Hi, I'm Colin Wright. We're out here at Sapphire, very much from an industry perspective I'm part of the
worldwide retail and consumer goods teams and we're here talking about pain points that our industry has.
In fact, there's a lot of data that comes in from a lot of
different places; but for our consumer goods companies,
the largest, some of the largest in the world, they need to know where the products
are selling in what retail stores and they need to know that every single day.
Some of the challenges that we've had along that journey is getting
that data back from the retailers and into their systems.
And one of the most, I think, important use cases that our CPG companies go through is how do they combine
that data with their SAP data where they can combine what their products cost with how many have sold.
So these are important everyday data problems that these organizations are running their business with.
And I'm here today with Andre Furman who is a thought leader and heads up a think
tank that works with some of these largest companies that really get their most out
of the SAP data and their sell-through data that they get from their retailers.
So tell us a little bit about, obviously tell us a little bit
about the problems you're seeing these big CPGs are having.
I head up a think tank called Ibins and we're helping companies figure out how to use Microsoft analytics
and Jumpstart efforts using cloud and figuring out how to solve business problems using Microsoft.
And so some of the important use cases that the customers have been wanting to do,
tell us about promotional effectiveness and sell-through statistics trend analysis,
the really important things for them to run their business on.
Well, some of the things we're doing is, as Colin mentioned,
companies go and source this sell-through data by store,
by SKU, by day. And there are very high volumes of data. And now that we have all this raw data,
we're trying to figure out, okay, is our strategy with promotion,
is it valid or not. In order for us to understand that,
we need to take that data and also mash it with SAP data. We need to understand what are the margins,
what does the customer look like, what's selling, what's
not selling. So we're able to take some of the COPA data,
some of the orders data, combine it with promotions data, provide it with consumption data,
mash it all together. You're looking at millions, tens of millions, hundreds of millions of records.
And we're able to train this and understand by SKU, by store, by geography what's
selling and what's not selling and which promotions are effective and which are not.
Of course, the reason we can start to do all this now is with the scalability of the cloud.
I mean, we're seeing a transformation of SAP into the cloud
but we have to also transform those analytical workloads,
those reports, that business intelligence for the customers. What sort of improvements have you
seen from being able to get that into a data lake in the cloud and really scale out those reports?
Well, there's several things. Number one is ability. So some of the
customers have only been able to look at things on a consolidated level.
So then they weren't able to look at things by store, by date, by
state even. They were only looking at the entire book of business.
So now they're able to mash that data into demographics, mash that
data with individual geographies and get that fine level of,
grain level of control. So if something is selling in inner cities, may not be selling in suburban areas.
So they're trying to understand, okay, instead of having one strategy that
works for -- they're trying to make it work for any kind of customer,
now they're segmenting it for specific demographics.
This is a real step forward in customer personalization. And we know that all of our customers,
whether they're in the retail area or they're in the consumer goods area and selling to retailers,
they want to personalize things to customers and that's
where we see the future going. So thanks very much.