Nick Goad, BCG's global co-leader for retail merchandising, argues that retail's biggest problem isn't a lack of data, it's the gap between data and real customer understanding. He explains how AI and large language models are finally bridging that divide, making it possible to localize assortment, sharpen pricing, and accelerate product innovation at scale. For leaders wondering where to start, he makes the case for codifying the knowledge already in merchants' heads.

What You’ll Learn:

• Retailers have more data than ever, but AI finally gives them the tools to turn it into action merchants can actually trust.
• The biggest near-term opportunities are in assortment localization, smarter pricing, and AI-accelerated product innovation.
• The new data advantage for retailers isn't raw data — it's codifying the knowledge already in their merchants' heads.

Learn More:

• BCG’s Latest Thinking on the Retail Industry: https://on.bcg.com/4bYoe8x
• Merch AI by BCGX: https://on.bcg.com/3RxTfJF

Chapters

(0:00) Has Retail Lost Its Personal Touch?

(1:13) Serving Customers Before the Digital Age

(2:33) The Internet’s Impact on Retail

(3:11) Does More Data Mean Better Customer Understanding?

(4:12) Why Are Retailers Drowning in Complexity?

(5:32) What Can AI Do to Help Retailers?

(7:02) Will AI Create a New Data Overload?

(7:54) What Are Retail Leaders Hopes and Fears for AI?

(9:02) Will AI Get Products to the Shelf Faster?

(9:57) Will AI Influence Product Design?

(10:37) Can AI Create More Personalized Loyalty Programs?

(11:34) How Will AI Shape the Shopping Experience?

(12:02) Is Physical Retail Here to Stay?

(13:36) Will AI Make All Our Shopping Decisions for Us?

(14:23) Where Can Retailers Make the Biggest Changes?

(15:00) Where Should Retailers Apply AI First?

(15:48) What New Data Advantage Can AI Create?

Meet the Expert:

Nick Goad, BCG Managing Director & Senior Partner: https://on.bcg.com/4wuIlU0

Listen to Other Episodes of The So What from BCG podcast

Punch Cards, Frequent Flyers, and the Future of Loyalty: https://lnk.to/so-what-BCG-Crouch-Loyalty09

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Speaker: 00:00:00

- My so what is retail got big

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and retailers stopped knowing you,

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and AI is about to change that.

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It's going to get really exciting

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for consumers and retailers.

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- Welcome to "The So What

from BCG," the podcast

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that explores the big

ideas shaping business,

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the economy, and society.

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I'm Georgie Frost

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at the Consumer Goods

Forum in Vienna,

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and joining me today

is Nick Goad,

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BCG's global co-leader

for retail merchandising.

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In this episode, retailers have never had

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more information about

their customers,

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yet understanding them has

arguably never been harder.

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So how might AI reshape that relationship,

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and what could that

mean for the future

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of merchandising and shopping?

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Welcome, Nick. Nick, when

I think about retailing,

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I think about my mum's story

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of working in Woolworths

as a Saturday girl

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and my grandma working

in Army & Navy,

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both shops that have

sadly disappeared

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from the British high street.

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- Yeah.

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- You also have a story

of your grandparents

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who, I believe,

ran a retail store.

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Just take us back

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to those days of ...

- Yeah.

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- my grandparents, your grandparents.

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- Yeah.

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- How has that changed, that relationship

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with their customers to now?

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Take us on a little journey if you would.

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- Yeah, sure. So I grew up in

a small, rural town in Vermont

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and lived just up the

hill from my grandparents'

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home furnishing store.

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- Oh, lovely.

- They sold carpet and paint

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and everything you needed to

make a home in, in Vermont.

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You know, the thing about it was

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they were not category experts.

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They didn't know about, you

know, the fiber of the carpet

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or the upholstery,

you know, detail.

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But what they did know were

their customers, intimately.

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They knew what, you know,

Sally might like to buy for,

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you know, redecorating her home.

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They knew what Jim might like to,

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to put on the wall, et cetera.

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And when they got in their car

to go down to North Carolina

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for the annual shows to

choose what furniture

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to put in their showroom, for

example, they had that,

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those specific

customers in mind.

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- Yeah.

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- When they might like

to splurge on something,

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when maybe it was more of a

frugal or practical purchase.

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And they really made sure

that that store was stocked

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with things that their

customers would, would enjoy

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and would be willing to purchase.

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- Was it just about geographic

proximity, I suppose,

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to knowing your customers

or something else?

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- No, it was much more,

you know, human

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and innate than that, right?

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It was really about

understanding sort of

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what that customer needed

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and what brought them joy

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and what, therefore,

they found true value in

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and would be willing to spend.

