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The hottest megatrend in enterprise computing today is Big Data – the use of massive computational ability to solve problems that were previously unfathomable. According to August Capital partner David Hornik, “Big Data is not a thing in and of itself. Big Data is an enabler. Big Data is the realization that our world is now full of lots of data that can not be ignored.”

A great example of this principle is the rapidly emerging category of Applied Big Data for Offline Retail, the ability to measure and ultimately optimize brick-and-mortar retail stores just as their online cousins have been doing for more than a decade.

Online analytics turned out to be a huge business, as exemplified by Adobe’s 2009 acquisition of Omniture for $1.8 billion or the presence of Google Analytics on nearly 50% of the world’s million most popular websites. The digital nature of e-commerce made online measurement straightforward from day one, but in the physical world the engineering required to measure consumer behavior is much more challenging.


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In the last few years technology has reached the level where brick-and-mortar stores can detect and abstract shoppers’ behaviors and use that information to draw conclusion about how they respond to the specifics of store layout, merchandising, staffing, marketing, promotions, and more. In-store analytics now rivals online analytics in its depth, reliability, and usefulness. And as the technology advances, offline analytics will actually surpass online, since the information that can be gleaned from a physical retail environment is, in principle, bottomless.

How Big Is Big (Data)?

By combining a store’s point-of-sale (POS) systems, video feeds from ceiling cameras, staffing software, RFID tags, Wi-Fi trackers, and more, an in-store analytics system can collect up to 10,000 data points per store visit. In the case of RetailNext, we have more than 50 retail chains measuring over 300 million shoppers a year. To do so, our in-store systems annually collect roughly 57 petabytes (or 57,000,000 GB) of raw data and then process them in real time into the trillions of data points that feed our analytics engine.

The online world doesn’t have access to all the robust situational data that is available to physical retailers. If you visit your favorite online retail site and put an item in your cart, you’ll see that there are at most a dozen or two links on that page, meaning there are a dozen or two actions you can now take. Compare that to a shopper standing in a physical store with an item in her hand. The possibilities of what she can do are infinite. She can walk to the register, or anywhere else in the store, through an endless number of possible paths. She can interact with any object in the store, whether or not it’s a product for sale. She can speak with an employee on a limitless number of topics, and no two conversations will be the same.

Even though e-commerce sites measure critical user actions online, such as the amount of time spent on individual pages, they do not have insight into what the shopper is doing while on that page. For example, an online retailer might see that a user spent ten minutes on a particular web page. That could indicate heavy involvement, or no involvement at all. The visitor may have switched between applications or walked away from the keyboard entirely. In the physical retail environment, we can be certain of the shopper’s presence and active involvement in the store.

Situational Data: There Is No “Mood Mouse”

In addition to the rich set of actions shoppers can take, advanced offline analytics offer visibility into other factors that web properties can never see:

  • Emotion: There is no “mood mouse” to tell if an online shopper is frustrated, confused, excited, or bored. In the near future, physical stores will be able to draw conclusions about shoppers’ emotions based on facial expression, movement through the store, or other indicators.
  • Location: Online retailers can’t reliably place visitors’ locations; most don’t even try to get more specific than the country level. Brick-and-mortar retailers by their nature uncover the differences in shopping behavior down to a street-by-street level. Population demographics, average income level, and residential vs. business neighborhoods are all hugely influential in shopping behavior, and they’re measurable for brick-and-mortar chains.
  • Weather: Shoppers may make very different choices depending on whether it’s sunny or stormy. Online retailers have no way of knowing the weather for each shopper, while physical stores do.
  • Demographics: Are shoppers male or female? Old or young? Alone or in groups? With children or without? These factors change the shopping experience and purchasing decisions, and physical stores can detect and tie them to sales at the register.

Turning Big Data into Big Information

These analytics open up a host of deeper insights for store managers. For instance, retailers can “heat map” their stores, providing information on how much shoppers travel to specific parts of the store – from aisles to endcaps. Heat mapping helps retailers increase sales considerably by matching the location of the products they most want to sell with the most trafficked parts of the store. Additionally, retailers can differentiate and compare employee staffing to shopper traffic, identifying opportunities for increasing conversion rates or measuring how actively sales associates engage with customers. Retailers can also test the effects of promotions, programs, and in-store merchandising to truly understand their effect on making the register ring.

Adweek recently opined that a full decade from today brick-and-mortar shopping will still represent at least 80 percent of retail sales. With this kind of industry dominance, there is no room for brick-and-mortar retailers to have any disadvantage over online retailers – including the big rewards of Big Data.

Alexei Agratchev is Co-Founder and CEO of RetailNext, which provides real-time in-store monitoring and analytics. Prior to joining RetailNext, Alexei was the founder and General Manager of an internal startup within the Cisco Emerging Technologies Group focused on developing video applications for the gaming and retail markets.

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