Kuber Sharma: Every retail customer that I actually talk to
they want to talk about personalization, how they can better
serve their customer. What does really personalization mean?
Can you help me understand in layman’s language?
ShiSh Shridhar: So, at the base of it is really that retailers
want anything that they offer you will be relevant for you
and will be just in time. And any offer that has no relevance
to what you like is not going to be a very valid engagement
and could potentially make you lose that customer as
well. The customer churn goes up when you’re trying
to engage with a customer the non-relevant communication.
The other aspect is when you’re talking about it from a
personalization perspective for online, what are the kind
of products that you see online?
Sharma: Yeah.
Shridhar: Is it relevant? Is it personalized based on what
you like? And you’re not sifting through a thousand
products to find what you want.
Sharma: Like a recommended product or a next best action,
like what else I might want to buy or try.
Shridhar: Exactly. So, if the recommendation that shows
up in an offer or online or even when you go into the store,
the assortment that you have in the store is more relevant
to you.
Sharma: Got it.
Shridhar: You’ll find an apparel that’s the right color, the
right size, that enables the retailer to have better engagement
and retention of you as a customer. So, that’s a huge value
that retailers are looking at.
Sharma: I struggle with this a lot. I drink a specific
brand of almond milk and I know only one of the retailers
around my house carries it. And because they have this
product for me, I end up going to them for all my groceries.
Shridhar: Exactly. Exactly. And that’s the level at which at
a very personal level they want to know what your preferences
are and what’s the kind of services and products you would
like without you actually explicitly telling them that. And
that’s really where analytics will help them gauge and guess
what you really like and what products and services should
they invest in in the future as well. And that’s the other
aspect that they’re looking at.
Sharma: So, ShiSh, it sounds like personalization is a
very big field. So, what are some of the common use cases
that you’ve seen our customers deploying?
Shridhar: So, in terms of use cases, the online
recommendations is one aspect that a lot of retailers
are implementing as almost like the first thing to do.
They’re building out these recommendation engines where
when you go online, you can choose products and see
recommendations based on your choice. That same thing
can be carried over to the store as well. For example, in
an assisted sales scenario, the store associate could be
recommending products. That’s another aspect of it.
The same thing can also be translated to what assortment
of products are available in the store based on - -
Sharma: Like the milk?
Shridhar: Exactly. Like the collective requirements of the
people around that store. What’s the most popular product
during a particular time, around a particular area? For
example, the Walmart example where when there’s a
hurricane out there, they used analytics to figure out what
are the top 10 products that are in demand - -
Sharma: Yeah.
Shridhar: - - when the forecast is out. So, having the
right product is extremely important. It becomes more
personalized. When you walk into the store, you find what
you’re looking for. When you go online, the recommendations
that show up is relevant to you. And when they try to
engage with you in response to an event, the offer is
relevant as well and just in time. When the temperature
hits 80 degrees, they know you want a cold coffee and the
offer will be relevant for that particular situation.
Sharma: That totally makes sense. I do remember this year
was a particularly hot year in Seattle and I saw a bunch of
retailers were running out of fans and air conditioners. If
only they would use a intelligent solution, they wouldn’t have
this stock outage.
Shridhar: Exactly. To be able to predict it well ahead of
time what the demand will be based on what the predicted
weather is going to be because weather has a big impact
on that situation, yeah.
Sharma: So, can you talk a little bit about some customer
examples that you’ve seen?
Shridhar: One of the examples I can think of that I like is
McDonalds. McDonalds, they had a situation where every
one of their customers was a nameless, faceless person and
they wanted to better engage with the customers. They
were doing a lot of promotions and things like that. They
wanted to make sure all the promotions they drive had
high ROI. So, in order to do that, they had to better
understand their customers. They had an application, but
the challenge with most mobile applications is we as
customers when we go into a retail store chances are they’re
not using the mobile application. We have no need to do that.
What we did with McDonalds really with a partner called
Plexure is to figure out what’s the value exchange? So, we
geofenced the McDonalds stores. When a customer walks
in close proximity to the store, it knows you’re there. It
looks at the time of the day, looks at your past purchases,
and gives you a very customized menu and offers. You
make a selection from that, your order goes to the kitchen,
you walk in, pick up your order, and you leave. The payment
is handled frictionless. And that was the kind of experience
that most customers wanted. That was the value exchange.
If I could be in and out of a McDonalds within a few seconds,
I would use the app because it helped me do that. And that
was the success for the McDonalds implementation. As a
result, McDonalds got all the telemetric data from the
behavioral information of the customers—what were they
selecting? What time of day they were coming in. How often
were they going to McDonalds? So, all that telemetric data
became available. As a result, they were also able to see
what promotions were successful with what demographic
Sharma: Testing.
