What No One’s Telling You About Digital Shelf | Pulse of the Retail Industry at GroceryShop

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Season 3!

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Welcome to the ecommert Podcast, the

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definitive space for CPG Executives.

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Hosted by Mert Damlapinar, a top LinkedIn

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voice, we bring insights from top CPG and

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MarTech leaders in eCommerce, retail

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media, AI, and digital shelf strategies.

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If you find our content valuable, please

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leave a 5-star review.

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It's completely free for you, but very

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valuable for us.

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In return, we promise to bring many more

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distinguished guests in the future, like this

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medley of leaders interviewed on Grocery

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Shop 2025, an event that has united the

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grocery and CPG ecosystem like no other

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show, evolving into the premier global

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platform for executives shaping the future

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of retail.

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Powered by the experts behind ShopTalk,

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Grocery Shop attracts thousands of

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attendees from across the world.

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Here we are on the floor of the grocery

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shop with magnificent Ben Miller, who is

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behind this great organization.

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Ben, thank you very much for having us.

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I'm so pleased you're here.

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It's a lovely event.

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Everybody's excited.

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I have a tough question for you because

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I'm asking a lot of vendors similar

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questions.

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Are you ready?

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Okay, let's do it.

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Okay.

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If you had to pick one friction today,

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especially in the retailer domain, between

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on-the-channel and in-store execution,

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which one would you pick to tackle first

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with all the tech we have available to us?

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Oh, wow.

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Look.

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Great question.

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It's a fascinating dichotomy because

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grocery, 90% still in-store, some markets,

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85%, but the growth, a lot of it's coming

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through online.

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So online continues to be, in many

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markets, the fastest growing channel.

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So if you're a retailer, you've got this

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challenge of, I've got to get the core

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working, but if I'm not doing the online

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right, I'm not getting the growth.

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Missing the opportunity.

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Exactly.

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There is no way that you will do that

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opportunity profitably and effectively if

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your in-store operations aren't running

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well.

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So if I was to pick one, I'd pick the in-store,

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invest in smart store technology,

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understand your availability, use robotics,

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use AI, use shelf-edge sensors, get that

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in-store working really well because that

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gives you the efficient base to build a

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profitable online business from.

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That makes sense.

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I'm a 3P player, up-and-coming digital

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native.

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I'm not the global enterprise brand.

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I don't have $10 billion sales with 30 brand

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portfolio.

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Then what would you suggest me to do on

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my retailer expansion?

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So right now, if you're CPG, the big

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challenge, you know this, is volume

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growth.

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Exactly.

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You've got to find routes to volume growth.

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We believe that the answer to that is a

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combination of physical availability to get

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your product, the classic CPG, point of

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distribution, yeah, the stuff you grew up on

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as a sales director.

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Physical distribution, but mental

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availability.

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Share of voice, capture the mental of your

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consumer, yeah.

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And that is all digital.

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So as a CPG, you've got to have a brilliant

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digital game to be able to do all the

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demand creation, and then you've got to

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make sure your physical supply chain, and

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when I say physical, if you're delivering it

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through somebody's house or into a store

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where it's Don't worry about the point of

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purchase, you've still got to physically get

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products to be Products should be

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available on the shelf, but there are a lot of

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vendors I see which give you data

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advantage, which gives you capture the

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digital domain with reasonable enough

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spending without breaking the bank.

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And that's really important, because if you

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want to do digital demand creation really

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well, you need the first party data.

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Correct.

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And a lot of that is about building first

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party data that you can activate against,

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and that's the muscle that we see CPGs

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really needing to build.

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That's okay, guys.

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You heard from the master, the

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mastermind behind all this event we have

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going on, Ben.

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Thank you very much for all the tips.

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Absolute pleasure.

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Great insight.

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I'll catch you up later again.

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Thank you.

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Thanks for being with us.

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Hi, Grace.

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Thanks for having us here with the grocery

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shop floor today.

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We're going to talk about one of the crucial

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questions I've been asking all my guests

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today on the floor.

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If you had to pick one friction in the retail

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environment today between omni-channel

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execution and in-store execution, which

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one would Micmac tackle first, and what

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worked really great in the past?

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Yes.

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Thank you for having me, first of all, Mert.

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So Micmac, we're really focused on

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eliminating friction in the omni-channel

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consumer journey.

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So brands can really struggle with needing

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to control consumers or send them to one

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specific retailer, like a Walmart, because

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they have a JVP with Walmart, where

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Micmac, what we believe is putting a

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consumer in the driver's seat and allowing

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them to tell you as the brand where they

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want to shop.

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So Micmac enables a multi-retailer

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experience.

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So we're channel and platform agnostic.

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We can drive all your traffic to a Micmac

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landing page, surface, a real-time

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inventory to ensure that your consumers

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can tell you where you want to shop, put

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that product in your basket, and then

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check out.

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And then we're going to provide all those

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wonderful back-end insights, reporting,

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and the data.

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and says to allow you to retarget even

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smarter once these shoppers tell you

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where they want to go.

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And some retailers even provide some

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juicy sales data.

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Like which one?

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Like Amazon, like Target, like Walmart,

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and many more.

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So come to the McNutt booth.

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Check out.

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I'll come soon.

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And I got more information on some

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secrets too.

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Thank you very much.

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All right.

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Hi, everyone.

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We're with Eric Mitchell from Seek here at

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the grocery shop floor.

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Eric, thanks for being with us.

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Yeah.

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I'm asking a lot of rapid fire questions to

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the vendors today.

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Now, if you wanted to choose which AI

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capability being undervalued or overstated

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in the current omnichannel ecosystem?

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I think the one that's being most

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overvalued is just using AI as a chatbot.

