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Know what your customers want before they do Know what your customers want before they do Document Transcript

  • HBR.ORG DECemBER 2011 reprint R1112ESpotlight on Reinventing RetailKnow What YourCustomers WantBefore They DoRetailers need to target customers with the rightdeal at the right time. Here’s how to nail the “nextbest offer.” by Thomas H. Davenport, Leandro DalleMule, and John Lucker
  • Spotlight on Reinventing RetailSpotlight Artwork Rachel Perry Welty Lost in My Life (Playmobil) 2010, pigment printKnow WhatYour CustomersWant BeforeThey Do hoppers once relied onRetailers need to target customers with a familiar salesperson—the right deal at the right time. Here’s such as the proprietorhow to nail the “next best offer.” of their neighborhood general store—to helpby Thomas H. Davenport, Leandro Dalle them find just what theyMule, and John Lucker wanted. Drawing on what he knew or could quickly deduce about the cus- tomer, 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 dis- Photography: Rachel Perry Welty and Yancey Richardson Gallery, NEW YORK tracted 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 per- haps 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.”2 Harvard Business Review December 2011  
  • For article reprints call 800-988-0886 or 617-783-7500, or visit hbr.org
  • Spotlight on Reinventing RetailConsider Microsoft’s success with e-mail offers forits search engine Bing. Those e‑mails are tailored What Makes to adjust merchandise for local tastes and to custom- ize offerings at the individual level across a varietyto the recipient at the moment they’re opened. In An NBO? of store formats, from hypermarts to neighborhood200 milliseconds—a lag imperceptible to the re- “Next best offer” is shops. For example, Clubcard shoppers who buy increasingly used tocipient—advanced analytics software assembles an diapers for the first time at a Tesco store are mailed refer to a proposaloffer based on real-time information about him or customized on the coupons not only for baby wipes and toys but also forher: data including location, age, gender, and online basis of beer. (Data analysis revealed that new fathers tend toactivity both historical and immediately preceding, the consumer’s buy more beer, because they are spending less time attributes andalong with the most recent responses of other cus- behaviors at the pub.) More recently, Tesco has experimentedtomers. These ads have lifted conversion rates by as (demographics, with “flash sales” that as much as triple the redemp-much as 70%—dramatically more than similar but shopping history) tion value of certain Clubcard coupons—in essenceuncustomized marketing efforts. the purchase making its best offer even better for selected custom- context The technologies and strategies for crafting next (bricks and mortar, ers. A countdown mechanism shows how quicklybest offers are evolving, but businesses that wait online) time or products are running out, building tensionto exploit them will see their customers defect to p  roduct or service and driving responses. Some of these offers have characteristicscompetitors that take the lead. Microsoft is just sold out in 90 minutes. (shoe style, type ofone example; other companies, too, are revealing mortgage) Tesco’s NBO strategy seeks to expand the range ofthe business potential of well-crafted NBOs. But t  he organization’s customers’ purchases, but it also targets regular cus-in our research on NBO strategies in dozens of re- strategic goals tomers with deals on products they usually buy. As a (increase sales, buildtail, software, financial services, and other compa- customer loyalty) result of its carefully crafted, creatively executed of-nies, which included interviews with executives at fers, Tesco and its in-house consultant dunnhumby15 firms in the vanguard, we found that if NBOs are NBOs are most often achieve redemption rates ranging from 8% to 14%— designed to inspiredone at all, they’re often done poorly. Most are indis- a purchase, drive far higher than the 1% or 2% seen elsewhere in thecriminate or ill-targeted—pitches to customers who loyalty, or both. grocery industry. Microsoft had a very different sethave already bought the offering, for example. One They can consist of of objectives for its Bing NBO: getting new customersretail bank discovered that its NBOs were more likely products to try the service, download it to their smartphones, (a coupon for diapers)to create ill will than to increase sales. install the Bing search bar in their browsers, and services Companies can pursue myriad good goals us- (a discount on a spa make it their default search engine.ing customer analytics, but NBO programs provide visit) Starting with a clear objective is essential. So isperhaps the greatest value in