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And I think, you know,

retailers today have gotten

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really big, and it's

hard for them

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to stay that connected

to customers,

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even if they know the

category really well.

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- What do you think has

been the biggest shift

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in consumer behavior since

your grandparents' time?

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- Yeah. I think it's really

the expectation of choice,

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you know, that we can go online

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or walk into a store and,

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and we know that we have

a global catalog almost

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in our phones and

available to us.

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And so when we go online,

we expect to be able

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to find virtually anything

that we could dream up.

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And if not, there's somebody

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that will maybe make it for us.

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When we go in a store,

we expect that,

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that that retailer has really

thought very carefully

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about what they've put there

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and from all of the

choices that are out there,

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and that doesn't always happen.

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So I think that's

the real opportunity.

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- It's interesting you

talk about sort

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of the personal,

the human touch.

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That is sort of

translated, I suppose,

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much more in

consumer data now.

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That's what perhaps

is being looked at

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rather than those

conversations in the stores.

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We have an abundance--

we know we do--

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- Yeah.

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- of this data.

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Why do you think it's not

quite translated in a way

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that perhaps could

be the most useful

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- Yeah.

- for retailers?

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- Yeah. I mean, a couple of things.

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First off, translating the

data is hard work, right?

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First, it's got to

be in great shape.

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Second, you have to

have really smart people

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that know how to

wrangle it and,

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and do the right analytics

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and use the right, you know,

data science techniques

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to have it produce

a meaningful answer

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that's, that's accurate.

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And then third, and maybe

most importantly, is

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that you really need to be

able to then explain it,

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explain why that's

the right answer.

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And I think that's

been the big gap

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between, say, the

data science folks

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and the merchant folks

on the commercial side

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of a business is really being

able to bridge that gap

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so that one can trust the other.

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- Your research suggests

that retailers are

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drowning in complexity.

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- Yeah.

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- Explain to those of us

who are outside of retail

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what that actually looks like

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on a sort of, I suppose,

day-to-day environment.

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- Yeah. So I mean, let's,

let's take an example.

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Let's say that maybe

you're the category manager

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for vitamins in

a US drugstore, okay?

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So your job is to try to

drive sales and profit

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for the vitamin category--

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this, you know, one shelf

that you're responsible for

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in, say, you know, 5,000

stores across the country.

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That's a pretty complicated job.

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You have to decide, you know,

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who are my customers

and what do they need?

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And those customers have

a lot of other choices,

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whether it's, you know,

a vitamin shop

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that specializes in just

that down the street

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or Amazon online.

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They need to think about

what am I going

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to uniquely put in the store

that's going to, you know,

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best suit my customers

that are going to walk in?

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They have to think about the

right price to put on that,

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how to promote it,

when to promote it,

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how deep to promote it.

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They have to keep

it in stock, right?

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How do I decide how much

to put on the shelf

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so that it's always there,

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but I don't waste a lot

of money on the inventory?

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And so they've got a myriad of

complexities all while trying

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to, you know, drive

innovation in their category

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and have deep relationships

with their suppliers

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so that they can really

deliver for the customer

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at the end of the day.

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- So let's get into the AI question then.

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What can it really do to help

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what you're talking about here?

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I mean, you've spoken

about complexity,

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but there's also the

explainability gap, which is

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what sophisticated analytics can recommend

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and what, I suppose, retailers

feel confident to act on.

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We've seen a bit of

a disparity there.

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Where does AI fit

into the picture?

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- Merchants are being

asked to do a lot, right?

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I had mentioned

all that complexity,

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but then it's at a scale

of, you know, hundreds

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of items in

thousands of stores.

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And you need data to be

able to actually solve

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that problem if you're

just one person.

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But without being able to understand

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how what the data science

answer is telling you

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relative to the strategy

that you're trying to drive,

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you can't really bridge that gap.

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With the newest AI techniques

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and leveraging large language

models, we finally can.

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Like we can actually speak

in category manager terms

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so that the category

manager can understand

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what the science is suggesting

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and how that fits to the

strategy that they're trying

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to describe and push the

direction of their category.

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Another example would be

in a regional grocery store,

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for example, where

we've had a chance

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to work with the

merchants to make the work

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between them and their

suppliers much more efficient.

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So rather than having to prepare and pull

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tons of data for all the

conversations that they have,

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they're able to use AI in order

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to help speed up that process

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and help them develop the strategies

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and help them develop the

ways that they're going

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to discuss the approaches

they want to take

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with the assortments

with their vendors.