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Right.

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Right.

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I think the one that is most undervalued is

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where we're specializing, which is using AI

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to analyze multiple data sets occurring

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time to do things like plan media,

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understand assortment, inventory, solve

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inventory issues, things like that.

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So I'd say using AI across sources at the

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same time is probably where it's the most

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undervalued.

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Adam, nice to meet you today.

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Good to meet you too.

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Day three on grocery shop.

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Yes.

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Are you ready for one of my rapid fire

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questions?

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I'm looking forward to it.

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Adam is the co-founder of Ideal.

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And right now we're going to talk about

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how are you using or planning to use retail

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media to drive more incremental ROI

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rather than shifting ad spend?

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That's a great question.

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So my company, Ideal, has built a dynamic

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digital circular platform.

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It's a different user experience.

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It moves away from a traditional PDF

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ecosystem into a dynamic database-driven

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system.

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It has animation, recipe, list building, but it

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also has distribution.

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Distribution that targets the customers of

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the retailer, their competitors, customers,

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and drives traffic back to the store and

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measures it.

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But most importantly to this question, it

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has a retail media network that runs on it.

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We actually have a proprietary network

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that's patented that allows the CPGs to buy

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media targeting regular customers of the

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store, drive traffic to that weekly ad where

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there's rich content, and move it back

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through.

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So to answer your question directly, most

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retailers have been monetizing their

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weekly ad for decades, right?

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Pretty traditional, pretty well-known.

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Exactly.

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But they've been monetizing in a very

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generic way that's based on print, right?

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I'll pay to be in the circular.

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I'll pay a little bit more to be on the cover.

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Clearly in our world, there's the potential

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to have CPGs engage a little bit more, pay

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more for animation, video, recipe, help to

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tell their story, right?

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You launch a new product.

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Is it healthier for me?

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Does it taste better?

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Does it make me feel better?

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Why would I want to buy it?

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And that story can be told with that rich

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media.

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But the other piece of the puzzle that's

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really important is that in our system, the

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CPG can target a specific subset of the

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customer and drive traffic directly to their

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content, rearranging the order of the items

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in the circular so that their content's at the

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top.

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So what this is doing is it's opening up a

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new branch of retail media for retailers,

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right?

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Typically, the circular has been monetized

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to some degree to charge for on-platform

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content in a very limited way.

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And we expand that and allow retailers to

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really grow investment within their weekly

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ad ecosystem.

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And that's what's helping incremental

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growth.

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So it's not shifting dollars from one place

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to another.

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In fact, if anything, most retailers and

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wholesalers are pretty significant

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decrease in trade spend and shopper

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marketing dollar.

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We're helping to level that off and in many

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cases grow it a little bit by just creating an

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ecosystem that's attractive to CPGs.

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And now you unlock the curiosity side of

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my mind because e-commerce followers

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will know that I sometimes throw a couple

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of curveballs.

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00:08:24,600 --> 00:08:25,560
I'm happy to take one.

281
00:08:25,560 --> 00:08:26,880
Where does ideal live?

282
00:08:26,880 --> 00:08:27,940
What domain does it live?

283
00:08:27,940 --> 00:08:29,060
Is it retailer side?

284
00:08:29,060 --> 00:08:30,200
Is it agnostic wallet?

285
00:08:30,200 --> 00:08:31,260
It lives on the retailer side.

286
00:08:31,260 --> 00:08:32,440
So basically, our platform is a

287
00:08:32,440 --> 00:08:34,000
white-labeled weekly ad ecosystem that

288
00:08:34,000 --> 00:08:35,919
lives in the weekly ad page of the retailer's

289
00:08:35,919 --> 00:08:37,580
website in the weekly ad section of their

290
00:08:37,580 --> 00:08:38,440
app.

291
00:08:38,440 --> 00:08:40,280
Anywhere that the customer would find

292
00:08:40,280 --> 00:08:41,700
the weekly ad, that's where we live.

293
00:08:41,700 --> 00:08:43,580
One of the most successful or more

294
00:08:43,580 --> 00:08:45,880
prominent retailers already utilizing it.

295
00:08:45,880 --> 00:08:48,260
So we're going to roll out here in just a

296
00:08:48,260 --> 00:08:49,760
couple of weeks with a couple of large

297
00:08:49,760 --> 00:08:50,500
regional players.

298
00:08:50,500 --> 00:08:52,540
We're not supposed to announce yet.

299
00:08:52,540 --> 00:08:54,580
We work with great retailers like Lunds

300
00:08:54,580 --> 00:08:57,120
and Byerly's, Hein Gelsen's, Northgate

301
00:08:57,120 --> 00:08:58,700
Market.

302
00:08:58,700 --> 00:09:01,020
But we're also launching with some large

303
00:09:01,020 --> 00:09:03,240
national and regional players very soon.

304
00:09:03,240 --> 00:09:05,020
And we work with most of the wholesalers

305
00:09:05,020 --> 00:09:05,700
across the U.S.

306
00:09:05,700 --> 00:09:07,260
as well to help sell through to their

307
00:09:07,260 --> 00:09:08,160
independent community.

308
00:09:08,160 --> 00:09:09,940
How is the reaction from the media teams

309
00:09:09,940 --> 00:09:11,540
or brand teams on the consumer brand

310
00:09:11,540 --> 00:09:12,120
side?

311
00:09:12,120 --> 00:09:14,240
We're a source of investment that they just

312
00:09:14,240 --> 00:09:16,600
typically don't get, and that helps to open

313
00:09:16,600 --> 00:09:18,339
up the doors to new funding.

