Kuber Sharma: So, help me understand what does demand
forecasting really truly mean for a retail customer?
ShiSh Shridhar: So, from a retail perspective, what most
retailers are looking at is they’re saying we want to be able
to better manage our costs. And when you have too much
product on the store floor that is not selling, you’re taking
up that space that could’ve been used by another product
that is in demand. Customers are coming in. They’re not
finding what they want. The floor space is taken up by
products that they don’t want. Now, how do we balance
that? How do we ensure that we have the right products
that you’re looking for and we don’t have too much of
what you’re not looking for? And that’s a fine balance. And
it changes; it’s dynamic because the demand for certain
products, the trends are changing. There are certain products
that will spike in demand at a certain point and then just go
down. There is seasonality associated with it. There is also
sometimes certain products will no longer be in demand.
How do you predict those things before they happen?
Before I send all the stock to the store, I want a better
predict to be able to send the right amount, where I don’t
have a situation where customers are not finding what
they’re looking for. That’s one driver really what retailers
are saying. We want to be able to get better at that. We
want to be able to do know what product you really like
and without you actually telling us. We want to analyze
your data, your past purchases, your correlations, and
infer from things like what movies you like, what books
you like, and all those kind of things that tells us what
service or product you could potentially like.
Sharma: Okay.
Shridhar: And that’s the area that a lot of retailers are
looking at and saying, if we can get better at that, we’re
going to improve the performance of the store. We’re
going to be able to reduce the customer churn and improve
retention and drive customer acquisition, all of those
additional benefits about that as well. So, that’s the area
that most retailers are really looking at.
Sharma: That sounds like an operations problem. But I can
imagine you can do better pricing with this based on if you
can predict the demand of a particular item, you can price
it better. What are the other use cases you’ve seen our
customers use?
Shridhar: So, in addition to the inventory and price, the
other areas that I’m seeing is really the assortment about
what are the sizes and colors and things like that? So,
that’s one aspect of it that’s extremely important. Using
machine learning and advanced analytics and data to
have the right supply chain in place as well, to ensure that
based on weather, based on current delivery times and
things like that, can I fine tune that so the deliveries are
just in time? So, that’s the other aspect a lot of retail is
looking at. Looking at also they’re using data for better
managing the workforce within the store driving customer
experience. They’re saying, if I can measure the traffic
in the store over time, combine that with weather and
events and all of that and be able to better predict what’s
going to be my traffic at a certain time, that enables
having the right products, having the right amount of
staff so that I don’t have a decline in customer experience
and all that. So, that’s the other aspect that they’re
looking at as well.
Sharma: So, if a retailer knows that they’ll get more people
on Tuesday afternoons, they can have an extra shift or
two, so to ensure that there are enough people to support
their customers’ needs.
Shridhar: Exactly. And those kind of forecasts, predictions
can actually get improved by pulling in event information.
For example, if there’s a football game and you know that
is a cause for traffic to decline, so I know I can better
manage that process and things like that too, yeah.
Sharma: Interesting. So, we know on Superbowl, we won’t
have too many people going to buy clothes.
Shridhar: Exactly. Exactly. So, those are kind of things
that you can better predict—Thanksgiving Day sales and all
those based on patterns to better predict that as well.
Sharma: This is fascinating. Can you talk a little bit about
some customer use cases that you’ve seen recently?
Shridhar: So, there’s a couple of customers. One of them
is Pier 1.
Sharma: Okay.
Shridhar: So, Pier 1, what they were trying to do is they
were wanting to improve the conversion rate in the stores
and they felt that the assortment that they had in the
store was not the right level. And one of the ways that
they addressed this to figure out what should be the right
assortment by store was to look at their online data. So,
they had pier1.com. They had their web logs that actually
had information about the customer behavior when they
went online. One is where did the customers come from?
Their location information was available to the weblog.
What did they search for? What did they click on? And time
of day and all those kind of things. So, based on that
information and demographic data from the U.S. Census
Bureau combining that with store locations to figure out
these customer behavior that we’re observing—what is the
closest store for those clusters of customers? And then
building models around that. So, that enabled us to really
say, what should be the assortment for a particular store
based on the people around that store that went online,
looked for certain products and categories at a certain
time? So, for a particular season, this is the popular
product. For another season, it’s a different set of products
by store. And that was really the outcome of that
predictive model to really finetune and improve the
assortment using data from online.
Another example is really the Mars drinks, where for
Mars drinks, it was really what we had was vending
machines.
Sharma: Yeah.
Shridhar: And what Mars drinks wanted to do was better
predict what products get used more and what products don’t.
And they wanted to ensure that every product was available
in the vending machine; there was no situation where certain
products are not available, certain products never move.
Sharma: So customers are never disappointed, but there’s
no overstock ever?
Shridhar: Right, exactly. Exactly. So, they wanted to prevent
that overstock and understock and they wanted to better
predict the demand. In the case of Mars drinks, they also
took into account all these external factors as well, like
for example, what impact did weather have on consumption
of products in the vending machines?
Sharma: Yeah.
Shridhar: What impact did external events and holidays and
all things have? So, part of that training that they did for
the predictive model is they took the historic data as well
as they took weather and events and so on. The historic
data was really collected by putting sensors in the vending
machines, so there was telemetric data being collected
from the vending machines indicating what products are
being consumed at which vending machines. So, they
could at any time look at a map of all the vending machines
and figure out what’s the consumption pattern? Is it
different? How does it correlate to the demographics of
the people around that area and so on? So, they got
some interesting insights from it, were able to better
predict what products to have in stock and things like that
and improve customer satisfaction.
Sharma: So, ShiSh, is it fair to assume that demand
forecasting has always been around? That every retailer
always had to worry about things like stock outages or
planning? But what has changed is the power of cloud, - -
Shridhar: Right.
Sharma: - - streamlines these solutions and a customer
can get started with an intelligent solution in say weeks
and sometimes maybe sometimes months.
Shridhar: Right.
Sharma: Instead of years and years that it took for planning
them. Is that the biggest thing—time to value?
Shridhar: That is one and one of the most common things
that we’ve seen is really the accuracy of the predictions.
Sharma: Got it.
Shridhar: Yeah. And because with democratized tools,
what we have really enabled them to do is to reduce or
easily retrain the model. So, because the process of
retraining models, because the process of adding new data
sets is very easy, most of the customers that we work with
started retraining their models more frequently, started
using additional data sets to improve the accuracy. So,
that was some of the things that we saw as a benefit,
where they could easily pull in, ingest data from public
data sources, purchase data sources, retrain the model
when they start seeing degradation or decay in the accuracy.
So, they could always keep that in good healthy and that
was really one of the benefits, yeah.
Sharma: This is really incredible. This was super helpful
for me to understand. I work on a lot of machine learning
customers, lots of use cases. What’s helpful for me is to
understand the depth of what retail customers are doing.