Hello and welcome to our post NRF Interviews.
So let me introduce Paul Windsor who is Head of Retail and
Industry at Snowflake. So Paul, welcome to the podcast.
For those who don't know, why don't you tell us a little bit
about yourself and then a little bit about the company that you
work for, Snowflake and and what they.
Do, yeah. Thank you, Alex, and thanks for
having me today. Yes.
So a bit of background about myself.
My career in retail can be split split perfectly down the middle.
I spent the 1st 19 years of my career at Sainsbury's, where I
think in those nineteen years I've got an incredible
understanding of the retail business and it was a great 19
years in the company. But my last job I had at
Sainsbury's, My last role was all about the data component and
how data is shared and collaborated on with large CPG
companies. Now that may into my next 19
years of my career, which is always it's been in the data and
analytics space. So I spent 19 years pretty much
working for companies that are helping retailers solve their
data challenges, whether that was with data warehousing in the
kind of first generation of when data warehouses became something
that retailers invested in. I've spent a number of years in
the whole BI space and then recently in the AI space around
automated machine learning. And two years ago I got the
opportunity to join Snowflake, which is the leading data
platform that was born in the cloud and snowflakes.
Mission today is to mobilise the world's data.
So this opportunity to give access to retailers for the data
they need with the data consumers that need to consume
that data in a way that makes it easy and and approachable in the
way that they they tackle their their data challenges.
So, so, yeah, so been with Snowflake for just over 2 years.
I lead our industry go to market for Snowflake in EMEA.
We have a number of retail customers today that are
publicly, publicly I can talk about it's using Snowflake.
One of them happens to be, if I can point to it correctly Alex,
Under Armour which are obviously I've got my Under Armour and
Snake like talk on today, that's all about how they use Snowflake
from their supply chain perspective.
And then my old, my old company that I work for, Sainsbury's is
also a customer of Snowflake as well as John Lewis and River
Island if we kind of localise this down to kind of the UK
market. So that's about me and a little
bit about about Snowflake, but I'd love to get on to the
conversation of NRS as well. Yeah so I just want to this is
in the spirit of you know what my audience is thinking cause
every talk at NRF that had anything with AI in in the title
irrespective of what technology, vendor or partner it was from.
You can imagine all or all of the the the major players had AI
in their title. Everyone you know everyone was
super interested. So I just want to be, you know,
cut to the chase and in in my mind and I'd love your opinion
on this, how much of AI that we talk about is true AI as opposed
to machine learning plus, right. So for those who are data
specialists, they'll be like well that's just that's machine
learning. We've been doing that for years.
That has nothing to do with AI or generative AI.
And and I'm just thinking from a from a strategy perspective
before we sort of sort of go deeper down and down.
If we just keep it at that high level, what are your thoughts in
actual AI outcomes? And if you want, why don't you
define what AI means to you and Snowflake And then we can sort
of distinguish because that was the biggest frustrating thing
for me that people would just like talking about machine
learning as it was a I and hoping that no one would ask
that question and like hang on a minute, that's just machine
learning. That's like something that you
could put in an Excel spreadsheet and extract better
outcomes from. So go, go ahead, tell me.
Well, I mean it's it's it's a great point to kick off with,
right? So without a doubt, those three
days in NRS, the number one hype and the topic and the theme was
Jenna in large language models without a doubt.
But I agree with you, not many of those people that were in
those speaking sessions went much deeper than just sort of
reflecting on the fact that this is something that they'll be
looking to try and experiment with.
Yeah, from snowflakes perspective and perhaps it's
sometimes does get missed off here is that you can't have an
AI strategy without a data strategy.
Absolutely. Now, sometimes this gets lost.
Now this is something that we're trying to kind of.
Interrupt you there, Yeah. Is that is the reason because
there's a separation between the two?
Because the people talking about the subject are not from a day
like yourself? From a data background is what?
What's the reason for the disconnect between the two?
Well, I think, I think we've now come into a new era of how AI
can be used because generative AI is something that can now be
used. Large language models is
something that can be used and deployed.
