week, before the first week inventory that they've got to pay for it.
731
00:39:36.458 --> 00:39:37.988
That's how tight the margins are.
732
00:39:38.588 --> 00:39:42.688
And when you have a vendor-managed inventory situation that becomes
733
00:39:42.758 --> 00:39:48.358
a big problem if they're not being held to the compliance of things.
734
00:39:48.568 --> 00:39:50.758
It's all about inventory control, isn't it?
735
00:39:51.388 --> 00:39:51.658
Yeah.
736
00:39:52.048 --> 00:39:52.148
Yeah.
737
00:39:52.788 --> 00:39:56.498
The customer's entire perception is whether or not you have things in stock.
738
00:39:57.123 --> 00:39:57.733
yeah.
739
00:39:57.908 --> 00:40:00.968
And they may not like the ambiance as much, but they always have it.
740
00:40:00.998 --> 00:40:03.878
They may like another store because the people are nicer,
741
00:40:03.878 --> 00:40:06.498
but this one always has what I
742
00:40:06.663 --> 00:40:06.743
Yeah.
743
00:40:06.743 --> 00:40:07.183
Mm-hmm.
744
00:40:07.393 --> 00:40:07.603
Yeah.
745
00:40:07.913 --> 00:40:09.063
It's the most important factor.
746
00:40:09.618 --> 00:40:15.211
the Maslow's hierarchy of needs, that's number one when it comes to
747
00:40:15.571 --> 00:40:16.181
It's so funny.
748
00:40:16.181 --> 00:40:20.521
We have this butter that I don't know, everybody I know, like we all eat
749
00:40:20.561 --> 00:40:25.891
the same butter, and i- it's only at Whole Food... It's at Whole Foods.
750
00:40:26.711 --> 00:40:31.831
It's-- I, I feel like somebody just never marked it up, or it's like
751
00:40:31.831 --> 00:40:34.401
been priced wrong for 10 years.
752
00:40:35.131 --> 00:40:40.481
But it's limit four per customer, and so we all know the delivery
753
00:40:40.481 --> 00:40:45.341
schedule at which Whole Foods on when it's restocked, so we can all
754
00:40:45.341 --> 00:40:49.161
go get our four cubes of like butter.
755
00:40:49.696 --> 00:40:50.246
It's
756
00:40:50.291 --> 00:40:52.271
Are you calling each other going, "Hey, are you going
757
00:40:52.591 --> 00:40:53.731
calling each other.
758
00:40:53.821 --> 00:40:55.961
get your... Hey, hey, can you pick me up a couple extra?"
759
00:40:57.151 --> 00:41:00.291
I mean, one of my friends, every time she comes over, she brings me
760
00:41:00.291 --> 00:41:05.071
four bricks of butter because she lives right next to O- one of the Whole Foods.
761
00:41:05.231 --> 00:41:05.811
Wow.
762
00:41:06.036 --> 00:41:11.116
Like super random, but like customers, like sometimes
763
00:41:11.116 --> 00:41:12.526
like we know where inventory is.
764
00:41:12.526 --> 00:41:13.646
We know the day that it gets filled.
765
00:41:13.646 --> 00:41:14.706
I'm only allowed to have four.
766
00:41:14.706 --> 00:41:19.826
Like we've got a little call tree going "Oh, there's some butter over here.
767
00:41:19.826 --> 00:41:22.446
Do you want me to pick you up like four bricks or two?
768
00:41:22.476 --> 00:41:26.763
I'll share." "I don't need butter right now, but it's here, so
769
00:41:26.763 --> 00:41:28.613
does anybody else need butter?"
770
00:41:30.108 --> 00:41:32.928
I'm gonna have to get with you offline on this butter here, you know,
771
00:41:33.228 --> 00:41:33.828
missing out.
772
00:41:34.158 --> 00:41:35.718
I'm not in this this club.
773
00:41:35.818 --> 00:41:37.278
Yep you're not in the inner butter circle.