terms of both potential information being flexible about modifying it as needed. TheROI and enhanced competitiveness. In this article (Google ads to click on) low-cost DVD rental company Redbox initially madewe provide a framework for crafting NBOs. You may relationships e-mail and internet coupon site offers intended tonot be able to undertake all the steps right away, but (LinkedIn and Facebook familiarize consumers with its kiosks. Redbox ki- recommendations)progress on each will be necessary at some point to osks were a new retail concept, but in time peopleimprove your offers. Despite the name, an became accustomed to automated movie rentals. NBO may in fact be an As the business grew, the company’s executives re- initial engagement. AndDefine Objectives whether the customer alized that to increase profits while maintaining theMany organizations flounder in their NBO efforts not relationship is new or low-cost model, they needed to persuade customersbecause they lack analytics capability but because ongoing, the NBO is to rent more than one DVD per visit. So they shifted intended to be a “bestthey lack clear objectives. So the first question is, offer.” the emphasis of their NBO strategy from attractingWhat do you want to achieve? Increased revenues? new customers to discounting multiple rentals.Increased customer loyalty? A greater share of wal-let? New customers? Gather Data The UK-based retailer Tesco has focused its NBO To create an effective NBO, you must collect and in-strategy on increasing sales to regular customers and tegrate detailed data about your customers, your of-enhancing loyalty with targeted coupon offers deliv- ferings, and the circumstances in which purchasesered through its Clubcard program. As Roland Rust are made.and colleagues have described (“Rethinking Market- Know your customers. Information valu-ing,” HBR January–February 2010), Tesco uses Club- able for tailoring NBOs can be relatively basic andcard to track which stores customers visit, what they easily acquired or derived: age, gender, number ofbuy, and how they pay. This has enabled the retailer children, residential address, income or assets, and4 Harvard Business Review December 2011  
  • For article reprints call 800-988-0886 or 617-783-7500, or visit hbr.org Idea in Brief Targeting individuals with Perfecting these “next It’s hard to perfect all four perfectly customized of- best offers” (NBOs) involves steps at once, but progress fers at the right moment four steps: defining objec- on each is essential to com- across the right channel is tives; gathering data about petitiveness. As the amount marketing’s holy grail. As your customers, your of- of data that can be captured companies’ ability to capture ferings, and the contexts in grows and the number of and analyze highly granular which customers buy; using channels for interaction pro- customer data improves, data analytics and business liferates, companies that are such offers are possible—yet rules to devise and execute not rapidly improving their most companies make them offers; and, finally, applying offers will only fall further poorly, if at all. lessons learned. behind.psychographic lifestyle and behavior data. Previous about 600 billion geospatially tagged data feedspurchases are often the single best guide to what a back to telecommunications providers every day.customer will buy next, but that information may be An application developed by the software analyt-harder to capture, particularly from offline channels. ics company Sense Networks can compare a con-Loyalty programs like Tesco’s can be a powerful tool sumer’s movements with billions of data points onfor tracking consumers’ buying patterns. the movements and attributes of others. Using this Even as companies work (and sometimes strug- location history, it can estimate the consumer’s age,gle) to acquire these familiar kinds of customer data, travel style, level of wealth, and next likely location,the growing availability of social, mobile, and loca- among other things. The implications for creatingtion (SoMoLo) information creates major new data highly customized NBOs are clear.sets to be mined. Companies are beginning to craft Know your offerings. Unless a company hasoffers based on where a customer is at any given detailed information about its own products ormoment, what his social media posts say about his services, it will have trouble determining which of-interests, and even what his friends are buying or ferings might appeal most to a customer. For somediscussing online. products, such as movies, third-party databases One example is Foursquare, which makes cus- supply product attributes, and companies that renttomized offers according to how many times con- or sell movies can surmise that if you liked onesumers have “checked in” to a certain retail store. movie