But if you think about it, it can't be used without the data.
So if you don't have a data strategy, and by a data strategy
we mean you to be able to truly get the best value out of
building a Gen AI model or a large language model, you are
going to need to have access to the data that you need to build
that model. Now this has been a challenge
for a number of companies for years.
This is how Snowflake has grown so fast as a business is that
lots of data sits in multiple silos.
You can't just connect a large language model when data sits in
multiple silos. You need to have that data
unified. You need to have that data in
one place. You need to have it governed.
And also Alex, what's really important here is you need to
have the kind of framework behind it so that when you start
to build these large language models, it's contained inside
your own platform for security purposes.
But the first thing that any company needs to do when we
start to hear from our customers, they're teens hear
about Jenna and large language models.
We always ask the question, you know where are you in terms of
your data strategy? Do you have the data today ready
to start to take advantage of those large language models in
general, I and I don't believe I heard that in NRF.
I heard exciting. I heard market signals increase
the efficiency of the supply chain, but it didn't hear
anything about the fact that first and foremost you need to
have a really good unified data strategy.
So I'm I'm not going crazy thinking that, right?
Why not at all? That that is like there would
cause, I was thinking but just maybe there's an an NLM that
you've not thought about Ali that's actually magically
presenting the data into these platforms.
No, because all the platform providers that's their, you know
come come with us on the journey and we'll take care of.
I'm thinking if your data is not ready and as we know most of the
industry, yeah is not ready because everything sits in
silos. So let let's just talk about the
the, if you like the silos and breaking that down as well.
So does does my finance data need to sit?
Like how do I manage that? My finance take the market
marketing data? My ERP data?
Where? Where do I start with, with my
silos? Within my business or my systems
to be fair? Yeah.
Well, this is one of the, this is one of the key strategies
that Snowflake is helping to solve for.
Our retail customers of course have the source systems where
the data actually is generated and curated, but that's not
where you would take advantage of building large language
models, Gen AI, machine learning models, analysing that data to
understand business performance, looking at past historical sales
etcetera. We all know that that's just
where the data is sourced and generated from.
You need to bring that into a platform which is where the data
can be consumed. So where Snowflakes been helping
retailers for a number of years is to bring exactly those
functional data sets that you just mentioned here.
You need to have your finance data alongside your marketing
data alongside your supply chain data.
If you're thinking about running a campaign that is a promotional
campaign to your consumers, you need to understand their
behaviour. So that's all of the customer
data residing in the same platform as your inventory data.
Because if you're going to be promoting stock, you need to
understand if you actually have the stock available to actually
promote those brands. And you need to have your
finance data available to understand the costs of running
these promotions. So bringing all of that data
together is an absolute necessity and this is what we've
been helping our retail customers in the first instance.
Now Gen AI and large language models is just taking that onto
another level. But again, this is just another
capability to start driving those outcomes and those
predictions. But the underlying part, which
unfortunately is not the interesting exciting parts of
many people, Alex, is you need to have that governed unified
data platform way you can consume that data from.
And then I'll go one step further as well, which is
something that many retailers are not necessarily truly taking
advantage of to a wide degree is in order to kind of build real
good predictions on future behaviour or detections of
market signals, you need to access third party data as well.
So think about all those third party datasets, Alex, that some
of the biggest data providers in the world are monetizing today.
Now everybody draws their attention straight away to
weather data because it's been something that's been talked
about and the retail industry for 20 years.
But we're also talking about demographic data, economic data,
ESG data. We've got all of these types of
datasets that could also be enriched alongside your own data
to then run those kind of AI and those predictions as well.
So you need access to that third party data.
Now another little tiny plug for Snowflake here Alex, it's it's
Snowflake has the largest marketplace on its platform, the
third party datasets. It has over 2200 third party
datasets sitting on the Snowflake platform.
So if you are a Snowflake customer and you'll you'll find
all of your own first party and all your own data unified
together to start to really understand your business.
But you feel that there might be a competitive advantage to
access third party data. That data is available to you as
well as part of the platform for you to enrich it.