774
00:41:41.313 --> 00:41:43.123
like, come on, just like text message us.
775
00:41:43.178 --> 00:41:44.088
I need more butter.
776
00:41:46.678 --> 00:41:49.758
So, so Greg with this, everything we've talked about here on
777
00:41:49.758 --> 00:41:53.298
shelf intelligence, what's the single most important decision you would say
778
00:41:53.298 --> 00:41:58.148
to a retail CIO that they need to make this year to go from, let's say pilot
779
00:41:58.148 --> 00:41:59.738
to scale with shelf intelligence?
780
00:42:00.051 --> 00:42:02.461
Yeah, I think you've got to determine, your format.
781
00:42:02.461 --> 00:42:06.681
It's-- I, I can't say one technology in and of itself is your solution.
782
00:42:06.791 --> 00:42:11.071
The, the one that crosses everything is your network there,
783
00:42:11.131 --> 00:42:12.911
and then edge would be next.
784
00:42:13.131 --> 00:42:16.681
And then you can decide whether it's cameras, whether it's RFID,
785
00:42:16.681 --> 00:42:19.641
whether it's shelf labels for those.
786
00:42:19.641 --> 00:42:23.981
The order in which I would do them for most is likely shelf labels first and
787
00:42:23.981 --> 00:42:26.021
then RFID second, computer vision third.
788
00:42:27.091 --> 00:42:30.871
And that's just simply because of the returns that you get from that.
789
00:42:30.991 --> 00:42:34.361
The, Whereas the shelf labels, the fact that you, you now know
790
00:42:34.361 --> 00:42:37.471
what your upcoming costs are gonna be, it's sort of like gas prices.
791
00:42:37.471 --> 00:42:41.981
You can change those prices very quickly because your input costs are there
792
00:42:41.981 --> 00:42:44.141
because you got tariffs or, or whatever.
793
00:42:44.581 --> 00:42:47.041
That's too compelling to not change.
794
00:42:47.091 --> 00:42:50.241
That's a drop right to the bottom line in terms of that pricing.
795
00:42:50.811 --> 00:42:52.460
And then I would say RFID.
796
00:42:52.510 --> 00:42:57.372
Because the, uh, RFID is more consistent across more categories that
797
00:42:57.372 --> 00:42:59.132
you have than the computer vision.
798
00:42:59.132 --> 00:43:04.732
So where RFID can see things that are stacked on top of each other
799
00:43:05.262 --> 00:43:08.912
there, it can be used as a checkout technology, it can be used to find
800
00:43:08.972 --> 00:43:10.472
where in a store something is.
801
00:43:10.882 --> 00:43:15.132
Computer vision can do great when it's looking at theft or when it's
802
00:43:15.132 --> 00:43:19.892
looking at the front of, of shelves to see what's in stock or not.
803
00:43:20.182 --> 00:43:23.802
It doesn't do well for the things that are behind the things that
804
00:43:23.802 --> 00:43:25.422
are f-front-facing on the shelf.
805
00:43:25.752 --> 00:43:29.722
So you, you don't get as accurate of an inventory when you do it that way.
806
00:43:30.062 --> 00:43:34.272
The real solution is really a combination of the two for most for most places.
807
00:43:34.332 --> 00:43:38.527
And some are using weight-weighted shelves and others to do things, but...
808
00:43:38.637 --> 00:43:42.167
But that would be the order of the-- it would be the network, the
809
00:43:42.167 --> 00:43:46.827
processing, and then I would say for most it's gonna be the shelf labels,
810
00:43:46.827 --> 00:43:50.067
RFID, and then computer vision last.
811
00:43:50.177 --> 00:43:54.347
Unless you're in a really high-theft environment where the facial recognition
812
00:43:54.347 --> 00:43:56.067
type of piece is so critical.
813
00:43:56.737 --> 00:44:00.987
I mean, we know, we know in the, the height of the theft that was
814
00:44:00.987 --> 00:44:04.997
happening in San Francisco, I know of a drugstore that was losing ten percent
815
00:44:05.077 --> 00:44:09.077
of same-store sales after spending forty times their national average on
816
00:44:09.147 --> 00:44:11.867
physical guards because of the loss.