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- Just explain to me

because we spoke earlier

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about the fact that

retailers that have all

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of this data about their customers

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and yet turning it, insight

into action is proven

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to be a little bit of a problem.

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How do you know, I suppose,

that with these abundance

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of AI tools, that we

won't have a similar issue

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with interpreting the data,

having too much of it,

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where do we prioritize?

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- Well, I mean, that's

the advent of the tools,

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and what we're doing is

designing them to be able

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to actually fit

a new process, right?

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So rather than just trying

to fit a tool to the way

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that merchants worked

today, work today,

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we're trying to fit

the tools to the way

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that we can imagine

they would work

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with the advantages

of these in the future.

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And so they will

actually learn to trust

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what the tools are telling them

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and take out the grunt

work that's necessary

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and be able to focus on the

more exciting innovations

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that, that they want to drive

for their category.

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- What are leaders in this

space talking to you about?

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You speak to, you speak to

them all the time.

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- Yeah.

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- What are their biggest concerns?

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What is their greatest excitement?

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- Three things that

I talk most about

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in the merchandising space

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where there's real excitement is

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around assortment,

pricing, and innovation.

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- Right.

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- So we've talked a lot

about assortment already

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and how you get that to

be really truly localized.

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I think that's, that's clear.

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Pricing, while it's been

a topic around forever

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and we've been using elasticity models

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and fancy, you know,

things like that to try

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to optimize price, it's

always been a challenge

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to really consider all of

the factors that you want.

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And the third and

most exciting for me,

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frankly, is around innovation.

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Think about how AI can

understand what are the trends

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and how can we turn those trends

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of what consumers are wanting

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or thinking about

into actual products

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much more quickly on shelf.

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For example, the GLP-1 trend, right?

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For me, it's taken

a very long time for us

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to see products arrive on

shelves that fit the needs

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of consumers who are on these

new, new drugs, for example.

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But with AI, that,

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in those that are taking advantage of it,

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they're able to put those

on shelves much faster

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and, and meet the consumer need.

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- What would it look like

practically to get products

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to the shelves faster

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- Yeah.

- for AI?

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- So imagine a system that's

continuously monitoring

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what people are talking

about, what people are buying,

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and then pulling patterns

from that to understand

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where there might be

additional needs and things.

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And then, further, developing

products on the fly

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for what those products

could actually look like.

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And then, next, feeding

that into another model

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that can then take the

consumer survey itself

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in order to see how well

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that product might

actually do on shelf,

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comparing that product to

other products on shelf

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and giving the machine

a choice for what to buy

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because it's been

trained on consumers and,

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and knowing what

their preferences are.

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And so by accelerating that

pathway, right, from idea

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to product generation

to filtering it down,

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we can really get real products

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that we think have real

merit to test with customers

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and to test on shelves rather

than ones that have just been,

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you know, ideas in someone's mind.

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- Could it impact, I guess,

what's even being designed?

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- Yeah, absolutely. I mean, let's go back

Speaker: 00:10:04

to the furniture example, right?

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I actually happen to, to

know that industry now

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in my adult life, which

has, which has been fun

Speaker: 00:10:10

to kind of connect those

two things together.

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We're thinking, you know, a lot

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about how do we design

furniture with AI, right?

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How do you take a concept

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but then apply it to

different consumer needs?

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So if you think about

a couch, for example,

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how do you adjust the same design

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but make it fit for an

apartment dweller on a budget

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versus a home that might

have more money

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to splurge on a couch

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and be able to make multiple

versions of a similar product

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but that fit well in different stores?

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- One area that is quite,

I suppose, difficult

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to navigate at the moment--

Speaker: 00:10:43

we've done a podcast about

this for "The So What"--

Speaker: 00:10:45

is things like promotions

and loyalty schemes.

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You know, with changing

consumer behavior,

Speaker: 00:10:51

with all of this data,

you don't really understand

Speaker: 00:10:53

who your consumer is at the moment.

Speaker: 00:10:55

Loyalty programs are incredibly important,

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but how, how do you get it right?

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How do you see AI changing

so that almost we can

Speaker: 00:11:04

be more targeted with loyalty?

Speaker: 00:11:05

We can be more loyal as, as

consumers to, to retailers?

Speaker: 00:11:10

- So I think loyalty and

personalized pricing,

Speaker: 00:11:12

for example, has gotten a bad rap, right?

Speaker: 00:11:14

And, and the challenge has

been that marketers have had

Speaker: 00:11:16

to really preprogram a

lot of what has gone in

Speaker: 00:11:19

and anticipate, you know,

what, what is needed.

