October 21, 2025 • 18 mins
This episode dives into the mechanics of AI-powered personalization with Cédric Chéreau, Co-Founder of Eagle AI (ex-Untie Nots). Eagle AI’s platform delivers scalable, signal-based personalization that predicts behaviour and nudges loyalty — generating 7:1+ ROI and unprecedented digital engagement.
Cédric and Alex discuss:
• What personalization really means in 2025
• How gamification boosts engagement without gimmicks
• Why pragmatic AI often outperforms big “innovation” projects
• How Eagle AI achieves 7:1–20:1 ROI with minimal friction
• Why retailers must act now rather than wait for the next hype cycle
🔗 Guest: Eagle AI
🎙️ Host: The Retail Podcast
Referenced topics:
Personalization in grocery and beauty retail
Predictive AI for offer relevance
SaaS integration for signal-based personalization
ROI optimization and customer lifetime value
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
And I think what is very, very important in what we're doing is
the core philosophy of what we're doing is remaining
customer centric. What is really interesting for
customers, how will they naturally behave?
Because we're not here to force them to change their behaviour,
We're here to incentivize them to do a little bit more of what
they're already doing. Hello and welcome back to
(00:33):
another special episode of the Retail Podcast.
Now Eagle Eye, who will meet oneof the cofounders in a minute.
Cedric, I've seen all over the place.
I've seen them for years at NRF,I've seen the literature and I
also we have a shared customer that we have both worked on.
And so when I have the opportunity to interview Cedric,
I thought what a great opportunity to interview someone
(00:54):
that I pretty much can bet my bottom dollar that the problem
they solve is a massive problem for retailers right now.
So Cedric, why don't we just start with you?
I know you're the Co founder andobviously managing director of
Eagle Eye, but one, just tell usa little bit about yourself and
then tell us a little bit about Eagle Eyes to set the frame for
the conversation. Yeah, absolutely.
(01:16):
So hi, thank you very much for having me today.
I'm Cedric Chaho, I'm French, I live in in France and I'm the Co
founder of Eagle AI, which is one of the department of Eagle
Eye. What we do at Eagle Eye globally
is to help retailers to scale personalization and to make it
(01:36):
really efficient for them, for the customers and also for the
suppliers who are usually participating in all this.
And that's really the mission that we have.
How can we make it easy and effective for retailers to
operate personalization once again at a very, very large
scale. That's I don't.
You have, can you do when we talk about personalization, can
(01:59):
you frame that for us? Because sometimes people use
personalization for so many different things.
What? What is that?
If if we're just going to take another layer down and say what
personalization means to you, aneagle eye, Well, how would you
describe that? It, it, it's a very good comment
and I think you're absolutely right.
Personalization sometimes for some people means being able to
put the first name of someone inon top of an e-mail.
(02:23):
That's that's it. It's but it's not really
effective. What we mean by personalization,
eagle Eye is to be able to define the right parameters of
an offer to make it really efficient for me.
For instance, which brand, whichproduct are the most relevant
for me as a customer, probably product that are different than
(02:45):
than your favourite brands. So we all have very specific
product and, and if even if we have the same product, maybe we
can adapt the threshold that I need to reach to get a reward.
And that's what we mean by personalization, is being able
to identify, predict the naturalbehaviour of each customer,
(03:07):
define the potential of each customer to be able to really
define the threshold that each customer individually need to
reach to get a specific reward. And even the reward can be
personalised because we all havea promotional sensitivity that
is a bit different. So if we can identify all these
elements, at the end you have, you could receive an offer that
(03:29):
is completely unique that no other customer will receive,
completely adapted to your need,your affinities with the
different brand. And that's what we mean by
personalization. OK, I got you.
How does gamification fit in into that?
Because again, sometimes I feel that's misleading as a as a
subject in terms of people talk about.
Yeah, tell, tell me what? Yeah, I, I, I your perspective
(03:51):
on. That yeah, absolutely.
I think we never design the solution that we worked on as a
gamification platform, but the gamification came on top of it
and it was a little bit of a surprise.
Like the cherry on the cake, it was designed to be
personalization. The idea was to for each
customer to receive different offers with threshold to reach,
and if I reach this threshold, Iwill get a reward.
(04:14):
By definition, it's a little bitgamified.
It's not designed to be just fun.