817
00:44:11.867 --> 00:44:17.637
In that case, in that case theft is an existential threat to overall survival
818
00:44:17.937 --> 00:44:19.227
for the company, and they pulled out.
819
00:44:19.457 --> 00:44:20.487
They just pulled out of the region.
820
00:44:20.517 --> 00:44:22.087
They said, "Can't afford to have stores here."
821
00:44:22.687 --> 00:44:26.667
And so that would be an extenuating circumstance where they would, they
822
00:44:26.667 --> 00:44:29.437
would put that over another technology.
823
00:44:29.467 --> 00:44:33.417
But generally, if you're talking about a grocery store type of environment, you're
824
00:44:33.447 --> 00:44:38.337
usually looking at shelf labels first because that has a direct labor component
825
00:44:38.617 --> 00:44:41.947
savings or a labor reutilization area.
826
00:44:42.177 --> 00:44:47.147
Not-- Your labor instead of doing changing prices, which you think about it, how much
827
00:44:47.147 --> 00:44:51.757
value is there in changing the price, the price labels somebody physically doing
828
00:44:51.757 --> 00:44:57.437
that compared to doing a co-- a, a profit center like they're making more prepared
829
00:44:57.857 --> 00:45:00.487
foods and using that labor in that way.
830
00:45:00.487 --> 00:45:04.177
Or just making sure the stuff is on the shelf in the first place.
831
00:45:04.547 --> 00:45:07.817
That is a far more valuable use of human labor than it is
832
00:45:07.817 --> 00:45:10.097
changing the price tags per se.
833
00:45:10.427 --> 00:45:15.187
It also automatically, because the price is coming from the same database that's
834
00:45:15.187 --> 00:45:19.197
ringing up at the front, the price that's on the shelf matches the price
835
00:45:19.197 --> 00:45:20.897
that's gonna ring up on the scanner.
836
00:45:21.177 --> 00:45:23.837
Whereas when you're physically putting shelf tags on there,
837
00:45:23.867 --> 00:45:26.017
you're never quite sure type thing.
838
00:45:26.017 --> 00:45:27.787
So there's a lot of losses
839
00:45:28.157 --> 00:45:31.487
that occur because the prices were wrong or we didn't get around to putting
840
00:45:31.587 --> 00:45:34.297
the shelf tags on that sort of thing.
841
00:45:34.367 --> 00:45:35.847
So that's, that's a big one.
842
00:45:35.877 --> 00:45:41.337
But RFID, because of just the rise in these multi-channel sales that
843
00:45:41.337 --> 00:45:45.287
are occurring and just knowing you have the product at the store level.
844
00:45:45.887 --> 00:45:49.757
So we're seeing overall eighty-nine percent of all orders
845
00:45:49.947 --> 00:45:51.387
are fulfilled from a store.
846
00:45:52.107 --> 00:45:55.887
And so, uh, that RFID is, is really helpful, and then it works all
847
00:45:55.887 --> 00:45:56.977
the way through the supply chain.
848
00:45:57.047 --> 00:46:00.897
You can use it for, tracking, traceability, all those sort of things.
849
00:46:01.617 --> 00:46:04.207
Okay I think the-- my most interesting bit, there
850
00:46:04.207 --> 00:46:05.537
are lots of great points there.
851
00:46:05.887 --> 00:46:09.227
I think it's most interesting that, you put upgrading the network first, that you
852
00:46:09.227 --> 00:46:11.027
can actually handle all of these things.
853
00:46:11.397 --> 00:46:14.037
Now, because it's been a while since we've seen so much data-intensive
854
00:46:14.037 --> 00:46:16.777
technology going into the store that needs to be supported, so
855
00:46:16.777 --> 00:46:17.897
that's an excellent point to watch
856
00:46:18.032 --> 00:46:23.162
we're seeing it in AI tools right now where, vendors are so compute
857
00:46:23.162 --> 00:46:24.872
limited that they're slowing down.