with a particular actor or plot type, you willAnother is Walmart, which acquired the social media probably like another. But in other retail industries,technology start-up Kosmix to join its newly formed such as apparel and groceries, compiling product at-digital strategy unit, @WalmartLabs, in capitalizing tributes is much more difficult. Manufacturers don’ton consumer SoMoLo data for its offers. Among uniformly classify a sweater as “fashion forward” orthe unit’s projects is finding ways to predict shop- “traditional,” for example. They don’t even have clearpers’ Walmart.com purchases on the basis of their and standardized color categories. So retailers mustsocial media interests. Walmart is also looking into spend a lot of time and effort capturing product at-location-based technologies that will help custom- tributes on their own. Zappos has three departmentsers find products in its cavernous stores. The apparel working to optimize customers’ searches and createretailer H&M has partnered with the online game the most effective offers for its shoes. Even whenMyTown to gather and use information on customer the attributes are narrowed down to product type,location. If potential customers are playing the game style, color, brand, and price, a shoe might have anyon a mobile device near an H&M store and check in, of more than 40 material patterns—pearlized, patch-H&M rewards them with virtual clothing and points; work, pebbled, pinstripe, paisley, polka dot, or plaid,if they scan promoted products in the store, it enters to name just those beginning with “p.” Without athem in a sweepstakes. Early results show that of system for such detailed classification of product at-700,000 customers who checked in online, 300,000 tributes, Zappos wouldn’t know that a customer hadwent into the store and scanned an item. often bought paisley in the past, so it wouldn’t know Many retailers focus on how to use customers’ that it should include paisley products in NBOs tolocation information in real time; where the cus- that customer.tomers have been can also reveal a lot about them. Similarly, without good classification systems,In the United States alone, mobile devices send grocers can’t easily determine what products will December 2011 Harvard Business Review 5
  • Spotlight on Reinventing Retail lure adventurous, health-conscious, or penny- customer contact don’t work well because custom- pinching customers. When Tesco wants to identify ers have neither the time nor the inclination to en- products that appeal to adventurous palates, it will gage with them, whereas they might be receptive to start with something that is widely agreed to be a the same offers during a walk-in. Likewise, someone daring choice in a given country—Thai green curry who calls customer service with a complaint is un- paste in the UK, perhaps—and then analyze the other likely to respond to a product offer, though he or she purchases that buyers of the daring choice make. If might welcome it by e-mail at another time. customers who buy curry paste also frequently buy Other contextual factors that may affect the squid or wild rocket (arugula) pesto, these products design of an NBO—and a consumer’s response to have a high relationship coefficient. it—include the weather, the time of day or the day Know the purchase context. Finally, NBOs of the week, and whether a customer is alone or ac- must take into account factors such as the channel companied. Although clickstream or recent online through which a customer is making contact with purchase data are often the most relevant in guiding a business (face-to-face, on the phone, by e-mail, an online NBO strategy, in some cases, such as air- on the web), the reason for contact and its circum- travel ticket pricing, time and day are important: Air- stances, and even voice volume and pitch, indicating lines can hike prices on a Sunday evening, because whether the customer is calm or upset. (Emotion- more people search then than, say, midday during detection software is proving valuable for the last the week. A Chinese shoe retailer we studied is test- factor.) Bank of America has learned that mortgage ing offers that target primary buyers’ companions. offers presented through an ATM at the moment of When a woman walks into one of its stores with herBuilding THE NEXT BEST OFFERExemplary companies build or sharpen an1 2 3 4NBO strategy through four broad activities: Defining Gathering Analyzing and Learning and objectives data executing evolving Craft NBOs to achieve Collect detailed data Use statistical analysis, Think of every offer as a specific goals, such as about customers (demo- predictive modeling, test. Incorporate data on attracting new custom- graphics and psycho- and other tools to match customers’ responses in ers or increasing sales, graphics; purchase his- customers and offers. Use follow-on offers. Formu- loyalty, or share of wallet. tory; social, mobile, and business rules to guide late rules of thumb for Be ready to modify your location information), what offers are made un- designing new offers that objectives to exploit your offerings (product der what circumstances. are based on the perfor- changing circumstances. attributes, profitability, Carefully match offers mance of previous ones. availability), and purchase and channels. Make of- context (customer’s con- fers sparingly, time them tact channel, proximity, deliberately, and monitor the time of day or week). contact frequency.6 Harvard Business Review December 2011  