It's designed to incentivize me to be a little more loyal to
that brand of that retailers. So there's there's a nudge,
there's an incentive here. So therefore it's a little bit
little bit gamified, but it's not fun per SE.
I would say it's not a pure gamification, yeah.
(04:35):
OK. Thank you.
You're right, I think it's, it'sfar the solution.
It's part of the solution and it's it's especially in grocery
retail, it's important to keep in mind that what is important
is for customers to be able to earn something and to earn a
reward. Having fun, playing a little
game is always nice, but in grocery report, it's not the
main mission of the of the of the retailer.
(04:56):
The main mission of the retaileris to provide something that is
clearly bringing value to the customer at the end.
How does when, if we stick with grocery or or or maybe not?
We can however you want to find answer the question.
But can you share like a pragmatic approach to AI where a
sort of simple, well defined usecase has outperformed big flashy
(05:21):
AI initiatives which you you seeacross across the industry?
I think that the multiple usage of of AIS is something very
pragmatic, probably the most basic for me, but it but it
everything is in the detail. The most basic is being able to
identify the five most importantitems for each customer.
(05:42):
When the retailer is sending an e-mail to to to an e-mail or
notification to their customers.I want to re interest all my
customers with the five best offers we have installed this
week. Just being able to do this is
difficult because you need to understand the affinities
between the products and the customers.
You also need to look at the data.
(06:04):
If I bought my laundry detergentlast week, there's no need for
you to put that on top of my list because I will not buy it
again. I will probably buy it again in
a few weeks, but not tomorrow. If you have products that are,
that have a facial value that ismore important than others,
maybe they should rank higher in, in, in the, in, in the
e-mail. So all these elements, if you
(06:25):
use AI, you're able to combine all these KPI's to be able to
really offer the best, bless you, the best 5 offers for each
customer. So it's a simple, very simple
use case. But to answer it very
efficiently you you need the power of AI to to to accomplish
(06:46):
this. And so I don't want to put words
in your mouth, but if I understand what you're saying to
me is that you look at all the signals post purchase to make
sure that communication is in line with those signals, which
is the example that you gave. If I bought detergent, don't
send me another offer on detergent to five, sorry to Five
(07:06):
products or Five Points. If you send the communication to
a customer and you say come to astore next week because we have
a lot of offers. Among all the offers we have
here are 5 that are really interesting for you collect the
best of I for the customer. We look in the past, We look at
what's happened in the past. What are the signals of a
female? And that's what Eagle I are
(07:27):
doing. Absolutely perfect.
That's OK. You're doing understood.
We're identifying which product you bought in the past to
predict which product you will probably buy in the future you
provide. That as a service or I guess is
it a? Solution.
It's a SAS solution and all the algorithm that we've built over
the years, we encapsulate them into different use cases just to
(07:50):
make it very pragmatic, very plug and play and easy to use.
We received the customer data, what happened over the last 12
months. We looked at all the interaction
between products, customers, stores and everything and we
predict what will happen in the future.
And we look at the potential of each customer if they did this
three months ago, how will they behave in three months.
(08:12):
And based on this, we are able to identify which products,
brands, categories are the most relevant to them.
And I think what is very, very important, what we're doing is
the core philosophy of what we're doing is remaining
customer centric. What are what is really
interesting for customers, how will they naturally behave?
Because we not here to force them to change their behaviour,
(08:36):
we're here to incentivize them to do a little bit more of what
they're already doing. I'm not asking customers to
switch from brand A to brand B. I'm here to tell him if you're
more loyal then you should be rewarded for that.
Yeah, OK. I don't know why you've not
taken over the industry because I mean that that sounds like the
(08:56):
ideal thing when when, when, when I talk to retailers and the
problems they face. But one of the things I read
somewhere in your literature, I don't know if it's a promise or
an example, but you look at sortof like a A7 to one return on
investment, which if true is remarkable by any standard,
which I sort of understand from the way that you've just
(09:17):
described it. I was wondering what, what are
the hidden mechanics behind performance?
And then what misconceptions do retailers have about achieving
this return? I can be even more bold.
It's old here if you say it. Oh, there.
OK, go for it. The the the 7 to 1 can be 12 to
(09:38):
one. It could be a 20 to 1 because
everything is in the way we build their parameters of the
offer. The seven to one is based on the
best practise. Once again, the idea is to
create more loyalty from customers.
