Increasing numbers of companies are employing Predictive Analytics technology to better understand their customers, and then capitalize on that information. Organizations want to use this wealth of customer data to create 360-degree portraits of their customers' shopping habits and purchasing patterns.
These predictive analytics tools are not only for specialized analysts. Marketers are using these resources and acting on this information to offer consumers special deals and coupons, identify loyal customers and increase profits. The premise is that to stay competitive, retailers need to understand not only their customers' wants and needs, but also predict future tendencies. However, this consumer data has privacy implications that regulatory agencies, courts, rights advocates and corporations themselves are debating.
The fact is that the current combination of moderate growth in domestic markets and more informed, budget-conscious consumers presents a challenge to developing profitable growth strategies for U.S.-based retailers. Partly as a result, companies are using analytics tools to tell them things that most shoppers would probably prefer to keep to themselves, if they knew about it.
These behavioral snapshots are most common on the Web. Of course, you've noticed how when you shop online for, say, a Coach bag and then change your mind. Afterwards, ads for Coach bags seem to appear on nearly every website you visit. That's because online retailers track users with a virtual identification number, and then purchase targeted ads for products of interest to that particular consumer.
At this point, omni-channel retail—shopping that combines the Web, mobile devices and brick and mortar shops—is here to stay. Frequently, consumers are both offline and online as they make purchasing decisions. Online these consumers are sharing brand information, researching, learning about products and using social media to become better informed.
Offline, they're evaluating, testing, and brand associating. And they're using their mobile devices to compare merchandise and prices while physically shopping in a store. For marketers, the point is to engage these customers in a two-way dialogue and uniquely tailor offerings to make a sale, not just push messages and ads to users' smartphones or tablets.
As smartphone technology evolves to provide contextually sensitive information to make offers to users at the right time and place, Predictive Analytics is being extended to brick and mortar retail establishments. For example, in-store retail analysis represents one area where physical customer monitoring can reveal valuable information.
Analytics companies such as RetailNext offer real-time store monitoring to better understand shopper behavior—and to capitalize on that information. Capabilities include video-tracking customer movements through stores, recognition technology to determine gender and identifying unique visitors across one or more store visits.
Whether shoppers are being alerted to such profit-focused surveillance is uncertain. Counter-claims point out that retailers can use this information to offer customized and improved services and products.
As a result, concerns and controls to protect consumer privacy have emerged in a number of areas. A recent report from the World Economic Forum looked closely at online privacy issues and recommended user-controlled privacy options for data.
The idea is that all captured data would contain software-based personal preference tags that designated how that data could be used. Personal data, such as that gleaned using Predictive Analytics, would be registered and data usage violations would be penalized.
However, the emphasis on data technology to protect privacy may be misplaced. Rather, many experts believe more effective rules should be legislated to curb data misuse by corporations and retailers. To that end, the MIT Media Lab, an emerging technology research center, is helping to establish basic principles when it comes to captured personal data.
Its three guidelines state that users have the right to possess their data, control how it is used and destroy or distribute it as they see fit. The group has created openPDS: A Privacy-Preserving Personal Data Store that helps control, store and audit flows of personal data. It's possible that this type of framework would undermine the role of data brokers and better protect users' privacy.
The fact is that decisions on how Predictive Analytics data is used can't be dependent on the moral compass of marketers and analysts at retail companies. It's well-known that commerce is based on a two-way interaction, and it's been that way historically for a long time. It's also true that retailers consistently need feedback from their customers to provide them with the desired products, services and shopping experiences.
However, the build-up of increasing amounts of data will require the parallel expansion of users' privacy rights. Similar concerns occurred in the 1960s when mainframe computers first began to compile the financial information of millions of Americans for the IRS and fledgling credit bureaus
Eventually, the Fair Credit Reporting Act of 1970 recognized citizens' rights to control how their information was being used. Perhaps Big Data and Predictive Analytics simply represent the next stage in the evolution.
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