858
00:46:25.152 --> 00:46:29.402
Just can't use their models now, even though everybody wants to use them.
859
00:46:29.562 --> 00:46:33.072
It's the same same thing happening with the, the network at the stores.
860
00:46:33.482 --> 00:46:36.782
You start putting all of these different touch points in there, and
861
00:46:36.842 --> 00:46:39.182
the network simply can't handle it.
862
00:46:39.182 --> 00:46:43.192
And that all affects customer experience, number one, and then associate what
863
00:46:43.192 --> 00:46:46.612
associates think of working there and the tools that you can give them, so.
864
00:46:47.317 --> 00:46:48.137
Yep, and that's so true.
865
00:46:48.697 --> 00:46:50.897
Greg, thank you for joining us today and kicking off the
866
00:46:50.897 --> 00:46:54.147
series with this incredible topic around shelf intelligence.
867
00:46:54.147 --> 00:46:56.957
I think we have a few hints in there on what's to come in the
868
00:46:56.957 --> 00:46:58.437
next two parts of the series.
869
00:46:58.437 --> 00:47:02.347
I think we've got some things coming up on store associate tools and
870
00:47:02.347 --> 00:47:05.177
supply chain and returns, and we kind of touched on all of those.
871
00:47:05.497 --> 00:47:06.837
So looking forward to those coming up.
872
00:47:07.752 --> 00:47:07.942
Yep.
873
00:47:08.052 --> 00:47:08.542
Thank you.
874
00:47:09.146 --> 00:47:13.086
Greg, as we wrap up part one, if anyone would like to follow up
875
00:47:13.086 --> 00:47:18.596
with you to dig deeper with IHL's help, what's the best way for them to reach out?
876
00:47:20.266 --> 00:47:24.046
So e- the easiest way is on our website, ihlservices.com, and
877
00:47:24.106 --> 00:47:28.766
just say, "Contact us," or you can find me at Greg Buzek on LinkedIn
878
00:47:29.406 --> 00:47:31.546
at Greg Buzek and find me there.
879
00:47:31.996 --> 00:47:32.236
Great.
880
00:47:32.236 --> 00:47:34.696
And you guys, if you're not already following Greg, you need
881
00:47:34.696 --> 00:47:35.976
to be following Greg on LinkedIn.
882
00:47:36.596 --> 00:47:37.036
Thank you.
883
00:47:37.996 --> 00:47:39.346
again, thanks Greg.
884
00:47:39.346 --> 00:47:43.066
We'll be looking forward to part two on in-store associate tools.
885
00:47:43.386 --> 00:47:45.726
So Ricardo, I'd say this episode is a wrap.
886
00:47:53.290 --> 00:47:54.430
Loved this episode?
887
00:47:54.430 --> 00:47:59.000
Drop us a five-star rating and review on Apple Podcasts, Spotify, or Goodpods.
888
00:47:59.400 --> 00:48:02.910
And if you're watching on YouTube, like and subscribe before you go.
889
00:48:03.150 --> 00:48:04.140
I'm Casey Golden.
890
00:48:04.397 --> 00:48:07.567
Follow us on LinkedIn, Bluesky, Threads, and Instagram.
891
00:48:07.857 --> 00:48:10.927
And subscribe to our Substack for highlights and bonus content.
892
00:48:11.357 --> 00:48:14.307
For transcripts and guest info, visit retailrazor.com.
893
00:48:14.897 --> 00:48:15.807
I'm Ricardo Belmar.
894
00:48:16.161 --> 00:48:19.751
Thanks for joining us on the Retail Razor Data Blades, part
895
00:48:19.751 --> 00:48:21.771
of the Retail Razor Podcast Network.
896
00:48:22.277 --> 00:48:25.757
Until next time, stay sharp, be data-driven, and harness AI.
897
00:48:26.197 --> 00:48:28.357
This is the Retail Razor Data Blades.