Speaker: 00:11:22

I think AI and agents will

really unlock a new way

Speaker: 00:11:25

for us to be able to

deliver promotions

Speaker: 00:11:27

on the spot for consumers

Speaker: 00:11:28

when we know they need it most

Speaker: 00:11:30

in order to help them get over

that, that hurdle of,

Speaker: 00:11:32

of clicking, clicking buy.

Speaker: 00:11:34

- So if AI can help retailers

Speaker: 00:11:36

with pricing, with

products, with promotions,

Speaker: 00:11:40

what does that change for me as a shopper?

Speaker: 00:11:43

What will it look like?

Speaker: 00:11:44

- Well, I think it means

that you'll go into a store,

Speaker: 00:11:47

and you'll feel that it

was actually, you know,

Speaker: 00:11:49

more tailor-made for you,

Speaker: 00:11:50

that you'll find things that you like.

Speaker: 00:11:52

I think it'll mean that

when you pick up the item

Speaker: 00:11:54

and look at how much it costs,

Speaker: 00:11:55

that you're more likely to

think that it's of value

Speaker: 00:11:58

because they've been thoughtful

about pricing the items

Speaker: 00:12:00

that are going to

matter most to you.

Speaker: 00:12:02

- We're talking a lot

about going into stores.

Speaker: 00:12:04

And yet, after years, it feels like

Speaker: 00:12:07

of predictions about the fact

Speaker: 00:12:09

that the bricks-and-mortar

stores are going to go,

Speaker: 00:12:11

we're actually seeing, aren't we,

Speaker: 00:12:12

something of a renaissance,

Speaker: 00:12:14

particularly among young

people, of going into stores.

Speaker: 00:12:16

And you combine that with the

preference for experience,

Speaker: 00:12:19

I suppose, over stuff now.

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Add that all together.

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What does the future of

the retail space look like?

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Are those predictions sort of over?

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Is, is that renaissance here to last?

Speaker: 00:12:30

- Yeah, I think it's a

development for sure.

Speaker: 00:12:32

And you could add to that the advent

Speaker: 00:12:34

of agentic commerce, right,

Speaker: 00:12:34

and how people will

shop on large, on GPT

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and really, you know, nothing else, right?

Speaker: 00:12:39

I think those are all

coming together in really,

Speaker: 00:12:43

in really a real confluence, right?

Speaker: 00:12:45

And what I think is people

are really being able

Speaker: 00:12:48

to focus on shopping

where they find joy

Speaker: 00:12:51

rather than having

to do it as a chore.

Speaker: 00:12:53

And that's what's really

exciting to me, right?

Speaker: 00:12:55

And I think retailers need

to understand that, you know,

Speaker: 00:12:58

you might find joy out of going

Speaker: 00:13:00

on a grocery shopping

mission to think about

Speaker: 00:13:02

what you're going

to cook for dinner.

Speaker: 00:13:04

I might find joy out of

going to a clothing store

Speaker: 00:13:06

and shopping for--

okay, I got you wrong,

Speaker: 00:13:08

but maybe it's the reverse.

Speaker: 00:13:10

- Depends if I'm hungry.

- Right.

Speaker: 00:13:13

I might find joy out of

going to a clothing store

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and thinking about what

I'm, what I'm going

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to wear to an event, right?

Speaker: 00:13:17

But it's about retailers

enabling you and I to go

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on the shopping missions

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and find joy, whether that be

browsing online or in a store,

Speaker: 00:13:26

and then leaving it to things

like large language models

Speaker: 00:13:28

to help with the

efficiency aspects, right?

Speaker: 00:13:30

You still need your

cupboard, you know, stocked,

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even if now I know you

don't really like going

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to the grocery store.

Speaker: 00:13:36

But it is interesting you

talk about that, you know,

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going in and enjoying

whether it's buying groceries

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or buying a new outfit.

Speaker: 00:13:43

With large language models,

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with, you know, so much

more data and design

Speaker: 00:13:48

around our shopping experience,

Speaker: 00:13:50

I was wondering whether

we were actually on a path

Speaker: 00:13:53

to making no decisions at

all about what we buy.

Speaker: 00:13:56

- Mmm. Yeah.

Speaker: 00:13:58

- Do you think that's possible?

Speaker: 00:13:58

- No. I don't think so.

- No?

Speaker: 00:14:00

- I don't think we're ever going

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to give everything

up to the machine.

Speaker: 00:14:02

I mean, like, I'm happy

to give up lots of things,

Speaker: 00:14:04

but I actually still enjoy

thinking about, you know,

Speaker: 00:14:06

what I might wear, right?

Speaker: 00:14:07

And so that's where I go back to the joy.

