Sears’ Big Data Omnichannel Adventure

How is Sears using “big data” to improve its bottom line?  By using it to support initiatives like “Shop Your Way,” Sears is determined to deliver a quality customer experience to every shopper. Even though more individuals are turning to their smartphones for purchases, Sears is not deterred, and is finding new ways to take Big Data and use it to their advantage.

Sears General Manager of Big Data Ankur Gupta spoke with MPD CEO Karen Webster about Sears’ push into big data, and what the company has been doing to accommodate customers as technology continues to evolve.

KW: I think it’s very interesting to have a general manager of big data for a large department store.  Give us a sense of what it means to be a data-driven digitally integrated retailer today.

AG: Data is ultimately the underlying factor for retailers to be able to make better business decisions – decisions that align with the business vision of the retailer. At Sears, that’s something we call “Shop Your Way”.

Consumers can buy online, pick up in-store, buy online in-store, or combine all of these channels together from their phone.  They can actually go to our store and get items delivered to their car. The business idea is excellent – it brings all of the channels together and creates an integrated shopping experience, and convenience for the customer, and hopefully result in better business for our company.

But underlying it is a ton of required infrastructure.  All of the data points that we need to gather from a variety of different sources, different channels, should help us make better business decisions.  And we have built the right infrastructure that allows us to provide the shopping experience that we expect to provide to a customer, and be a totally  integrated retailer.

KW: So, Ankur, I can imagine that data’s always been important to any retailer, but I would imagine that given all of the consumer pressures to do the things that you just described, so shop your way, the demands around big data, not only capturing big data, but analyzing big data, have really become more acute.  Is this a recent phenomenon?

AG: I wouldn’t say it’s recent.  I mean, big data originally was a concept that started to get popularized in the 2004 – 2005 timeframe.  But it’s the underlying technology, that is just beginning to evolve over time, making adoption a bit more gradual.  Now, pretty much every IT vendor is coming out with their own version of big data tools and technologies now.  So, I think the adoption is accelerating as organizations realize the value of data.

At Sears, we were one of the early adopters of this technology.  We started using big data several years ago when most of the mainstream enterprises weren’t – and we were one of the first “mainstream enterprises” to do so, so not the Googles, or Facebooks who were the pioneers of these technologies.

The important point here is to remember that the data, our lives are becoming more and more digital.  And we are bombarded with data points all over places.  I mean, think of the things we all do every day … for instance, when you drive a car, you now have a smart car that can send signals to an operator in case there’s an emergency who can send help – all via the cloud and the internet and the phone. And think of how many times, we access Facebook, and Twitter via our phone. So, as I said, our lives are bombarded with the digital data from all over the place, from these different channels.  And organizations are realizing the need to understand that data, to collect it and then make sense of it so they can make better consumer decisions.

KW: How do you actually establish benchmarks for delivering value, given all of the various data points that are available to you?  How do you assess which data points actually deliver the greatest impact?

AG: There’s not really a single formula for it.  There’s not really a benchmark to follow. You identify the use case first – what is it that you’re trying to gauge?  What is it that you’re trying to make sense of and then work backwards from there.

So, I’ll give you an example here.  We were just talking about social media.  So, suppose you’re a beverage company with multiple brands, you’re selling water, you’re selling, soda, you’re selling energy drinks. You then monitor social media to see how people are talking about your brand – what keywords they are using to associate with the brand. Do you think of fresh, or thirst, or do they think it makes me feel happy?

So the benchmark associated with this example is the sentiment of people, using social media data, to better understand branding and messaging based on the association of those keywords to your brand.

Similarly, you could also gauge how well your marketing efforts are working.  So, let’s say if in a certain ZIP code, you — the same company, the same beverage company — launches a campaign, and they’re giving a certain discount on the purchase of a particular beverage during lunch. They can actually then just see the sales results from that area, and then combine that with social media sentiment from that area, and determine whether the marketing campaign is not only working but having the desired brand association.

I think the fact that retailers and brands now have access to this large amount of data, and now the tools and technologies to process that data in a very cost-effective manner, they can ask questions that weren’t possible before.  They can drill down more deeply, now they have access to the customer data, through social media, through customer behavior online, on their website, or customer behavior in-store, or on the phone, or whatever that may be for them to get a complete 360 view of that customer, and identify who their most loyal customers are, what their lifestyles could be, how to better reach out to them, and most importantly, how to better serve them.  Ultimately, that’s what we are in the business for.

So, just to wrap up my super long answer to your very direct question, I think having that access to this large amount of data, and ability to ask questions on that data, and ability to process the data, allow retailers and brands to get insight into their business that were not possible before, and they can benchmark it to what they are trying to accomplish.

KW: I’m curious to know how you look at data as an input into developing different business models, which of course is what a lot of retailers are exploring now.  There are different ways in which business models can be designed if there’s better and different data that’s available to the retailer to use as inputs.  What are your thoughts?

AG: I think so. As I said, now, with the vast amount of data that is available at our disposal, and the ability to ask questions on top of data, allows retailers to really define the business in a completely different way.

Data helps to develop different customer service levels, define new products open up new channels that were missed before.

I’ll give you an example.

We worked with a retail partner to launch a big data initiative and to do that they offered wireless access to their customers in their store.  The goal was to understand the customer’s behavior at an aggregate level, obviously for privacy reasons and not what customers did specifically when they entered the store. What we learned was pretty interesting – a massive number of customers were actually going to video sites.  The retailer was worried that the customer was looking at the competition, but they actually going to video sites such as Netflix and Youtube.

KW: So, they were kind of hanging out in the store, watching TV?

AG: Well, I guess, the theory was that this retailer had Moms coming to the store, and giving the iPod or iPhones to their husbands or kids to entertain them while they were shopping!

But, all of a sudden, access to data forces you to rethink your business model and test assumptions about consumers in new ways. It could also lead to business models that retailers are not directly part of. Here’s another example…we recently implemented real-time inventory capability for a retailer, and other lead retail clients in the specialty retail business.

What we learned was since their inventory team had access to almost real-time sales data, they could save money on their logistics cost.  Before they were getting their inventory data once a day, and they were placing orders for particular items in a particular store accordingly.  But now that they could see in real time how items were sold in the store, they could adjust delivery. So instead of waiting until the next day to know what inventory may be missing in the store, they could, pretty much in real time, determine inventory levels and stock up trucks that were leaving on their regular daily schedule.   Now all of a sudden, they’re able to save a ton of money on the logistics, just by having the real-time capability to see inventory.

To hear more of the discussion with Ankur Gupta, please click here. Ankur is but one of thirty six industry experts who will share their views on the reinvention of retail at the deep dive on retail’s reinvention at PYMNTS Summer School, August 12 – 14. To claim one of the few remaining available seats, please click here. 

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