314
00:09:18,339 --> 00:09:21,060
As a consumer, if I scan any QR code, can I

315
00:09:21,060 --> 00:09:22,980
download it right away or how should I use

316
00:09:22,980 --> 00:09:24,920
You find our content just by going to

317
00:09:24,920 --> 00:09:25,819
retailer websites.

318
00:09:25,819 --> 00:09:26,060
Okay.

319
00:09:26,060 --> 00:09:27,319
I can certainly give you some of them and

320
00:09:27,319 --> 00:09:27,920
drive through.

321
00:09:27,920 --> 00:09:28,319
I'll check it out.

322
00:09:28,319 --> 00:09:29,280
Yeah, absolutely.

323
00:09:29,280 --> 00:09:30,420
It's great to meet you.

324
00:09:30,420 --> 00:09:31,100
Thanks for the time.

325
00:09:31,100 --> 00:09:35,620
Thanks for the answers.

326
00:09:35,620 --> 00:09:37,980
Okay, we're with Gilbert Hernandez from

327
00:09:37,980 --> 00:09:40,460
IBM, who is leading the AI productivity and

328
00:09:40,460 --> 00:09:42,839
working for the productivity success of the

329
00:09:42,839 --> 00:09:44,600
AI solutions at IBM.

330
00:09:44,600 --> 00:09:46,940
Here at Grocery Shop Floor on Vegas, day

331
00:09:46,940 --> 00:09:47,740
three.

332
00:09:47,740 --> 00:09:49,780
Gilbert, one of my rapid fire questions I've

333
00:09:49,780 --> 00:09:52,100
been asking a lot of vendors today.

334
00:09:52,100 --> 00:09:53,460
Where do you see agentic AI

335
00:09:53,460 --> 00:09:55,300
implementation for omnichannel

336
00:09:55,300 --> 00:09:58,340
commerce success across retailers?

337
00:09:58,340 --> 00:10:00,580
Where we've seen the most amount of with

338
00:10:00,580 --> 00:10:02,700
our clients is connecting all the

339
00:10:02,700 --> 00:10:04,420
applications and data sources that they

340
00:10:04,420 --> 00:10:05,360
have.

341
00:10:05,360 --> 00:10:08,360
There seems to be a lot of convolution and

342
00:10:08,360 --> 00:10:12,140
pointed proprietary solutions and the best

343
00:10:12,140 --> 00:10:14,300
way to future-proof any sort of experience

344
00:10:14,300 --> 00:10:17,380
is by having a strategy that includes any

345
00:10:17,380 --> 00:10:19,840
type of data, any type of applications and

346
00:10:19,840 --> 00:10:21,440
all the while trying to pick the most high

347
00:10:21,440 --> 00:10:22,840
value use cases.

348
00:10:22,840 --> 00:10:26,560
So imagine I'm a data engineer working for

349
00:10:26,560 --> 00:10:27,540
a consumer brand.

350
00:10:27,540 --> 00:10:27,860
Yeah.

351
00:10:27,860 --> 00:10:30,120
I'm trying to integrate one of the IBM AI

352
00:10:30,120 --> 00:10:32,580
solutions to my new tech stack.

353
00:10:32,580 --> 00:10:34,320
What does the onboarding journey look

354
00:10:34,320 --> 00:10:38,400
like or how much data literacy do I need as

355
00:10:38,400 --> 00:10:40,100
an end-user to be productive on the

356
00:10:40,100 --> 00:10:40,740
solution?

357
00:10:40,740 --> 00:10:43,020
Yeah, data literacy is obviously a very

358
00:10:43,020 --> 00:10:46,120
important thing and a big part of your data

359
00:10:46,120 --> 00:10:48,420
and any sort of downstream application is

360
00:10:48,420 --> 00:10:49,560
making sure it's clean.

361
00:10:49,560 --> 00:10:51,420
So that's one of the things that we try to

362
00:10:51,420 --> 00:10:53,920
consult clients on is that you got to make

363
00:10:53,920 --> 00:10:56,880
sure that that data is in a way to actually

364
00:10:56,880 --> 00:10:57,840
get insights from.

365
00:10:57,840 --> 00:11:00,400
So as everyone here probably knows, crap

366
00:11:00,400 --> 00:11:01,220
in and crap out.

367
00:11:01,220 --> 00:11:03,420
Of course, but that makes life easier and

368
00:11:03,420 --> 00:11:05,360
faster to produce my results.

369
00:11:05,360 --> 00:11:06,440
That's super helpful.

370
00:11:06,440 --> 00:11:07,760
Thank you very much for the insight.

371
00:11:07,760 --> 00:11:09,080
Very nice talking to you.

372
00:11:09,080 --> 00:11:10,100
Have a great rest of the show.

373
00:11:10,100 --> 00:11:10,500
Likewise.

374
00:11:10,500 --> 00:11:14,300
Take care.

375
00:11:14,300 --> 00:11:15,220
Hi everyone.

376
00:11:15,220 --> 00:11:16,740
We're here with Catherine Ekerson from

377
00:11:16,740 --> 00:11:18,340
Blue Yonder who is leading the global

378
00:11:18,340 --> 00:11:20,920
industry strategy and Catherine, one of my

379
00:11:20,920 --> 00:11:22,700
rapid-fire questions for all vendors here

380
00:11:22,700 --> 00:11:24,800
today and I want to read it to you because

381
00:11:24,800 --> 00:11:26,300
you gave a really great insight in our

382
00:11:26,300 --> 00:11:28,000
previous conversation.

383
00:11:28,000 --> 00:11:30,000
So what's the single biggest innovation

384
00:11:30,000 --> 00:11:32,600
you've seen here so far or you brought

385
00:11:32,600 --> 00:11:35,080
here that could move the needle for CPG

386
00:11:35,080 --> 00:11:36,000
or retail?