So I'm going to incentivize you,Alex, with some offers on your
favourite brands and I'm going to ask you to spend a little bit
(10:00):
more on your favourite brand at this specific store.
And if you do a little bit more than you will get a reward.
Yeah, because it is incremental by design.
I'm asking you to do a little bit more than what the natural
behaviour is, then it is profitable.
Is it 7 to 1? Because I'm asking you to do a
little bit more. And because you're doing a
little bit more, you're buying alot more products at the end of
(10:21):
the week, at the end of the month, or should I ask you a lot
more from you? If I ask you a lot more than the
ROI will be 20 to 1. But The thing is on the long
run, you will be discouraged to participate in this and you will
say, well, that's too hard for me.
I'm not participating and not engaging in this anymore.
So it's, it's your fine tuning of finding the right parameters
(10:42):
on each offer to make sure that we keep a very good level of
engagement from customers on thelong run.
The customer lifetime value is avery important concept because
we don't want to have customers for one week and see them
disappear after that. We want to keep them on the long
run. So it's asking customers to do a
little bit more, but even if we're asking them to do a little
(11:04):
bit more, that will bring the full basket on average at the
end of the month and that is the7th 1:00.
I could make a comment, but I don't want to.
Now make makes sense, I'm going to flip this question around.
What is the ideal customer profile?
Then for you, what when we are really, really efficient is for
(11:25):
every retailer where you have a lot of customers, scaled is
always a challenge for them where promotion and loyalty are
playing a role, an important role in the business.
And when there's hard competition because at the end
of the gain, what you want to isto gain some market share from
your competitors. So when you took all these
elements into account, grocery is perfect for us because you
(11:48):
have frequency, you have a lot of customers, you have scales
and you have competition and promotion is playing an
important role. Pharmacy, health and beauty
retailers also completely core for us.
And then all the enterprise retailers, I would say once
again, we have frequency and competition.
(12:08):
Okay, all right, I'm, I'm okay, pharmacy, can you just expand on
that, 'cause that's a how, what does that look like as a use
case? Because I'm, I'm thinking like,
are you meant like pharmacy likein Boots, as in like the whole
beauty suite? What is that?
Not the the the medicinal part where you you get you you like
(12:31):
Nelson, you see Well, it does prolative school where you want
to buy your shrimp food, your vitamins, your your OK, your
space, all these. Followed in a barrel someone is
a great example. Exactly.
Exactly. OK.
Got you. So when you, when you look at
the future of innovation and youlook at for example, AI, dynamic
(12:53):
driven pricing, supply chain optimization, real time
individual engagement, what do you believe will have the
greatest impact on a retailer's strategy from your experience?
Because sometimes I think they think one thing, but you
probably see another. What I'm just curious on your.
Thoughts Innovation is very important and I think we're just
(13:16):
at the beginning of what AI can bring.
We, we, we do a lot of predictive AI, being able to
predict what the customer behaviour will be in the future.
If you combine this with agenticAI, it will be our with the
interaction of the customers andhow can we create and Gen AI on
top of this, you can create individualised personalised
(13:36):
content for them that will bringto, to to something that is very
specific, very unique, very innovative for the customers.
And obviously we're looking at this on a very regular basis.
All retailers should, should remain in contact of all these
innovation because this is just the beginning of it.
But to answer your question, I would say that what is very,
very important for for for retailers is not to look for
(14:00):
what will become the next great innovation is how can we make
sure that we're not missing the train that is right now in the
station. Yeah.
How can we make sure that even if it doesn't look that cool and
fancy, we hop on this train Because if we miss this train,
if we don't put AI and and more personalization and more
(14:21):
engagement from our customers right now, we might not be able
to do what will be super cool ina few years.
And, and, and I think that's, that's one of the biggest thing
that I'm sharing with my clientsis don't think necessarily too
big, too cool, too, too innovative, but make sure that
you're using what is already available that you can launch
(14:42):
within a few weeks. Make it a reality in your stores
because it will start the whole journey of change and you will
be able to, to, to reach the next innovation more easy.
I see what I mean. Yeah, Yeah, absolutely.
Final question, what's the one question I should have asked you
that I didn't? I like the question you asked
(15:03):
before when you said why are youall over the market.
That seems to be pretty simple and that that that's, that's a
very good question. I don't know.
