What Should You Know About Your Customers
Reprint: R1112E Shoppers in one case relied on familiar salespeople to help them discover exactly what they wanted—and sometimes to suggest additional items they hadn't fifty-fifty thought of. But today's distracted consumers, bombarded with data and options, often struggle to detect products or services that meet their needs. Advances in information technology, information gathering, and analytics are making it possible to evangelize something like the personal advice of yesterday'south sales staffs. Using increasingly granular customer data, businesses are starting to create highly customized offers that steer shoppers to the "correct" merchandise—at the right moment, at the right price, and in the right channel. But few companies can do this well. The authors demonstrate how retailers can hone their "side by side all-time offering" (NBO) capability by breaking the problem down into four steps: defining objectives, gathering data (about your customers, your products, and the purchase context), analyzing and executing, and learning and evolving. Citing successful strategies in companies such equally Tesco, Zappos, Microsoft, and Walmart, they provide a framework for nailing the NBO.
Idea in Brief
Targeting individuals with perfectly customized offers at the correct moment across the right channel is marketing's holy grail. As companies' ability to capture and analyze highly granular customer information 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 information 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'due south difficult to perfect all four steps at once, but progress on each is essential to competitiveness. As the corporeality of data that tin be captured grows and the number of channels for interaction proliferates, companies that are not rapidly improving their offers will just fall farther behind.
Photography: Rachel Perry Welty and Yancey Richardson Gallery, NY
Artwork: Rachel Perry Welty, Lost in My Life (Playmobil), 2010, pigment print
Shoppers once relied on a familiar salesperson—such as the proprietor of their neighborhood general store—to aid them find just what they wanted. Cartoon on what he knew or could rapidly deduce about the customer, he would locate the perfect product and, oft, advise boosted items the client hadn't even thought of. It'due south a quaint scenario. Today's distracted consumers, bombarded with data and options, often struggle to discover the products or services that will best meet their needs. The shorthanded and oft poorly informed floor staff at many retailing sites can't begin to replicate the personal touch that shoppers once depended on—and consumers are notwithstanding largely on their ain when they shop online.
This distressing state of affairs is changing. Advances in it, information gathering, and analytics are making it possible to deliver something like—or mayhap fifty-fifty 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 correct aqueduct. These are called "next best offers."Consider Microsoft's success with east-mail offers for its search engine Bing. Those due east‑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-fourth dimension information virtually him or her: information including location, age, gender, and online activity both historical and immediately preceding, forth with the nearly recent responses of other customers. These ads take lifted conversion rates by as much as 70%—dramatically more than than similar only uncustomized marketing efforts.
The technologies and strategies for crafting next all-time offers are evolving, but businesses that expect to exploit them will run into their customers defect to competitors that take the atomic number 82. Microsoft is only 1 case; 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 plant that if NBOs are washed at all, they're often done poorly. Well-nigh are indiscriminate or ill-targeted—pitches to customers who have already bought the offer, for instance. One retail depository financial institution discovered that its NBOs were more likely to create ill will than to increase sales.
Companies tin pursue myriad good goals using client analytics, but NBO programs provide mayhap 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, only progress on each will be necessary at some indicate to improve your offers.
Exemplary companies build or sharpen an NBO strategy through 4 broad activities:
1. Defining objectives
Craft NBOs to achieve specific goals, such every bit alluring new customers or increasing sales, loyalty, or share of wallet. Exist ready to modify your objectives to exploit irresolute circumstances.
2. Gathering data
Collect detailed data about customers (demo- graphics and psychographics; buy history; social, mobile, and location information), your offerings (product attributes, profitability, availability), and purchase context (customer's contact channel, proximity, the fourth dimension of mean solar day or week).
iii. Analyzing and executing
Employ statistical analysis, predictive modeling, and other tools to match customers and offers. Utilize business concern rules to guide what offers are made under what circumstances. Advisedly match offers and channels. Make offers sparingly, time them deliberately, and monitor contact frequency.
4. Learning and evolving
Think of every offering as a test. Incorporate data on customers' responses in follow-on offers. Formulate rules of thumb for designing new offers that are based on the functioning of previous ones.
Define Objectives
Many organizations flounder in their NBO efforts not because they lack analytics capability but considering they lack clear objectives. So the first question is, What do you lot 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 plan. 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 diverseness of store formats, from hypermarts to neighborhood shops. For example, Clubcard shoppers who buy diapers for the showtime time at a Tesco store are mailed coupons not only for baby wipes and toys but also for beer. (Information 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 equally much as triple the redemption value of certain Clubcard coupons—in essence making its best offering fifty-fifty ameliorate for selected customers. A countdown machinery shows how quickly time or products are running out, building tension and driving responses. Some of these offers take sold out in 90 minutes.