387
00:11:36,000 --> 00:11:37,980
Yeah, well the real thing is that it's not CPG

388
00:11:37,980 --> 00:11:40,800
or retail but these days it is now CPG and

389
00:11:40,800 --> 00:11:41,460
retail.

390
00:11:41,460 --> 00:11:42,120
Of course.

391
00:11:42,120 --> 00:11:44,280
So how does a technology company such

392
00:11:44,280 --> 00:11:46,880
as Blue Yonder provide access between

393
00:11:46,880 --> 00:11:49,760
your CPG vendors and your grocery retail

394
00:11:49,760 --> 00:11:52,319
stores or convenience stores in order to

395
00:11:52,319 --> 00:11:54,580
close the loop on your supply chains as a

396
00:11:54,580 --> 00:11:55,480
whole, right?

397
00:11:55,480 --> 00:11:55,960
Of course, yeah.

398
00:11:55,960 --> 00:11:57,940
So there's been a lot of innovation around

399
00:11:57,940 --> 00:12:00,120
how do you orchestrate your supply chain

400
00:12:00,120 --> 00:12:02,480
end to end and we're even taking it one

401
00:12:02,480 --> 00:12:03,960
step further and saying how do you

402
00:12:03,960 --> 00:12:06,840
orchestrate your supply chain beyond your

403
00:12:06,840 --> 00:12:08,280
four walls, right?

404
00:12:08,280 --> 00:12:10,000
So how do we collaborate more with your

405
00:12:10,000 --> 00:12:14,120
CPG vendors in order to get your supplies

406
00:12:14,120 --> 00:12:15,880
faster to the trends that you're seeing on

407
00:12:15,880 --> 00:12:17,160
the grounds in your store?

408
00:12:17,160 --> 00:12:18,780
And let's imagine I'm a key account

409
00:12:18,780 --> 00:12:21,760
manager at PepsiCo working with Walmart

410
00:12:21,760 --> 00:12:23,940
in North America and I'm also demand

411
00:12:23,940 --> 00:12:25,760
planner on the supply chain team.

412
00:12:25,760 --> 00:12:27,180
How am I going to benefit from a solution

413
00:12:27,180 --> 00:12:28,819
like Blue Yonder between those

414
00:12:28,819 --> 00:12:30,600
cross-functional teams?

415
00:12:30,600 --> 00:12:32,860
Yeah, so being able to provide PepsiCo

416
00:12:32,860 --> 00:12:35,980
with consumer insights much quicker in a

417
00:12:35,980 --> 00:12:39,380
more consumable data way so that

418
00:12:39,380 --> 00:12:41,020
PepsiCo supply planners and demand

419
00:12:41,020 --> 00:12:44,020
planners can secure the inventory for your

420
00:12:44,020 --> 00:12:45,040
store first.

421
00:12:45,040 --> 00:12:45,240
Right.

422
00:12:45,240 --> 00:12:47,020
And so you have, you know, better and

423
00:12:47,020 --> 00:12:48,960
faster access to serving your customers

424
00:12:48,960 --> 00:12:52,580
than the traditional, you know, sending of

425
00:12:52,580 --> 00:12:54,660
information at a cadence or through, you

426
00:12:54,660 --> 00:12:56,180
know, Excel spreadsheets or emails and

427
00:12:56,180 --> 00:12:57,160
those types of things.

428
00:12:57,160 --> 00:12:58,480
That makes my life easier.

429
00:12:58,480 --> 00:12:58,840
Yeah.

430
00:12:58,840 --> 00:13:00,040
Thank you very much.

431
00:13:00,040 --> 00:13:02,040
Thanks for joining on e-commerce.

432
00:13:02,040 --> 00:13:03,940
Grocery Shoppe going live in Vegas.

433
00:13:03,940 --> 00:13:08,020
Stay tuned.

434
00:13:08,020 --> 00:13:08,819
Hi, Megan.

435
00:13:08,819 --> 00:13:10,560
Very nice having you here on the Grocery

436
00:13:10,560 --> 00:13:11,840
Shoppe floor today.

437
00:13:11,840 --> 00:13:13,060
It's great to be here.

438
00:13:13,060 --> 00:13:14,140
Thanks for having me over.

439
00:13:14,140 --> 00:13:15,700
We're with Megan Murphy from Stackline.

440
00:13:15,700 --> 00:13:17,360
She's leading the marketing team on the

441
00:13:17,360 --> 00:13:18,819
Stackline product development and she's

442
00:13:18,819 --> 00:13:20,480
going to talk a little about what we

443
00:13:20,480 --> 00:13:21,560
discussed earlier.

444
00:13:21,560 --> 00:13:23,100
But what I wanted to ask you today, we

445
00:13:23,100 --> 00:13:24,520
talked about something really interesting.

446
00:13:24,520 --> 00:13:26,780
You brought it up in our conversation and I

447
00:13:26,780 --> 00:13:28,460
asked you about agentic AI and the

448
00:13:28,460 --> 00:13:29,240
trending topic.

449
00:13:29,240 --> 00:13:30,940
This is one of my rapid-fire questions.

450
00:13:30,940 --> 00:13:31,380
Yeah.

451
00:13:31,380 --> 00:13:33,620
How do you see shopper behavior evolving

452
00:13:33,620 --> 00:13:36,540
over the next 24 months and how you and

453
00:13:36,540 --> 00:13:38,200
the Stackline team is positioning, Corey?

454
00:13:38,200 --> 00:13:39,600
Yeah, it's a great question.

455
00:13:39,600 --> 00:13:41,280
We've spent a lot of time thinking and

456
00:13:41,280 --> 00:13:43,260
talking about it recently.

