By Kerry Doyle | Former senior editor at ZDNet.com and contributor covering business and technology issues for PCWeek Labs, PCWeek magazine, and Harvard Business School.
Increasing number of businesses are attempting to make sense of the vast amounts of data generated through personal, societal, and industrial internet interactions from social media, mobile devices, geolocators, and digital sensors.
By examining this big data, companies can create successful customer loyalty and retention programs, and personalize consumer interactions in meaningful ways–both of which are crucial to strong bottom-line performance. Such information helps companies target customer communications, marketing campaigns, and special offers. The premise is that to stay competitive, companies need to understand not only their customers’ wants and needs, but also predict their future tendencies.
Analysis of that collected data is termed “customer analytics.” Many companies are finding that such analysis directly addresses their desire to cut costs, create returning customers, and turn those loyal shoppers into effective advocates for their brand. However, due to the volume, velocity, and variety of this unstructured information, companies face challenges when trying to produce meaningful results.
Fortunately, customer analytics tools enable companies to analyze that data, gain buying pattern insights, and deliver valuable, personalized customer messages. Big data analysis offers companies a way to identify those shoppers who are the most valuable as returning customers.
These loyal customers then act as brand advocates, an equally valuable resource. But special offers and shopping deals come at a price: personal information. Such consumer data has privacy implications that regulatory agencies, courts, rights advocates, and corporations themselves are debating.
Due to the current combination of moderate economic growth and more informed, budget-conscious consumers, companies face challenges to developing profitable growth strategies. Partly as a result, organizations are using big data and analytics tools to uncover things that most shoppers would probably prefer to keep private, if they knew about it.
For example, “behavioral snapshots” are common on the web. You’ve likely noticed how when shopping online in search of an item, 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.
Online, consumers are sharing brand information via social media as well as researching and learning about products. Offline, they’re evaluating, testing, and brand associating. And they’re using mobile devices to compare merchandise and prices while shopping in an actual 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. For example, as smartphone technology evolves to provide context-aware information for making offers to users at the right time and place, analytics are being extended to brick-and-mortar establishments.
In-store analysis represents one area where physical customer monitoring can reveal valuable information. Analytics companies, such as RetailNext, offer real-time store monitoring to understand shoppers better–and to capitalize on that information. Capabilities include video-tracking customer movements, recognition technology to determine gender and identifying unique visitors across multiple 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.
In an era with ever-increasing amounts of data, questions remain: are companies collecting the right data? Are they doing so in a manner that enables effective interpretation of that data to provide tangible benefits? Are they keeping the best interests of consumers in mind? Some are still uncertain as to whether analyzing data can effectively maintain privacy and tell us what we need to know about our economic world.
This article was produced by Xerox and not the Quartz editorial staff.