Tesco's NBO strategy seeks to expand the range of customers' purchases, merely it as well targets regular customers with deals on products they ordinarily buy. As a result of its carefully crafted, creatively executed offers, Tesco and its in-house consultant dunnhumby reach redemption rates ranging from viii% to 14%—far higher than the one% or 2% seen elsewhere in the grocery manufacture. Microsoft had a very unlike set of objectives for its Bing NBO: getting new customers to effort the service, download information technology 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 nigh modifying it as needed. The low-toll DVD rental visitor Redbox initially made eastward-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 depression-cost model, they needed to persuade customers to rent more than one DVD per visit. So they shifted the accent of their NBO strategy from alluring new customers to discounting multiple rentals.
Gather Data
To create an constructive NBO, you must collect and integrate detailed data about your customers, your offerings, and the circumstances in which purchases are made.
Know your customers.
Information valuable for tailoring NBOs can be relatively basic and easily caused or derived: age, gender, number of children, residential accost, income or avails, and psychographic lifestyle and behavior information. Previous purchases are frequently the single best guide to what a customer volition buy side by side, merely that information may be harder to capture, particularly from offline channels. Loyalty programs like Tesco's can be a powerful tool for tracking consumers' buying patterns.
Even as companies work (and sometimes struggle) to acquire these familiar kinds of customer data, the growing availability of social, mobile, and location (SoMoLo) information creates major new data sets to be mined. Companies are beginning to craft offers based on where a customer is at whatever given moment, what his social media posts say about his interests, and fifty-fifty what his friends are buying or discussing online.
One case is Square, which makes customized offers according to how many times consumers have "checked in" to a sure retail store. Another is Walmart, which acquired the social media applied science first-up Kosmix to join its newly formed digital strategy unit, @WalmartLabs, in capitalizing on consumer SoMoLo data for its offers. Among the unit'southward projects is finding ways to predict shoppers' Walmart.com purchases on the ground of their social media interests. Walmart is as well looking into location-based technologies that will assist customers find products in its cavernous stores. The apparel retailer H&1000 has partnered with the online game MyTown to gather and use information on customer location. If potential customers are playing the game on a mobile device virtually an H&M shop and check in, H&G rewards them with virtual clothing and points; if they browse promoted products in the store, it enters them in a sweepstakes. Early results show that of 700,000 customers who checked in online, 300,000 went into the shop and scanned an item.
Many retailers focus on how to use customers' location information in existent time; where the customers have been tin also reveal a lot about them. In the U.s.a. lonely, mobile devices transport near 600 billion geospatially tagged information feeds back to telecommunications providers every twenty-four hour period. An application adult by the software analytics visitor Sense Networks tin can compare a consumer's movements with billions of data points on the movements and attributes of others. Using this location history, it tin can estimate the consumer's historic period, travel mode, level of wealth, and next likely location, among other things. The implications for creating highly customized NBOs are clear.
Know your offerings.
Unless a visitor has detailed information most its own products or services, it volition accept trouble determining which offerings might appeal almost to a client. For some products, such as movies, tertiary-party databases supply production attributes, and companies that rent or sell movies tin can surmise that if yous liked one movie with a particular histrion or plot blazon, you will probably like another. But in other retail industries, such as apparel and groceries, compiling product attributes is much more than hard. Manufacturers don't uniformly classify a sweater as "fashion frontward" or "traditional," for example. They don't even have clear and standardized color categories. So retailers must spend a lot of time and effort capturing product attributes on their ain. Zappos has three departments working to optimize customers' searches and create the most effective offers for its shoes. Even when the attributes are narrowed down to product type, style, color, make, and price, a shoe might have whatsoever of more than 40 cloth patterns—pearlized, patchwork, pebbled, pinstripe, paisley, polka dot, or plaid, to name just those beginning with "p." Without a organisation for such detailed classification of production attributes, Zappos wouldn't know that a customer had often bought paisley in the past, then it wouldn't know that it should include paisley products in NBOs to that customer.
Similarly, without good classification systems, grocers can't hands make up one's mind what products volition lure audacious, health-conscious, or penny-pinching customers. When Tesco wants to identify products that entreatment to adventurous palates, it will start with something that is widely agreed to be a daring choice in a given country—Thai light-green curry paste in the UK, perhaps—then clarify the other purchases that buyers of the daring choice brand. If customers who buy curry paste also frequently purchase squid or wild rocket (arugula) pesto, these products have a high relationship coefficient.
Know the buy context.
Finally, NBOs must accept into account factors such as the channel through which a customer is making contact with a business organization (confront-to-face, on the phone, by email, on the web), the reason for contact and its circumstances, and fifty-fifty phonation volume and pitch, indicating whether the customer is calm or upset. (Emotion-detection software is proving valuable for the last factor.) Banking company of America has learned that mortgage offers presented through an ATM at the moment of customer contact don't work well because customers have neither the time nor the inclination to engage with them, whereas they might be receptive to the aforementioned offers during a walk-in. Also, someone who calls customer service with a complaint is unlikely to answer to a product offer, though he or she might welcome it by electronic mail at another time.
The weather, the time of day or twenty-four hours of the week, and whether or not a client is accompanied may affect the design of an offer.