Speaker: 00:14:10

I think we're going to

keep making the decisions

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that we enjoy making

Speaker: 00:14:13

and give the ones up where we

care less about to the machine

Speaker: 00:14:16

so that we have more space to

do things that, that we like,

Speaker: 00:14:18

whether that be shopping or

spending time with family

Speaker: 00:14:21

and being productive at work.

Speaker: 00:14:23

- Where do you think the

biggest changes need to happen

Speaker: 00:14:26

in an organization to really

make the most out of AI?

Speaker: 00:14:30

What does it mean for retailers?

Speaker: 00:14:31

Where do the big changes

need to happen?

Speaker: 00:14:33

- I think too much has been focused

Speaker: 00:14:34

on the efficiency

aspects of it, right,

Speaker: 00:14:36

which makes people reticent.

Speaker: 00:14:38

Why do I want to train a

model to put me out of a job?

Speaker: 00:14:41

- Exactly.

Speaker: 00:14:42

- But that's not the point, right?.

Speaker: 00:14:43

The point is to be able

to take the knowledge

Speaker: 00:14:45

and put it into the model so

that you can then free up time

Speaker: 00:14:48

to do the parts of the job

that you enjoy most, right?

Speaker: 00:14:51

And so I think when we can reframe

Speaker: 00:14:53

what we're actually trying to do

Speaker: 00:14:54

and give people purpose

back again in a world,

Speaker: 00:14:57

then we can approach this change

Speaker: 00:14:58

with much more excitement.

Speaker: 00:15:00

- You've spoken a lot

of, about a lot of areas

Speaker: 00:15:02

where AI can make a big

difference to retailers.

Speaker: 00:15:04

How do you prioritize

because you don't want

Speaker: 00:15:06

to be, I suppose, doing

everything at once?

Speaker: 00:15:09

Yeah, I think there's twofold.

Speaker: 00:15:10

So first, you know, we

talk about deploying AI,

Speaker: 00:15:13

and I do think that's important,

Speaker: 00:15:15

putting the tools in

the hands of the staff

Speaker: 00:15:17

so that they can really embrace it

Speaker: 00:15:19

and understand what's possible

Speaker: 00:15:21

and be able to make small changes

Speaker: 00:15:22

in the way that they work to,

to drive efficiency and,

Speaker: 00:15:25

and be able to produce,

you know, more work.

Speaker: 00:15:28

The second piece is around reshaping

Speaker: 00:15:31

some of the key functions.

Speaker: 00:15:32

And so we've talked a lot

Speaker: 00:15:33

about the commercial functions.

Speaker: 00:15:34

And so prioritizing what

are the ones that are going

Speaker: 00:15:37

to be the highest impact,

the highest value generation

Speaker: 00:15:40

for the least amount of effort.

Speaker: 00:15:41

And so I think, you know,

every retailer needs

Speaker: 00:15:43

to go through that process

Speaker: 00:15:44

and get started on

one or two of those

Speaker: 00:15:46

and then proceed from there.

Speaker: 00:15:47

- Nick, we've covered the so what.

Speaker: 00:15:49

Now it's the now what.

Speaker: 00:15:51

What are the next immediate

steps that retail leaders need

Speaker: 00:15:54

to take to make the most of this?

Speaker: 00:15:57

- I think it's time to think about

Speaker: 00:15:58

what's the new data advantage, right?

Speaker: 00:16:00

Retailers have forever talked

about data as their advantage

Speaker: 00:16:03

when most retailers had

the same amount of data,

Speaker: 00:16:05

and they didn't really do that much

Speaker: 00:16:07

with it to begin with.

Speaker: 00:16:08

I think the new advantage is really going

Speaker: 00:16:10

to be the knowledge,

right, that is in,

Speaker: 00:16:12

in merchants' heads or

in retailers' heads.

Speaker: 00:16:15

And how do they codify

that knowledge

Speaker: 00:16:17

and get that written down?

Speaker: 00:16:18

Because that's what the large

language models need in order

Speaker: 00:16:21

to be able to do everything

we've spoken about today,

Speaker: 00:16:24

to bridge the explainability gap

Speaker: 00:16:26

and to be able to actually

drive a difference and,

Speaker: 00:16:29

and automate many

of the processes.

Speaker: 00:16:31

- Nick, thank you so much.

An absolute pleasure.

Speaker: 00:16:34

And thank you for listening.

Speaker: 00:16:35

If you'd like to learn

more about BCG's work

Speaker: 00:16:37

on the future of merchandising,

AI, and consumer behavior,

Speaker: 00:16:41

you'll find links

in the show notes.

Speaker: 00:16:42

Until next time.