457
00:13:43,260 --> 00:13:46,500
Definitely, agentic ordering is really just

458
00:13:46,500 --> 00:13:48,560
the hot news right now.

459
00:13:48,560 --> 00:13:52,080
ChatGPT, a lot of articles this week.

460
00:13:52,080 --> 00:13:55,060
Amazon Rufus is going to start actually

461
00:13:55,060 --> 00:13:58,880
allowing ordering within the Rufus tool.

462
00:13:58,880 --> 00:14:00,780
And so at Stackline, how do we think about

463
00:14:00,780 --> 00:14:03,080
brands being able to prepare for that

464
00:14:03,080 --> 00:14:05,100
agentic ordering and what do they need to

465
00:14:05,100 --> 00:14:05,920
do?

466
00:14:05,920 --> 00:14:08,220
And a lot of it just really comes down to

467
00:14:08,220 --> 00:14:09,520
content.

468
00:14:09,520 --> 00:14:12,280
Making sure that your content is written

469
00:14:12,280 --> 00:14:14,640
for AI to be able to consume.

470
00:14:14,640 --> 00:14:16,740
Making sure you've got all of the right

471
00:14:16,740 --> 00:14:20,220
attribution and features so that the tool

472
00:14:20,220 --> 00:14:22,180
can recommend that product for the

473
00:14:22,180 --> 00:14:24,020
consumer it's searching for.

474
00:14:24,020 --> 00:14:25,600
So those are the things that we're really

475
00:14:25,600 --> 00:14:27,660
focused on right now is how do we help

476
00:14:27,660 --> 00:14:29,860
brands prepare for agentic ordering.

477
00:14:29,860 --> 00:14:32,100
It's really amazing because everything was

478
00:14:32,100 --> 00:14:34,839
developed for the SEO practices, PPC

479
00:14:34,839 --> 00:14:36,900
practices, which we've been for the last 15

480
00:14:36,900 --> 00:14:37,319
years.

481
00:14:37,319 --> 00:14:39,400
Now we're trying to adopt all the

482
00:14:39,400 --> 00:14:41,440
algorithms we have, all the ML models we

483
00:14:41,440 --> 00:14:44,120
have for agentic and AI algorithms.

484
00:14:44,120 --> 00:14:46,420
And it's about semantic positioning, it's

485
00:14:46,420 --> 00:14:48,480
about other rankings.

486
00:14:48,480 --> 00:14:50,000
Can you share one example, one

487
00:14:50,000 --> 00:14:52,120
successful example from Stackline client

488
00:14:52,120 --> 00:14:53,920
phase on the consumer side and one

489
00:14:53,920 --> 00:14:54,620
retailer side?

490
00:14:54,620 --> 00:14:55,660
What would be the most prominent

491
00:14:55,660 --> 00:14:56,440
example?

492
00:14:56,440 --> 00:14:58,620
Yeah, we've definitely been partnering with

493
00:14:58,620 --> 00:15:00,000
some of our clients.

494
00:15:00,000 --> 00:15:02,020
And we've got some great case studies on

495
00:15:02,020 --> 00:15:04,660
our webpage that talk about some of those

496
00:15:04,660 --> 00:15:07,360
exact quotes and where we've gone in and

497
00:15:07,360 --> 00:15:10,000
helped them to revamp their content and

498
00:15:10,000 --> 00:15:12,700
think about how to present it for the future.

499
00:15:12,700 --> 00:15:14,620
I'm looking forward to hearing and reading

500
00:15:14,620 --> 00:15:16,820
about it and I'm gonna check with you if

501
00:15:16,820 --> 00:15:18,480
there's any public information that you can

502
00:15:18,480 --> 00:15:19,120
share with us.

503
00:15:19,120 --> 00:15:21,620
That's perfect, thanks for having me.

504
00:15:21,620 --> 00:15:23,580
Grocery Shop continues from Vegas on

505
00:15:23,580 --> 00:15:24,340
day three.

506
00:15:24,340 --> 00:15:28,740
We're gonna be back soon.

507
00:15:28,740 --> 00:15:30,040
Hello, Greg.

508
00:15:30,040 --> 00:15:31,920
We're with Greg Stevens from Osmos.

509
00:15:31,920 --> 00:15:33,460
I've been hearing a lot about them lately

510
00:15:33,460 --> 00:15:35,140
and Greg is leading the Europe and

511
00:15:35,140 --> 00:15:37,020
Americas as the CEO of Osmos.

512
00:15:37,020 --> 00:15:38,240
Thanks for being with us today.

513
00:15:38,240 --> 00:15:40,160
I'm gonna throw one of my rapid fire

514
00:15:40,160 --> 00:15:41,560
questions that I've been asking a lot of

515
00:15:41,560 --> 00:15:42,840
vendors today.

516
00:15:42,840 --> 00:15:44,540
Where do you see the agentic AI

517
00:15:44,540 --> 00:15:46,680
implementation for omni-channel

518
00:15:46,680 --> 00:15:48,860
commerce success, especially across

519
00:15:48,860 --> 00:15:50,020
retailers?

520
00:15:50,020 --> 00:15:51,980
What we're seeing is the scale driver.

521
00:15:51,980 --> 00:15:53,880
One thing that our platform does is we

522
00:15:53,880 --> 00:15:57,100
unify all the retailers, whether it's offsite,

523
00:15:57,100 --> 00:15:58,820
onsite, and in-store.

524
00:15:58,820 --> 00:16:01,140
We take all that campaign information and

525
00:16:01,140 --> 00:16:02,820
put it into a common database.

