Know What Your Customers Want Before They Do
Thomas H. Davenport, Leandro Dalle Mule, and John Lucker
Idea in Brief
Targeting individuals with perfectly customized offers at the right moment across the right channel is marketing’s holy grail. As companies’ ability to capture and analyze highly granular customer data improves, such offers are possible—yet most companies make them poorly, if at all.
Perfecting these “next best offers” (NBOs) involves four steps: defining objectives; gathering data about your customers, your offerings, and the contexts in which customers buy; using data analytics and business rules to devise and execute offers; and, finally, applying lessons learned.
It’s hard to perfect all four steps at once, but progress on each is essential to competitiveness. As the amount of data that can be captured grows and the number of channels for interaction proliferates, companies that are not rapidly improving their offers will only fall further behind.
Artwork: Rachel Perry Welty, Lost in My Life (Playmobil), 2010, pigment print
Photography: Rachel Perry Welty and Yancey Richardson Gallery, NY
Shoppers once relied on a familiar salesperson—such as the proprietor of their neighborhood general store—to help them find just what they wanted. Drawing on what he knew or could quickly deduce about the customer, he would locate the perfect product and, often, suggest additional items the customer hadn’t even thought of. It’s a quaint scenario. Today’s distracted consumers, bombarded with information and options, often struggle to find the products or services that will best meet their needs. The shorthanded and often poorly informed floor staff at many retailing sites can’t begin to replicate the personal touch that shoppers once depended on—and consumers are still largely on their own when they shop online.
This sorry state of affairs is changing. Advances in information technology, data gathering, and analytics are making it possible to deliver something like—or perhaps even better than—the proprietor’s advice. Using increasingly granular data, from detailed demographics and psychographics to consumers’ clickstreams on the web, businesses are starting to create highly customized offers that steer consumers to the “right” merchandise or services—at the right moment, at the right price, and in the right channel. These are called “next best offers.”Consider Microsoft’s success with e-mail offers for its search engine Bing. Those e‑mails are tailored to the recipient at the moment they’re opened. In 200 milliseconds—a lag imperceptible to the recipient—advanced analytics software assembles an offer based on real-time information about him or her: data including location, age, gender, and online activity both historical and immediately preceding, along with the most recent responses of other customers. These ads have lifted conversion rates by as much as 70%—dramatically more than similar but uncustomized marketing efforts.
The technologies and strategies for crafting next best offers are evolving, but businesses that wait to exploit them will see their customers defect to competitors that take the lead. Microsoft is just one example; other companies, too, are revealing the business potential of well-crafted NBOs. But in our research on NBO strategies in dozens of retail, software, financial services, and other companies, which included interviews with executives at 15 firms in the vanguard, we found that if NBOs are done at all, they’re often done poorly. Most are indiscriminate or ill-targeted—pitches to customers who have already bought the offering, for example. One retail bank discovered that its NBOs were more likely to create ill will than to increase sales.
Companies can pursue myriad good goals using customer analytics, but NBO programs provide perhaps the greatest value in terms of both potential ROI and enhanced competitiveness. In this article we provide a framework for crafting NBOs. You may not be able to undertake all the steps right away, but progress on each will be necessary at some point to improve your offers.
Many organizations flounder in their NBO efforts not because they lack analytics capability but because they lack clear objectives. So the first question is, What do you want to achieve? Increased revenues? Increased customer loyalty? A greater share of wallet? New customers?
The UK-based retailer Tesco has focused its NBO strategy on increasing sales to regular customers and enhancing loyalty with targeted coupon offers delivered through its Clubcard program. As Roland Rust and colleagues have described (“Rethinking Marketing,” HBR January–February 2010), Tesco uses Clubcard to track which stores customers visit, what they buy, and how they pay. This has enabled the retailer to adjust merchandise for local tastes and to customize offerings at the individual level across a variety of store formats, from hypermarts to neighborhood shops. For example, Clubcard shoppers who buy diapers for the first time at a Tesco store are mailed coupons not only for baby wipes and toys but also for beer. (Data analysis revealed that new fathers tend to buy more beer, because they are spending less time at the pub.) More recently, Tesco has experimented with “flash sales” that as much as triple the redemption value of certain Clubcard coupons—in essence making its best offer even better for selected customers. A countdown mechanism shows how quickly time or products are running out, building tension and driving responses. Some of these offers have sold out in 90 minutes.
Tesco’s NBO strategy seeks to expand the range of customers’ purchases, but it also targets regular customers with deals on products they usually buy. As a result of its carefully crafted, creatively executed offers, Tesco and its in-house consultant dunnhumby achieve redemption rates ranging from 8% to 14%—far higher than the 1% or 2% seen elsewhere in the grocery industry. Microsoft had a very different set of objectives for its Bing NBO: getting new customers to try the service, download it to their smartphones, install the Bing search bar in their browsers, and make it their default search engine.
Starting with a clear objective is essential. So is being flexible about modifying it as needed. The low-cost DVD rental company Redbox initially made e-mail and internet coupon site offers intended to familiarize consumers with its kiosks. Redbox kiosks were a new retail concept, but in time people became accustomed to automated movie rentals. As the business grew, the company’s executives realized that to increase profits while maintaining the low-cost model, they needed to persuade customers to rent more than one DVD per visit. So they shifted the emphasis of their NBO strategy from attracting new customers to discounting multiple rentals.
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