You know, it's OK, you know, I mean, that means the other
questions did their job. But I'm just because I'm not an
what I'm always conscious of, although I've worked in data
technology, AI for probably longer than a lot of retailers,
(15:27):
I'm still conscious of the fact that I may not be the expert in
the room or in the field. And so therefore my approach may
lead to a bias in in in part of my thinking.
And hence why I always have thissort of question to sort of say,
have I actually asked the right question or am I?
Or am I, you know, reading the headlines, going down rabbit
(15:47):
holes and there's actually something else I need to be
thinking about. And so that's it, I mean.
But to, to continue on this to, to follow up on this, because,
because I think that was the right question.
And the, and the answer is I don't know why we're not all
over the market. I mean, I just, I think, I
think, I know, I think exactly for the reason that I described,
(16:07):
it's sometimes difficult for retailers to hop on the train
that is passing. What we're doing with the
personalised changes, the personalised promotion, the
personalised ranking of, of massoffers that we're doing for, for
you, it seems obvious, it seems like a no brainer, but sometimes
it's difficult to act on it. And, and I'm saying to my
(16:28):
retailers, you could do this in five weeks.
Yeah, but we're trying to build this on our own.
We have a whole road map. We'll be there in two or three
years. And we all know that in two or
three years, the road map will change, the priorities will be
different and you will be in a different situation.
And I think that's, that's the main difficulty is people are
(16:48):
looking too far ahead when, whenthis should be more operational,
more pragmatic. Let's do it right now.
Prove me that your solution is actually delivering A7 to one
ROI. Let's fine tune the whole thing
together and we'll talk about itin a few months, in a few weeks,
not in a few years. And that's, that's that's where
I think the retailers should sometimes be more action
(17:10):
oriented. I look this, we can talk about
this for a long time and I have to caveat because obviously I
know what you what it says on the tin of what you do and what
you've said in this podcast. But if you do and say what you
can do for retailers that it's signal based, SAS based, it's
like you've taken away all of the problems, all the reasons
(17:33):
why a retailer traditionally in technology would say no.
But it it could also be that thethe guys in tech aren't the guys
in merchandising, aren't the guys in product.
And it's sometimes the the people in the business that need
to get behind the AI and you know, again, but that's a that's
a whole nother conversation intoget get going through retail.
(17:53):
It's Cedric. Thank you so much for giving up
your time. And will you be at any other
shows before the end of the yearor you NRF is the next one for
you? Actually flying to Las Vegas
next week for grocery shopping. Oh, nice.
APA is in Vegas. It's a great event.
It's a fantastic, it's a fantastic event.
We have tech for retail in Parisin November.
OK. And NRF in in general.
(18:16):
Fantastic. So I will probably see you at
NRF. Absolute view pleasure.
Thank you. Thank you, Alex.
And I think what is very, very important in what we're doing is
the core philosophy of what we're doing is remaining
customer centric. What is really interesting for
customers, how will they naturally behave?
Because we're not here to force them to change their behaviour,
We're here to incentivize them to do a little bit more of what
they're already doing. Hello and welcome back to
(00:33):
another special episode of the Retail Podcast.
Now Eagle Eye, who will meet oneof the cofounders in a minute.
Cedric, I've seen all over the place.
I've seen them for years at NRF,I've seen the literature and I
also we have a shared customer that we have both worked on.
And so when I have the opportunity to interview Cedric,
I thought what a great opportunity to interview someone
(00:54):
that I pretty much can bet my bottom dollar that the problem
they solve is a massive problem for retailers right now.
So Cedric, why don't we just start with you?
I know you're the Co founder andobviously managing director of
Eagle Eye, but one, just tell usa little bit about yourself and
then tell us a little bit about Eagle Eyes to set the frame for
the conversation. Yeah, absolutely.
(01:16):
So hi, thank you very much for having me today.
I'm Cedric Chaho, I'm French, I live in in France and I'm the Co
founder of Eagle AI, which is one of the department of Eagle
Eye. What we do at Eagle Eye globally
is to help retailers to scale personalization and to make it
(01:36):
really efficient for them, for the customers and also for the
suppliers who are usually participating in all this.
And that's really the mission that we have.
How can we make it easy and effective for retailers to
operate personalization once again at a very, very large
scale. That's I don't.
You have, can you do when we talk about personalization, can
(01:59):
you frame that for us? Because sometimes people use
personalization for so many different things.
What? What is that?
If if we're just going to take another layer down and say what
personalization means to you, aneagle eye, Well, how would you
describe that? It, it, it's a very good comment
and I think you're absolutely right.