526
00:16:02,820 --> 00:16:04,280
And from that common database, we can

527
00:16:04,280 --> 00:16:06,680
apply our AI agent called Sophie to

528
00:16:06,680 --> 00:16:09,040
auto-optimize those campaigns and also

529
00:16:09,040 --> 00:16:10,580
reallocate budgets based upon

530
00:16:10,580 --> 00:16:13,220
performance that has been established by

531
00:16:13,220 --> 00:16:16,240
the retailer or based upon the goals set by

532
00:16:16,240 --> 00:16:17,220
the brands.

533
00:16:17,220 --> 00:16:18,980
And so what this is doing is it's increasing

534
00:16:18,980 --> 00:16:20,780
performance and it's lowering the

535
00:16:20,780 --> 00:16:22,460
operational overheads of both the retailers

536
00:16:23,000 --> 00:16:24,680
brands who are trying to manage these

537
00:16:24,680 --> 00:16:26,980
very complex omni-channel campaigns

538
00:16:26,980 --> 00:16:29,220
across numerous platforms.

539
00:16:29,220 --> 00:16:31,060
And we're doing it in milliseconds.

540
00:16:31,060 --> 00:16:32,819
And we're seeing the adoption rate

541
00:16:32,819 --> 00:16:34,100
skyrocket.

542
00:16:34,100 --> 00:16:37,100
Today, 70% of our onsite campaigns are

543
00:16:37,100 --> 00:16:39,319
being managed by agentic AI services

544
00:16:39,319 --> 00:16:40,360
through Sophie.

545
00:16:40,360 --> 00:16:43,060
And it's been going up continuously.

546
00:16:43,060 --> 00:16:45,860
So then on the tier levels, whether

547
00:16:45,860 --> 00:16:48,520
enterprise, mid-tier, or smaller retailer

548
00:16:48,520 --> 00:16:50,040
chains, mom and pop, where do you see

549
00:16:50,040 --> 00:16:51,819
the biggest adoption for this size of

550
00:16:51,819 --> 00:16:53,460
technology?

551
00:16:53,460 --> 00:16:55,740
We're seeing it across the spectrum.

552
00:16:55,740 --> 00:16:58,460
First, taking the brand perspective, brands

553
00:16:58,460 --> 00:17:00,540
of all sizes are taking advantage of it, but

554
00:17:00,540 --> 00:17:01,640
they're taking advantage of different

555
00:17:01,640 --> 00:17:02,840
pieces.

556
00:17:02,840 --> 00:17:05,099
So larger brands might not give up the

557
00:17:05,099 --> 00:17:08,180
budget control to AI, but they might give up

558
00:17:08,180 --> 00:17:10,740
the product selection control or the

559
00:17:10,740 --> 00:17:12,900
allocation by channel control.

560
00:17:12,900 --> 00:17:15,380
Smaller brands might give more control to

561
00:17:15,380 --> 00:17:16,619
the AI because they have less

562
00:17:16,619 --> 00:17:18,579
sophistication in-house perhaps.

563
00:17:18,579 --> 00:17:20,420
Across the large retailers versus small

564
00:17:20,420 --> 00:17:22,579
retailers, we don't see any difference.

565
00:17:22,579 --> 00:17:25,440
They're all using it as a tool to help create

566
00:17:25,440 --> 00:17:27,240
better performing campaigns, drive more

567
00:17:27,240 --> 00:17:29,020
sales, and create better buying

568
00:17:29,020 --> 00:17:30,440
propositions for their brands.

569
00:17:30,440 --> 00:17:32,340
And it just lowers their overheads, right?

570
00:17:32,340 --> 00:17:34,520
Fewer people are required to do the work

571
00:17:34,520 --> 00:17:34,920
of many.

572
00:17:34,920 --> 00:17:36,920
And I see the digital natives or emerging

573
00:17:36,920 --> 00:17:39,060
scale-ups also can benefit this type of

574
00:17:39,060 --> 00:17:41,120
acceleration technology without the FTE

575
00:17:41,120 --> 00:17:43,080
dependency.

576
00:17:43,080 --> 00:17:44,760
Yeah, absolutely, I accept.

577
00:17:44,760 --> 00:17:46,820
And region-wise, where do you see the

578
00:17:46,820 --> 00:17:48,000
highest growth?

579
00:17:48,000 --> 00:17:49,580
Do you see any saturation in any of the

580
00:17:49,580 --> 00:17:50,060
regions?

581
00:17:50,060 --> 00:17:51,400
Do you see acceleration in any of the

582
00:17:51,400 --> 00:17:52,100
markets?

583
00:17:52,100 --> 00:17:55,800
Just overall, we're AI-specific adoption.

584
00:17:55,800 --> 00:17:57,820
Right, yeah, AI-specific.

585
00:17:57,820 --> 00:18:00,160
I don't see any.

586
00:18:00,160 --> 00:18:02,060
We're looking at the data across our

587
00:18:02,060 --> 00:18:04,480
clients in Singapore and Hong Kong and

588
00:18:04,480 --> 00:18:08,280
South Africa, India, South America, US, and

589
00:18:08,280 --> 00:18:10,940
the adoption is pretty similar.

590
00:18:10,940 --> 00:18:12,800
There might be different adoption rates

591
00:18:12,800 --> 00:18:15,940
within certain features, but I'd have to send

592
00:18:15,940 --> 00:18:17,639
you our AI report, which I can't.

593
00:18:17,639 --> 00:18:18,700
Oh, I'd love to do that.

594
00:18:18,700 --> 00:18:20,840
I'd love to dig in and share more insights.

595
00:18:20,840 --> 00:18:23,080
Would you call it the AI portion of it, AI

596
00:18:23,080 --> 00:18:24,720
acceleration and automation portion of it,

597
00:18:24,720 --> 00:18:26,980
in its nascency, or it's in the more mature

598
00:18:26,980 --> 00:18:27,660
stages?