Bring that together and now you're really scaling the way
that you can do predictions on things like AI.
So if we take sort of the data economy in action and from your
experience from whichever you know whether it's Sainsbury's
Underarm or one of the the retailers that you, you you you
mentioned and understanding that they must have had some data
strategies that you know they they already set and and now
leveraging your platform. Can you share any sort of I I
don't know if any of them were in customer data or the loyalty
programme area but but I'm what I'm curious is what the use case
is and then what the intelligence is that's being
drawn out to make actionable business outcome.
So can you have you got any examples?
For us, yeah, I do. So I I think if you look at
snowflakes, opportunity to drive success for our retail customers
today, the typical conversations that we're having, So we've had
those data conversations. You need to unify your data, you
need to bring it together, you need to be able to give it
seamless access to that data of all your data consumers.
That's the first part of a road map of a data strategy journey,
bring that data together. We are now having conversations
with our retail customers today which is around surprise Alex,
this is, this is no kind of surprise for you.
And also what is now a hot theme as well, Customer 360 does not
go away and it continues and it will never be sold.
Customer 360 will never be sold. We know that.
But it's about now this opportunity to bring all your
data together from a customer perspective.
I don't know if you picked up on this at NRF, but one of the
sessions I think I went to and I think it was for an Abercrombie
and Fitch I think was speaking at that intersection and they
were talking about well moving now from personalization to
individualization. So this idea now the
personalization is we want to personalise as much as we can
our relationship with the consumers.
Now we're getting down to that level of granularity with the
data. The data is all unified
together. You've got the capabilities with
modelling in AI and this potential around large language
models. Can we get to the point where we
can start to individualise those relationships with our customers
as well? And you can see that happening
just here with Sainsbury's. Sainsbury's are now offering ten
products every seven days to their Nectar card members that
is absolutely personalised and individualised to those shopping
behaviour. We'll be able to bring that data
together. If you're a network card holder
today, every Monday you'll get those and new offers that are
absolutely individualised and personalised to you to be able
to do that. That's really understanding
previous customer shopping behaviour and doing that really,
really well. And you can see from their
results and they talk very positively about how Nectar
pricing is driving revenue from that sector.
It's a customer. 360 is a big, big factor.
Yeah. I've got, I've got another two
I'd love to just touch on. I'll be the second one is no
surprise again is that supply chain.
So you know Under Armour talk publicly about the fact that
they use Snowflake for the importance around sharing data,
Alex, to make the supply chain more efficient.
We know that there are so many situations in the supply chain
where that flow of goods can break down.
The idea that you can support that with data sharing is
really, really important. And where Snowflake does this
exceptionally well is we can help to share data between two
companies without moving or copying the data.
So this is a really good example where in supply chain you need
to share data with sales of inventory of demand and what you
don't want to do is turn that into a supply chain of data
having to move Where Snowflake does this exceptionally well is
around that sharing of data between two companies.
So that's supply chain is another key strategy for a lot
of our retail customers. And then the final one that I
want to sort of touch on is I I don't know if you heard it as
well, but retail media, I mean retail media is where the net I.
Was just about to that was gonna be my final question for you
because later media is absolutely how do I combat the
battering I'm getting from inflation by generating new
revenue from other means. So yeah, absolutely far away on.
That well, well look you know I, I again I captured a tonne of
notes here from last week. This was US numbers, Alex, but
for those listening in today, you've got Retail Media
generating $60 billion of revenue in 2023.
That's expected to increase to $100 billion in 2028.
So four years away from that increasing to $100 billion,
They're talking again very, very positively NRF about the next
era of we're in the golden generation of retail media.
And this is because now we've got this opportunity physically
in store to start generating those advertising and those
promotions through those digital screens that you can use and
place around the store. And then you've got that
absolute fantastic instant response to whether or not that
advert, that advertising and that product has resulted in the
product being purchased and then the closing that loop to how
much was that return on investment for that advertising.
So we're seeing a lot of our retail customers now wanting to
get really, really solid on their first party customer data
because they can use that to really decide which is the best
times to promote which items through their stores.