Personalization sometimes for some people means being able to
put the first name of someone inon top of an e-mail.
(02:23):
That's that's it. It's but it's not really
effective. What we mean by personalization,
eagle Eye is to be able to define the right parameters of
an offer to make it really efficient for me.
For instance, which brand, whichproduct are the most relevant
for me as a customer, probably product that are different than
(02:45):
than your favourite brands. So we all have very specific
product and, and if even if we have the same product, maybe we
can adapt the threshold that I need to reach to get a reward.
And that's what we mean by personalization, is being able
to identify, predict the naturalbehaviour of each customer,
(03:07):
define the potential of each customer to be able to really
define the threshold that each customer individually need to
reach to get a specific reward. And even the reward can be
personalised because we all havea promotional sensitivity that
is a bit different. So if we can identify all these
elements, at the end you have, you could receive an offer that
(03:29):
is completely unique that no other customer will receive,
completely adapted to your need,your affinities with the
different brand. And that's what we mean by
personalization. OK, I got you.
How does gamification fit in into that?
Because again, sometimes I feel that's misleading as a as a
subject in terms of people talk about.
Yeah, tell, tell me what? Yeah, I, I, I your perspective
(03:51):
on. That yeah, absolutely.
I think we never design the solution that we worked on as a
gamification platform, but the gamification came on top of it
and it was a little bit of a surprise.
Like the cherry on the cake, it was designed to be
personalization. The idea was to for each
customer to receive different offers with threshold to reach,
and if I reach this threshold, Iwill get a reward.
(04:14):
By definition, it's a little bitgamified.
It's not designed to be just fun.
It's designed to incentivize me to be a little more loyal to
that brand of that retailers. So there's there's a nudge,
there's an incentive here. So therefore it's a little bit
little bit gamified, but it's not fun per SE.
I would say it's not a pure gamification, yeah.
(04:35):
OK. Thank you.
You're right, I think it's, it'sfar the solution.
It's part of the solution and it's it's especially in grocery
retail, it's important to keep in mind that what is important
is for customers to be able to earn something and to earn a
reward. Having fun, playing a little
game is always nice, but in grocery report, it's not the
main mission of the of the of the retailer.
(04:56):
The main mission of the retaileris to provide something that is
clearly bringing value to the customer at the end.
How does when, if we stick with grocery or or or maybe not?
We can however you want to find answer the question.
But can you share like a pragmatic approach to AI where a
sort of simple, well defined usecase has outperformed big flashy
(05:21):
AI initiatives which you you seeacross across the industry?
I think that the multiple usage of of AIS is something very
pragmatic, probably the most basic for me, but it but it
everything is in the detail. The most basic is being able to
identify the five most importantitems for each customer.
(05:42):
When the retailer is sending an e-mail to to to an e-mail or
notification to their customers.I want to re interest all my
customers with the five best offers we have installed this
week. Just being able to do this is
difficult because you need to understand the affinities
between the products and the customers.
You also need to look at the data.
(06:04):
If I bought my laundry detergentlast week, there's no need for
you to put that on top of my list because I will not buy it
again. I will probably buy it again in
a few weeks, but not tomorrow. If you have products that are,
that have a facial value that ismore important than others,
maybe they should rank higher in, in, in the, in, in the
e-mail. So all these elements, if you
(06:25):
use AI, you're able to combine all these KPI's to be able to
really offer the best, bless you, the best 5 offers for each
customer. So it's a simple, very simple
use case. But to answer it very
efficiently you you need the power of AI to to to accomplish
(06:46):
this. And so I don't want to put words
in your mouth, but if I understand what you're saying to
me is that you look at all the signals post purchase to make
sure that communication is in line with those signals, which
is the example that you gave. If I bought detergent, don't
send me another offer on detergent to five, sorry to Five
(07:06):
products or Five Points. If you send the communication to
a customer and you say come to astore next week because we have
a lot of offers. Among all the offers we have
here are 5 that are really interesting for you collect the
best of I for the customer. We look in the past, We look at
what's happened in the past. What are the signals of a
female? And that's what Eagle I are
(07:27):
doing. Absolutely perfect.
That's OK. You're doing understood.
We're identifying which product you bought in the past to
predict which product you will probably buy in the future you
provide. That as a service or I guess is
it a? Solution.