599
00:18:27,660 --> 00:18:32,400
It's in its nascency, because it's learning,

600
00:18:32,400 --> 00:18:33,639
people are getting more comfortable with

601
00:18:33,639 --> 00:18:33,900
it.

602
00:18:33,900 --> 00:18:35,040
Well, there's two sides of it.

603
00:18:35,040 --> 00:18:38,880
People have to accept it and trust it.

604
00:18:38,880 --> 00:18:41,220
And the data has to be, the hygiene of the

605
00:18:41,220 --> 00:18:43,760
data that the AI uses has to be- Timelessly

606
00:18:43,760 --> 00:18:44,280
crucial, yeah.

607
00:18:44,280 --> 00:18:46,680
Yeah, and so using a single platform like

608
00:18:46,680 --> 00:18:47,960
Osmos to bring all your marketing

609
00:18:47,960 --> 00:18:51,320
channels into one, really allows for that.

610
00:18:51,320 --> 00:18:54,780
And two, right now for us, the AI is on the

611
00:18:54,780 --> 00:18:56,000
campaign side.

612
00:18:56,000 --> 00:18:57,820
It's now gonna be moved to the retailer

613
00:18:57,820 --> 00:19:01,460
controls of our platform so that they can

614
00:19:01,460 --> 00:19:02,800
start looking at yield.

615
00:19:02,800 --> 00:19:04,720
I think they'll be more willing to give more

616
00:19:04,720 --> 00:19:06,520
control just for the sake of speed and

617
00:19:06,520 --> 00:19:07,060
automation.

618
00:19:07,060 --> 00:19:08,840
There is so much metadata to look at that

619
00:19:08,840 --> 00:19:11,260
any one person cannot go through it all.

620
00:19:11,260 --> 00:19:12,960
And you need help, you need software to

621
00:19:12,960 --> 00:19:16,360
help tell you where to focus.

622
00:19:16,360 --> 00:19:17,660
So the humans are still making the final

623
00:19:17,660 --> 00:19:19,660
decisions, but we're surfacing all the

624
00:19:19,660 --> 00:19:20,940
actions that should be taken.

625
00:19:20,940 --> 00:19:21,800
That's super cool.

626
00:19:21,800 --> 00:19:22,440
Thank you very much.

627
00:19:22,440 --> 00:19:23,420
Thanks for being with us.

628
00:19:23,420 --> 00:19:24,060
Have a great show.

629
00:19:24,060 --> 00:19:30,040
Yeah, thank you.

630
00:19:30,040 --> 00:19:32,020
Hi Jason, thanks for being with us today.

631
00:19:32,020 --> 00:19:34,139
And we're in the Grocery Shop day three.

632
00:19:34,139 --> 00:19:35,300
Great to be here.

633
00:19:35,300 --> 00:19:37,340
So Jason Bush is currently leading the

634
00:19:37,340 --> 00:19:38,639
strategy for gain.

635
00:19:38,639 --> 00:19:40,300
And I watched a great presentation from

636
00:19:40,300 --> 00:19:42,639
his team yesterday on the green stage.

637
00:19:42,639 --> 00:19:43,900
And you were talking about the team

638
00:19:43,900 --> 00:19:46,240
members, but I wanna talk about one of

639
00:19:46,240 --> 00:19:47,880
my rapid fire questions, which I've been

640
00:19:47,880 --> 00:19:50,180
hearing a lot in the last three days.

641
00:19:50,180 --> 00:19:53,340
Which AI or data capability do you think is

642
00:19:53,340 --> 00:19:55,460
being oversold and which one is

643
00:19:55,460 --> 00:19:56,639
underrated?

644
00:19:56,639 --> 00:19:58,840
And is agentic AI is one of them?

645
00:19:58,840 --> 00:20:00,000
It's a really good question.

646
00:20:00,000 --> 00:20:02,760
And depending on the studies you look at,

647
00:20:02,760 --> 00:20:05,040
I've seen some coming out of MIT, you see

648
00:20:05,040 --> 00:20:08,280
95% of AI implementations, quote, failing

649
00:20:08,280 --> 00:20:10,540
or not delivering the value that they're

650
00:20:10,540 --> 00:20:12,520
intended to deliver today.

651
00:20:12,520 --> 00:20:14,940
And I think a lot of the issues come down

652
00:20:14,940 --> 00:20:17,160
to how agents are being sold.

653
00:20:17,160 --> 00:20:18,720
So a lot of my friends who've been

654
00:20:18,720 --> 00:20:20,800
successful in software sales in the past 25

655
00:20:20,800 --> 00:20:22,740
years are now all in on agents, typically

656
00:20:22,740 --> 00:20:24,460
for big software companies.

657
00:20:24,460 --> 00:20:26,900
And it sounds great on principle, extend

658
00:20:26,900 --> 00:20:28,980
the capability of a traditional package

659
00:20:28,980 --> 00:20:30,080
SaaS app.

660
00:20:30,080 --> 00:20:32,540
But in reality, package SaaS is designed

661
00:20:32,540 --> 00:20:35,280
for humans to be more productive.

662
00:20:35,280 --> 00:20:37,040
And so I think what's being undersold

663
00:20:37,040 --> 00:20:39,700
today is the notion of moving from agent

664
00:20:39,700 --> 00:20:42,100
extensions, which really aren't delivering

665
00:20:42,100 --> 00:20:44,660
the value, and some are taking forever to

666
00:20:44,660 --> 00:20:45,160
deliver.