  • For article reprints call 800-988-0886 or 617-783-7500, or visit hbr.org he weather, the time of day or day of the week, and whether or not a customer is accompanied may affect the design of an offer.husband, she is usually the primary buyer, and the offers a robust overview of key analytical, quantita-retailer’s NBO is usually a relatively inexpensive item tive, and computer modeling techniques.)for the husband. The choice of what to offer him Although such analytics can yield a profusion ofarises from the insight that men who accompany potentially effective offers, business rules govern thetheir wives shopping but are not actively shopping next step. When an analysis shows that a customerthemselves are more price sensitive than solo hus- is equally likely to purchase any of several products,bands who are searching for a specific product. a rule might determine which offer is made. Or it Of course, countless other contextual factors de- might limit the overall contact frequency for a cus-pend on the nature of the business and its customers. tomer if analyses have shown that too much contact reduces response rates. These rules tend to go be-Analyze and Execute yond the logic of predictive models to serve broad The earliest predictive NBOs were created by Ama- strategic goals—such as putting increasing customer zon and other online companies that developed loyalty above maximizing purchases.“people who bought this also bought that” offers A carefully crafted NBO is only as good as its de- based on relatively simple cross-purchase correla- livery. Put another way, a brilliant e-mail NBO that tions; they didn’t depend on substantial knowledge never gets opened might as well not exist. Should of the customer or product attributes, and thus were the NBO be delivered face-to-face? Presented at rather a blunt instrument. Somewhat more targeted an in-store kiosk? Sent to a mobile device? Printed offers are based on a customer’s own past purchase on a register receipt? Often the answer is relatively behavior, but even those are famously indiscrimi- straightforward: The channel through which the nate. If you buy a book or a CD for a friend who customer made contact is the appropriate channel doesn’t share your tastes, that can easily skew the for delivering the NBO. For example, a CVS customer future offers you receive. who scans her ExtraCare loyalty card at an in-store Companies that have systematically gathered in- kiosk can instantly receive customized coupons. formation about their customers, product attributes, There are times, however, when the inbound and and purchase contexts can make much more sophis- outbound channels should differ. A complex offer ticated and effective offers. Statistical analysis and shouldn’t be delivered through a simple channel. predictive modeling can create a treasure trove of Recall Bank of America’s experience with mortgage synthetic data from these raw information sources offers: The inbound channel—the ATM—was quickly to, for example, gauge a customer’s likelihood of found to be a poor outbound channel, because responding to a discounted cross-sell offer deliv- mortgages are just too complicated for that setting. ered on her mobile device. Behavioral segmentation Similarly, many call-center reps don’t understand and other advanced data analytics that simultane- customer needs and product details well enough to ously account for customer demographics, attitudes, make effective offers—particularly when the reps’ buying patterns, and related factors can help iden- primary purpose is to complete simple sales or ser- tify those customers who are most likely to defect. vice transactions. Armed with this information and a customer’s ex- Companies often test offers through multiple pected customer lifetime value, an organization can channels to find the most efficient one. At CVS, Ex- determine whether its NBO to that customer should traCare offers are delivered not only through kiosks encourage or discourage defection. (A detailed dis- but also on register receipts, by e-mail and targeted cussion of marketing data analytics is beyond the circulars, and, recently, via coupons sent directly to scope of this article, but the 2002 book Marketing En- customers’ mobile phones. Qdoba Mexican Grill, a gineering, by Gary L. Lilien and Arvind Rangaswamy, quick-serve franchise, is expanding its loyalty pro- December 2011 Harvard Business Review 7
