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Safe Haven from the Retail Analystics Privacy Fire: Retailer-Branded, Shopper-Approved Smartphone Apps

  • February 2014
  • 12 pages
  • Frost & Sullivan
Report ID: 2015959

Summary

Table of Contents

This Stratecast SPIE report analyzes an analytics-driven shopper engagement approach that gets away from the notion of installing a secretive data collection “dragnet” in a retail location; and, instead, engages customers 24/7, with shopper approval upfront—including when they are at or near a retail location.

Introduction

This may be the most challenging time in history to be a retailer. Shoppers are increasingly mobile, and thus harder to reach; and are less brand-loyal than ever before. Thanks to the Web and various apps that combine scanning and price comparison functionality, consumers with smartphones who are shopping for a product in one store or on one site can instantly find prices and availability for the identical product online or down the street at another retail location. This leads to a phenomenon known as “showrooming”: the act of shoppers viewing merchandise in a physical retail store only to then purchase the merchandise online or from another retailer. The instant and mobile access a smartphone offers to the vast array of e-commerce content, pricing, and social networks puts shoppers in a position of strength when it comes to getting the product they want at the best price, wherever they can find it.
Macroeconomic factors are creating challenges, too. For example, in the U.S., the latest U.S. census data showed average family incomes dropping by up to percent; and, by all measures, lower income generally translates into lower retail spending.

Bricks-and-mortar retailers also struggle in another way. They have long maintained solid data around customer accounts and purchases, and loyalty programs. Yet, when it comes to shopper behavioral data, they have been badly overmatched by e-tailers, who, for years, have been equipped with online analytics systems that track every move shoppers make while on an e-tailer’s Web site. The effect extends beyond the e-tailer’s site, because online analytics systems also provide data on referring sites and domains: the last Web “place” a shopper was before coming to the e-tailer’s site. As a result, retailers face a scenario where their customers, and their e-tailer competitors, have better access to a wide range of both useful and immediately usable data than the retailers themselves.

In the face of these challenges, retailers are beginning to arm themselves with retail analytics solutions that counteract some of those challenges, with new information opportunities through collecting data from shopper’s smartphones. This can help remedy the blind spot retailers have had up to now regarding many aspects of shopper behavior.

Yet, retail analytics systems, especially those that collect data with no shopper intervention required, raise fears of Big Brother (or Big Retailer) watching shoppers without their knowledge or approval, and possibly misusing the data for intrusive marketing purposes, or worse.

This Stratecast SPIE report analyzes an alternative, analytics-driven shopper engagement approach that gets away from the notion of installing a secretive data collection “dragnet” in a retail location; and, instead, engages customers 24/7, with shopper approval upfront—including when they are at or near a retail location.

Retail Analytics: Shopper Involvement and Privacy Concerns

Many retail analytics systems reside on retailers’ Wi-Fi networks, collecting data from the MAC addresses of Wi-Fi-equipped smartphones (with the Wi-Fi turned on) that pass through the coverage grid of the Wi-Fi network. In this way, retail analytics solutions provide much the same kind of behavioral data about shoppers when they are in a store as online analytics solutions do for shoppers traversing an e-tailer’s Web site; or, for that matter, a Web site operated by a retailer.

Stratecast has identified three processes that enable retail analytics systems to collect in-store data about shoppers, and to interact with shoppers.

Many retail analytics systems are software-based, and the companies that provide them either install these systems or link them with a retailer’s Wi-Fi equipment. Other providers sell retail analytics as a bundled solution with their own equipment. One of the reasons Stratecast has dropped “Wi-Fi” from its designation of this Big Data component area is that when it comes to retail analytics, Wi-Fi is only part of the story. For instance, retailers are also leveraging shopper data in Bluetooth beaconing systems that alert shoppers to items and deals in quite specific locations within a store; for example, on one end of a certain aisle. Other retail analytics systems, use video surveillance systems that tap into CCTV camera video, using software that attempts to identify unique shoppers, and track their movements inside the retail location.

A More Engaging Approach to Data Collection and Mobile Commerce?

Retail analytics is clearly an important tool for retailers. The data in a retail analytics system functions similarly to “cookies” on the Web, giving retailers personally identifiable information (PII) about shoppers. By triangulating MAC addresses against point of sale (POS) data, retailers can find out exactly who shoppers are. Even if, as providers claim, shoppers remain anonymous, retail analytics can help retailers develop highly detailed profiles about shoppers. While this is positive for retailers, it also has the potential to fuel highly intrusive marketing activities; or, worse, “interlocking” coordinated activities by multiple retail and financial entities that are part of a shopper’s consumer life. As a result, regulators and consumer watchdog groups are stepping up the pressure on retailers and their vendor providers, particularly those whose systems fall into the Passive category. Those systems require no action by shoppers in order for retailers to collect data from their smartphones—and require shoppers to take action (such as scanning a barcode on a sign, or turning off Wi-Fi on their smartphones) in order to opt out of data collection.

The app-based method is the least secretive for consumers because it requires their active participation through accepting terms and conditions, and installing a mobile app on their smartphones, well ahead of when they set foot in a retail location. That is why—although mobile game apps have drawn fire for unethical treatment of user data by some content providers—when it comes to retail analytics and mobile marketing, branded retail mobile apps are not in the cross hairs of government regulators and consumer privacy advocates.

One company that combines what is currently the least intrusive method of collecting retail shopper data with capabilities that support location-based marketing is Digby. Digby was one of the first providers to develop a mobile commerce platform for mobile and mobile Web applications; and AT&T offers Digby’s platform as Digby Mobile Commerce from AT&T. Today, Digby offers retailers an enterprise-ready mobile software platform called Localpoint that powers relevant proximity marketing and location analytics for retailers and brands. Localpoint is a prime example of two key constructs that Stratecast has established as relevant—one specific to retail analytics and the other with applicability across the Big Data solutions landscape:

1. Retail analytics looks a lot like online analytics. Retail analytics performs a similar function—when it comes to analyzing shopper behavior at a retail location—that online analytics performs with regard to user behavior on a Web site.

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