Shridhar: Exactly. So, they knew things like the mango
smoothie is popular with the millennial female. That was
the kind of information that was not available. They knew
what promotions were successful in what area or were not.
And they could in real-time finetune that as well. So, they
were able to do a lot of things because of the data collected
and primarily it was the value exchange that they provided
in there as well.
The other example is really 7-11.
Sharma: Okay.
Shridhar: So, with 7-11, it was a similar implementation,
where in 7-11 it was exactly the same thing, but the value
exchange was a lot different, where this was in Australia.
They had a gas station, so for value exchange, what they
did was they said, as a customer, if you are filling gas and
you notice the gas price is low today, you can download
the app, lock the price for the next two or three purchases - -
Sharma: That’s a great deal.
Shridhar: Exactly. You prepay and that enabled them to
capture people, use the application because there was a
need for it, and it also drove traffic into the 7-11.
Sharma: You become a sticky customer.
Shridhar: Because you had to come back, exactly. You
had to come back and use it. Also, what it really did was
now enable them to collect a lot of data about the
customers. They knew from just social media profiles and
things like that. They knew the profile. They could target
specific offers and, most importantly, they could drive
traffic to the convenience store that was attached to the
gas station. So, that was the other benefit that they got
from the solution.
Sharma: Those are pretty cool and those are mobile phone
based ideas. What about retailers who want to go beyond
the phone.
Shridhar: So, there’s some interesting examples. One of
them is Coop Italia. What they did is they didn’t really
rely on the mobile applications of their customers. Instead,
they had digital signages across the store. And what they
did in their situation was they had sensors around the
store. They knew what people were looking at, how long
were they dwelling on something? Were they actually
picking up something, pointing at something, or looking
at something? And this was cameras and connected devices
and sensors. And based on that, the digital signage would
change. So, if I’m looking at a certain product, it’d give
me recipes that were related to it. It’d give me the origin
of that product. It’d give me additional information, related
offers, things like that.
Sharma: That’s awesome.
Shridhar: Exactly. They were all on digital signages and
they would look at my behavior. If I stood too long in
front of something, there was a desire for me to get it
and it would change. It would give me recommendations,
and so. So, this is one example where it wasn’t really
mobile, but they actually had all the digital signages and
sensors in the store.
Another example that I can think of is Lowes, where what they
used is, it was a unique example were as a customer if I’m
designing my kitchen and I’m unable to articulate what
style I like. What’s my color preference? What’s my theme
preference and so on? But, on the other hand, when I see
a bunch of pictures, I can point out those pictures and say
what I like. So, what they really did is they looked at their
typical customer journey. People typically go to Pinterest,
look for pictures for ideas. So, they let you pin those
pictures. And what we did is we wrote an application that
will actually connect your Pinterest account, if you opt in
to do that, and once you do that, it will pull in all your
pins, use deep learning to analyze those pins, figure out
what your style is, what your theme is, and look at all
those little attributes that defines what your preference
really is. And then it matches it to the catalog in Lowes,
looks for all the products and says, based on the catalog
and based on your preferences, these are the types of
kitchens we think you’ll like.
Sharma: Yeah.
Shridhar: These are the products you like, so it actually
looks at all the products in those pins and then picks
up similar products from the Lowes inventory. And that
was a great personalization example and it’s great for
people who can’t quite describe what they’re looking for,
but they can point out something in a picture. And then
it personalizes the offers and the recommendations
based on that.
Sharma: This sounds like a useful thing for the customer
because they’re experience is so much more easier
because they don’t have to think so much about, oh, what
should I be buying? But I get a personalized recommendation
on what I might like.
Shridhar: Right, exactly.
Sharma: And it’s interesting the power here of big data, of
cloud, and intelligence because there’s a lot of data that
these retailers are relying upon.
Shridhar: Right, exactly.
Sharma: And the reason they can process it, store it,
analyze it easily is because of cloud.
Shridhar: Right.
Sharma: However, it’s the accessibility of intelligence
that you don’t have to be always a deep data scientist
to use deep neural networks or to get started with these
intelligent applications.
Shridhar: Exactly.
Sharma: So, a democratized version of intelligence can
really truly allow these customers to get started.
Shridhar: Exactly. And all these concepts we’re talking
about are very complex concepts. They’re deep learning
concepts and things like that. How do we now enable
companies to quickly build on these? And that’s where
the tools really come in, absolutely.