It's a SAS solution and all the algorithm that we've built over
the years, we encapsulate them into different use cases just to
(07:50):
make it very pragmatic, very plug and play and easy to use.
We received the customer data, what happened over the last 12
months. We looked at all the interaction
between products, customers, stores and everything and we
predict what will happen in the future.
And we look at the potential of each customer if they did this
three months ago, how will they behave in three months.
(08:12):
And based on this, we are able to identify which products,
brands, categories are the most relevant to them.
And I think what is very, very important, what we're doing is
the core philosophy of what we're doing is remaining
customer centric. What are what is really
interesting for customers, how will they naturally behave?
Because we not here to force them to change their behaviour,
(08:36):
we're here to incentivize them to do a little bit more of what
they're already doing. I'm not asking customers to
switch from brand A to brand B. I'm here to tell him if you're
more loyal then you should be rewarded for that.
Yeah, OK. I don't know why you've not
taken over the industry because I mean that that sounds like the
(08:56):
ideal thing when when, when, when I talk to retailers and the
problems they face. But one of the things I read
somewhere in your literature, I don't know if it's a promise or
an example, but you look at sortof like a A7 to one return on
investment, which if true is remarkable by any standard,
which I sort of understand from the way that you've just
(09:17):
described it. I was wondering what, what are
the hidden mechanics behind performance?
And then what misconceptions do retailers have about achieving
this return? I can be even more bold.
It's old here if you say it. Oh, there.
OK, go for it. The the the 7 to 1 can be 12 to
(09:38):
one. It could be a 20 to 1 because
everything is in the way we build their parameters of the
offer. The seven to one is based on the
best practise. Once again, the idea is to
create more loyalty from customers.
So I'm going to incentivize you,Alex, with some offers on your
favourite brands and I'm going to ask you to spend a little bit
(10:00):
more on your favourite brand at this specific store.
And if you do a little bit more than you will get a reward.
Yeah, because it is incremental by design.
I'm asking you to do a little bit more than what the natural
behaviour is, then it is profitable.
Is it 7 to 1? Because I'm asking you to do a
little bit more. And because you're doing a
little bit more, you're buying alot more products at the end of
(10:21):
the week, at the end of the month, or should I ask you a lot
more from you? If I ask you a lot more than the
ROI will be 20 to 1. But The thing is on the long
run, you will be discouraged to participate in this and you will
say, well, that's too hard for me.
I'm not participating and not engaging in this anymore.
So it's, it's your fine tuning of finding the right parameters
(10:42):
on each offer to make sure that we keep a very good level of
engagement from customers on thelong run.
The customer lifetime value is avery important concept because
we don't want to have customers for one week and see them
disappear after that. We want to keep them on the long
run. So it's asking customers to do a
little bit more, but even if we're asking them to do a little
(11:04):
bit more, that will bring the full basket on average at the
end of the month and that is the7th 1:00.
I could make a comment, but I don't want to.
Now make makes sense, I'm going to flip this question around.
What is the ideal customer profile?
Then for you, what when we are really, really efficient is for
(11:25):
every retailer where you have a lot of customers, scaled is
always a challenge for them where promotion and loyalty are
playing a role, an important role in the business.
And when there's hard competition because at the end
of the gain, what you want to isto gain some market share from
your competitors. So when you took all these
elements into account, grocery is perfect for us because you
(11:48):
have frequency, you have a lot of customers, you have scales
and you have competition and promotion is playing an
important role. Pharmacy, health and beauty
retailers also completely core for us.
And then all the enterprise retailers, I would say once
again, we have frequency and competition.
(12:08):
Okay, all right, I'm, I'm okay, pharmacy, can you just expand on
that, 'cause that's a how, what does that look like as a use
case? Because I'm, I'm thinking like,
are you meant like pharmacy likein Boots, as in like the whole
beauty suite? What is that?
Not the the the medicinal part where you you get you you like
(12:31):
Nelson, you see Well, it does prolative school where you want
to buy your shrimp food, your vitamins, your your OK, your
space, all these. Followed in a barrel someone is
a great example. Exactly.
Exactly. OK.
Got you. So when you, when you look at
the future of innovation and youlook at for example, AI, dynamic
(12:53):
driven pricing, supply chain optimization, real time
individual engagement, what do you believe will have the
greatest impact on a retailer's strategy from your experience?
Because sometimes I think they think one thing, but you
probably see another. What I'm just curious on your.