And then the other part of retail media Alex, which is
again where Snowflake really comes in is we're now seeing
this growth of off site media advertising.
And again the numbers, the numbers were staggering.
So again at NRS last week they talked about currently offsite
media revenue, it's about $6.7 billion in 2023.
That's taking what you can advertise of goods into
advertising and media channels which is away from your on site
channels. That growth is set to go from
6.7 to 24 billion in the next three years and that's because
you can take that data now and you can start to monetize it
through off-site channels. But it's Paul, just interrupt
you on that. But I see that as so I get the
outcome that if you're delivering adverts in store then
delivering those adverts off site somewhere else.
Yeah. But again because I've sort of
grown up in this industry you know the martech that that you
need to to to develop something like that, I don't know, I don't
I don't see it as easy as so. So the I guess the possibility
and the numbers are there to say look if you can do it, it is but
I don't know if I took the UK grocer market bar two of them, I
don't think the others could do that, right.
I don't know if you've got experience in the UK market and
obviously one of the grocers you you're working with.
Yeah. But I don't see how they could
because obviously then you're you're you you're just looking
at the integration with sort of expanding that problem of data
sets out across other other assets that might be owned by
other people. Well, it's interesting the
integration passed the really important words that you just
reflected on here. So of course the grocers are
leading the charge in terms of the way that they can do this.
They can take that marketing budget and they can invest that
in offside channels. This is where the risk is at
right now is obviously protecting personal identifiable
information is EI customer data, your data and my data.
And what you don't want to do is once you've brought that
together inside a data platform like Snowflake, and you've
actually governed it and you've got it secure and now you're
protecting that data and you're using it for your own insights
and your understanding of your customers and your business.
And now you're thinking about a media strategy where data needs
to actually be shared outside of your platform and with
advertisers and media agencies, et cetera.
Now we're into the real era of data cleanrooms.
Now this is where the conversation may get a little
bit more too technical, but for those listening in today, data
clean rooms is a fantastic way of being able to share data but
not expose the data. It's a beautiful way of sharing
data and insights without moving the data.
And so we're starting to see capability baked into platforms
like Snowflake to allow you to be able to share data without
moving or copying or exposing it.
And I think that's where we're going to see the platform
capability enabling that growth of that retail media.
So that advertising can happen off site, but the data stays
governed and secure. So that's where we're, we're
talking to most of our customers today.
Brilliant, Paul. This was going to be a quick
fireside chat, which I think has given the audience a lot of food
for thought. Any sort of where do you, where
do you think will be at next year's NRF?
Well, because it's like almost. Do you have any views of where
this is all going? Will it be AI .2 dot O or
something? I thought you might ask this
question so, so when I when I went 12, I went 12 months ago.
I think you were saying at the start before we started this
podcast. You've been for the last 13
years. Last year I took away three key
themes. The first one was how do we, how
do we help serve the consumers that are dealing with the cost
of living crisis and inflationary pressures.
So it was, it was very much about dealing with with those
kind of consumers. The second one was how do we
move from promising to proving that we are delivering an ESG
sustainability strategy. And the third one was how do we
optimise our assortment and our prices again connected to the
cost of living crisis that was very much in focus 12 months
ago. That felt quite a challenging
time and very much in media. If you look today, the three key
themes was Gen AI, it was retail media.
And the third one was where did I get the third one, please
don't let me go. Yeah.
The third one is connected consumer, Alex, with the three
that were absolutely focus this time that felt more optimistic.
If you asked me in 12 months time, I think we may be at the
point where the NRF is going to be asking retailers to prove out
how they've adopted Jenai large language models, how they've
been able to get to the point of individualising those
relationships with customers. I think we're going to start to
see more proof now in just concept talking.
No, that's wonderful. Paul, thank you so much for
giving up your time in the day and I look forward to seeing
what you guys carry on doing to support retailers go through
this journey of transformation. Well, listen, I'm really
appreciate you inviting me on here, Alex.
It's been a pleasure and great. Thanks so much.
Thank you.