667
00:20:45,160 --> 00:20:46,300
I think part of the issue is they're being

668
00:20:46,300 --> 00:20:47,320
oversold.

669
00:20:47,320 --> 00:20:49,180
But I think what is being undersold are

670
00:20:49,180 --> 00:20:51,540
actual capabilities to provide essentially

671
00:20:51,540 --> 00:20:53,620
digital arms and legs to companies to get

672
00:20:53,620 --> 00:20:56,020
more work done without necessarily

673
00:20:56,020 --> 00:20:58,440
having humans in the loop for everything.

674
00:20:58,440 --> 00:21:00,840
And we see tremendous demand for this

675
00:21:00,840 --> 00:21:01,880
coming down the pike.

676
00:21:01,880 --> 00:21:03,820
I think there's huge technical challenges to

677
00:21:03,820 --> 00:21:06,120
software in building out capabilities here,

678
00:21:06,120 --> 00:21:08,720
but the notion of not relying on humans to

679
00:21:08,720 --> 00:21:11,400
get work done, but actually using agentic

680
00:21:11,400 --> 00:21:14,300
beyond just human enablement to really

681
00:21:14,300 --> 00:21:17,180
drive results and performance without

682
00:21:17,180 --> 00:21:17,980
scale.

683
00:21:17,980 --> 00:21:19,540
You know, the notion of, and I was talking

684
00:21:19,540 --> 00:21:21,100
to somebody at the show here,

685
00:21:21,100 --> 00:21:23,900
two-person company growing massively

686
00:21:23,900 --> 00:21:26,820
in tons of big box stores in Walmart.

687
00:21:26,820 --> 00:21:29,000
Her vision for operations is not to hire

688
00:21:29,000 --> 00:21:29,680
people.

689
00:21:29,680 --> 00:21:31,320
She wants to hire agents to do the work

690
00:21:31,320 --> 00:21:32,060
for her.

691
00:21:32,060 --> 00:21:34,940
And we think that's the future.

692
00:21:34,940 --> 00:21:38,200
And which enterprise, let me ask you

693
00:21:38,200 --> 00:21:40,260
differently, which level of companies are

694
00:21:40,260 --> 00:21:42,060
benefiting the most and from a solution

695
00:21:42,060 --> 00:21:42,520
like Gain?

696
00:21:42,520 --> 00:21:45,020
Is it enterprise with the large teams or is it

697
00:21:45,020 --> 00:21:46,680
digital natives with smaller teams,

698
00:21:46,680 --> 00:21:47,800
mid-tier companies?

699
00:21:47,800 --> 00:21:50,120
Is it affordable for smaller companies, for

700
00:21:50,120 --> 00:21:50,980
instance?

701
00:21:50,980 --> 00:21:51,980
Where do you see the biggest interest

702
00:21:51,980 --> 00:21:52,980
coming from?

703
00:21:52,980 --> 00:21:55,100
Yeah, I think it's going to shift dramatically.

704
00:21:55,100 --> 00:21:57,140
I mean, we are on the forefront of the deep

705
00:21:57,140 --> 00:21:59,020
tech build out of this stuff.

706
00:21:59,020 --> 00:22:01,060
And to make the economics work at first,

707
00:22:01,060 --> 00:22:03,060
you know, we've got to go from mid-tier

708
00:22:03,060 --> 00:22:04,520
and enterprise.

709
00:22:04,520 --> 00:22:06,980
We did our first negotiation with an AI

710
00:22:06,980 --> 00:22:09,280
employee two days ago with a live

711
00:22:09,280 --> 00:22:10,000
supplier.

712
00:22:10,000 --> 00:22:11,540
We've been building for a year.

713
00:22:11,540 --> 00:22:13,139
So we're currently working with three

714
00:22:13,139 --> 00:22:15,480
mid-tier companies and one really large

715
00:22:15,480 --> 00:22:17,420
enterprise retailer.

716
00:22:17,420 --> 00:22:19,580
But I see very quickly in the next 12

717
00:22:19,580 --> 00:22:21,760
months, something that could roll down to

718
00:22:21,760 --> 00:22:24,780
the SME market for sure.

719
00:22:24,780 --> 00:22:25,880
But when you're pioneering new

720
00:22:25,880 --> 00:22:27,920
technology, you really want big design

721
00:22:27,920 --> 00:22:29,780
partners so you can hear what their

722
00:22:29,780 --> 00:22:31,520
problems are and you can design around

723
00:22:31,520 --> 00:22:32,120
them.

724
00:22:32,120 --> 00:22:34,520
I'm willing to hear more about this for our

725
00:22:34,520 --> 00:22:35,639
own agency as well.

726
00:22:35,639 --> 00:22:37,060
And I'm looking forward to hearing more of

727
00:22:37,060 --> 00:22:37,660
the updates.

728
00:22:37,660 --> 00:22:38,500
Thank you very much.

729
00:22:38,500 --> 00:22:39,020
It was a pleasure, thank you.

730
00:22:39,020 --> 00:22:40,020
Thanks for being with us, Jason.

731
00:22:40,020 --> 00:22:41,120
Have a great session.

732
00:22:41,120 --> 00:22:42,460
Thank you for listening.

733
00:22:42,460 --> 00:22:44,120
eCommerce continues providing insights

734
00:22:44,120 --> 00:22:47,580
of eCommerce, retail media, digital shelf,

735
00:22:47,580 --> 00:22:49,660
and artificial intelligence use cases for

736
00:22:49,660 --> 00:22:51,139
global CPGs.

737
00:22:51,139 --> 00:22:53,360
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738
00:22:53,360 --> 00:22:55,100
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739
00:22:55,100 --> 00:22:57,139
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740
00:22:57,139 --> 00:22:59,360
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741
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