Thoughts Innovation is very important and I think we're just
(13:16):
at the beginning of what AI can bring.
We, we, we do a lot of predictive AI, being able to
predict what the customer behaviour will be in the future.
If you combine this with agenticAI, it will be our with the
interaction of the customers andhow can we create and Gen AI on
top of this, you can create individualised personalised
(13:36):
content for them that will bringto, to to something that is very
specific, very unique, very innovative for the customers.
And obviously we're looking at this on a very regular basis.
All retailers should, should remain in contact of all these
innovation because this is just the beginning of it.
But to answer your question, I would say that what is very,
very important for for for retailers is not to look for
(14:00):
what will become the next great innovation is how can we make
sure that we're not missing the train that is right now in the
station. Yeah.
How can we make sure that even if it doesn't look that cool and
fancy, we hop on this train Because if we miss this train,
if we don't put AI and and more personalization and more
(14:21):
engagement from our customers right now, we might not be able
to do what will be super cool ina few years.
And, and, and I think that's, that's one of the biggest thing
that I'm sharing with my clientsis don't think necessarily too
big, too cool, too, too innovative, but make sure that
you're using what is already available that you can launch
(14:42):
within a few weeks. Make it a reality in your stores
because it will start the whole journey of change and you will
be able to, to, to reach the next innovation more easy.
I see what I mean. Yeah, Yeah, absolutely.
Final question, what's the one question I should have asked you
that I didn't? I like the question you asked
(15:03):
before when you said why are youall over the market.
That seems to be pretty simple and that that that's, that's a
very good question. I don't know.
You know, it's OK, you know, I mean, that means the other
questions did their job. But I'm just because I'm not an
what I'm always conscious of, although I've worked in data
technology, AI for probably longer than a lot of retailers,
(15:27):
I'm still conscious of the fact that I may not be the expert in
the room or in the field. And so therefore my approach may
lead to a bias in in in part of my thinking.
And hence why I always have thissort of question to sort of say,
have I actually asked the right question or am I?
Or am I, you know, reading the headlines, going down rabbit
(15:47):
holes and there's actually something else I need to be
thinking about. And so that's it, I mean.
But to, to continue on this to, to follow up on this, because,
because I think that was the right question.
And the, and the answer is I don't know why we're not all
over the market. I mean, I just, I think, I
think, I know, I think exactly for the reason that I described,
(16:07):
it's sometimes difficult for retailers to hop on the train
that is passing. What we're doing with the
personalised changes, the personalised promotion, the
personalised ranking of, of massoffers that we're doing for, for
you, it seems obvious, it seems like a no brainer, but sometimes
it's difficult to act on it. And, and I'm saying to my
(16:28):
retailers, you could do this in five weeks.
Yeah, but we're trying to build this on our own.
We have a whole road map. We'll be there in two or three
years. And we all know that in two or
three years, the road map will change, the priorities will be
different and you will be in a different situation.
And I think that's, that's the main difficulty is people are
(16:48):
looking too far ahead when, whenthis should be more operational,
more pragmatic. Let's do it right now.
Prove me that your solution is actually delivering A7 to one
ROI. Let's fine tune the whole thing
together and we'll talk about itin a few months, in a few weeks,
not in a few years. And that's, that's that's where
I think the retailers should sometimes be more action
(17:10):
oriented. I look this, we can talk about
this for a long time and I have to caveat because obviously I
know what you what it says on the tin of what you do and what
you've said in this podcast. But if you do and say what you
can do for retailers that it's signal based, SAS based, it's
like you've taken away all of the problems, all the reasons
(17:33):
why a retailer traditionally in technology would say no.
But it it could also be that thethe guys in tech aren't the guys
in merchandising, aren't the guys in product.
And it's sometimes the the people in the business that need
to get behind the AI and you know, again, but that's a that's
a whole nother conversation intoget get going through retail.
(17:53):
It's Cedric. Thank you so much for giving up
your time. And will you be at any other
shows before the end of the yearor you NRF is the next one for
you? Actually flying to Las Vegas
next week for grocery shopping. Oh, nice.
APA is in Vegas. It's a great event.
It's a fantastic, it's a fantastic event.
We have tech for retail in Parisin November.
OK. And NRF in in general.
(18:16):
Fantastic. So I will probably see you at
NRF. Absolute view pleasure.
Thank you. Thank you, Alex.
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