  • Spotlight on Reinventing Retail For article reprints call 800-988-0886 or 617-783-7500, or visit hbr.org gram by delivering coupons to customers’ smart- Learn and Evolve phones at certain times of the day or week to increase Creating NBOs is an inexact but constantly improv- sales and smooth demand. Late-night campaigns ing science. Like any science, it requires experimen- near universities have seen a nearly 40% redemp- tation. Some offers will work better than others; tion rate, whereas redemption rates average 16% companies must measure the performance of each for Qdoba’s overall program. Starbucks uses at least and apply the resulting lessons. As one CVS execu- 10 online channels to deliver targeted offers, gauge tive said to us, “Think of every offer as a test.” customer satisfaction and reaction, develop prod- Companies can develop rules of thumb from ucts, and enhance brand advocacy. For example, its their NBOs’ performance to guide the creation of fu- smartphone app allows customers to receive tailored ture offers—until new data require a modification of the rules. These rules will differ from one company to the next. In our research we identified some that leading companies use: Footlocker: Promote only fashion-forward shoes through social media. pscale retailers and CVS: Provide discounts on things a customer has bought previously.financial services firms find that Sam’s Club: Provide individually relevant offers for categories in which a customer has not yet pur-a human being is often the best chased, and reward customer loyalty. Nordstrom: Provide offers through sales associ-channel for delivering offers. ates in face-to-face customer interactions. Rules of thumb should be derived from data- driven and fact-based analyses, not convention or promotions for food, drinks, and merchandise based lore. The rules above have been tested, but they will on their SoLoMo information. need to be challenged and retested over time to en- Nordstrom and other upscale retailers, and fi- sure continued effectiveness. nancial services firms with wealthy clients, invest Meanwhile, legal, ethical, and regulatory issues heavily in their salespeople’s product knowledge associated with NBO strategies are evolving fast, as and ability to understand customers’ needs and the collection and use of customer data become in- build relationships. For these businesses, a human creasingly sophisticated. When companies enthusi- being is often the best channel for delivering offers. astically experiment with NBOs, they should be wary Many organizations devise multiple offers and sort of unwittingly crossing legal or ethical boundaries. them according to predictive models that rank a It would be hard for any company to incorporate customer’s propensity to accept them on the basis every possible customer, product, and context vari- of previous purchases or other data. Salespeople or able into an NBO model, but no retailer should fail customer service reps can select from among these to gather basic demographics, psychographics, and offers in real time, guided by their dialogue with the customer purchase histories. Most retailers need to customer, the customer’s perceived appetite for a accelerate their work in this area: Their customers given offer, and even the comfort level between the are not impressed by the quality or the value of of- customer and the salesperson. Combining human fers thus far. Variables and available delivery chan- judgment with predictive models can be more ef- nels will only grow in number; companies that aren’t fective than simply following a model’s recommen- rapidly improving their offers will just fall further dations. For example, insisting that a rep deliver a behind.   HBR Reprint R1112E specific offer in every case may actually reduce both customers’ likelihood of accepting the offer and Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management their postpurchase satisfaction. The investment firm at Babson College, a senior adviser to Deloitte Analytics, and T. Rowe Price provides call-center representatives the research director of the International Institute for Analyt- with targeted offers, but it has concluded that if a rep ics. Leandro Dalle Mule is the global analytics director at Citibank. John Lucker is a principal at Deloitte Consulting delivers the offers in more than 50% of interactions, LLP, where he is a leader of Deloitte Analytics in the U.S. and he or she probably isn’t tuning in to customers’ needs. of advanced analytics and modeling globally.8 Harvard Business Review December 2011