Other contextual factors that may affect the design of an NBO—and a consumer'southward response to information technology—include the weather, the time of mean solar day or the mean solar day of the week, and whether a customer is lonely or accompanied. Although clickstream or recent online buy data are oftentimes the almost relevant in guiding an online NBO strategy, in some cases, such as air-travel ticket pricing, time and 24-hour interval are important: Airlines can hike prices on a Sunday evening, because more people search then than, say, midday during the week. A Chinese shoe retailer we studied is testing offers that target principal buyers' companions. When a woman walks into one of its stores with her married man, she is usually the primary buyer, and the retailer'due south NBO is usually a relatively inexpensive particular for the husband. The option of what to offer him arises from the insight that men who accompany their wives shopping just are not actively shopping themselves are more price sensitive than solo husbands who are searching for a specific production.
Of course, countless other contextual factors depend on the nature of the business and its customers.
Analyze and Execute
The earliest predictive NBOs were created by Amazon and other online companies that developed "people who bought this also bought that" offers based on relatively simple cross-purchase correlations; they didn't depend on substantial knowledge of the customer or product attributes, and thus were rather a blunt musical instrument. Somewhat more targeted offers are based on a customer's own by buy beliefs, but even those are famously indiscriminate. If yous buy a book or a CD for a friend who doesn't share your tastes, that can easily skew the future offers you receive.
Companies that have systematically gathered data about their customers, product attributes, and purchase contexts can make much more sophisticated and constructive offers. Statistical analysis and predictive modeling tin create a treasure trove of synthetic data from these raw data sources to, for example, gauge a client'south likelihood of responding to a discounted cross-sell offer delivered on her mobile device. Behavioral sectionalization and other advanced data analytics that simultaneously business relationship for customer demographics, attitudes, buying patterns, and related factors can help identify those customers who are most likely to defect. Armed with this data and a customer'due south expected customer lifetime value, an system tin determine whether its NBO to that client should encourage or discourage defection. (A detailed discussion of marketing data analytics is beyond the scope of this article, simply the 2002 book Marketing Engineering, by Gary L. Lilien and Arvind Rangaswamy, offers a robust overview of key analytical, quantitative, and calculator modeling techniques.)
Although such analytics can yield a profusion of potentially constructive offers, business rules govern the adjacent step. When an analysis shows that a customer is as likely to buy any of several products, a rule might decide which offering is made. Or it might limit the overall contact frequency for a customer if analyses take shown that also much contact reduces response rates. These rules tend to get beyond the logic of predictive models to serve wide strategic goals—such as putting increasing client loyalty higher up maximizing purchases.
A carefully crafted NBO is only as proficient as its commitment. Put another way, a brilliant e-post NBO that never gets opened might as well not exist. Should the NBO be delivered face-to-confront? Presented at an in-store kiosk? Sent to a mobile device? Printed on a register receipt? Frequently the answer is relatively straightforward: The channel through which the customer made contact is the appropriate channel for delivering the NBO. For example, a CVS client who scans her ExtraCare loyalty menu at an in-store kiosk tin instantly receive customized coupons.
At that place are times, yet, when the entering and outbound channels should differ. A complex offer shouldn't exist delivered through a elementary channel. Call back Bank of America's experience with mortgage offers: The entering channel—the ATM—was rapidly found to be a poor outbound channel, because mortgages are simply besides complicated for that setting. Similarly, many call-center reps don't understand client needs and product details well enough to make constructive offers—particularly when the reps' chief purpose is to consummate elementary sales or service transactions.
Companies often examination offers through multiple channels to discover the nigh efficient 1. At CVS, ExtraCare offers are delivered not just through kiosks merely also on register receipts, by e-postal service and targeted circulars, and, recently, via coupons sent directly to customers' mobile phones. Qdoba Mexican Grill, a quick-serve franchise, is expanding its loyalty plan by delivering coupons to customers' smartphones at certain times of the solar day or week to increment sales and smooth need. Belatedly-night campaigns near universities have seen a nearly twoscore% redemption charge per unit, whereas redemption rates boilerplate 16% for Qdoba's overall program. Starbucks uses at to the lowest degree 10 online channels to deliver targeted offers, gauge client satisfaction and reaction, develop products, and enhance brand advocacy. For example, its smartphone app allows customers to receive tailored promotions for food, drinks, and merchandise based on their SoLoMo information.
Upscale retailers and financial services firms find that a human being is ofttimes the best channel for delivering offers.
Nordstrom and other upscale retailers, and fiscal services firms with wealthy clients, invest heavily in their salespeople's product knowledge and ability to sympathize customers' needs and build relationships. For these businesses, a human being is often the best channel for delivering offers. Many organizations devise multiple offers and sort them according to predictive models that rank a customer's propensity to accept them on the footing of previous purchases or other data. Salespeople or client service reps tin can select from among these offers in real time, guided by their dialogue with the customer, the customer'southward perceived appetite for a given offering, and even the comfort level between the customer and the salesperson. Combining human judgment with predictive models can be more effective than just following a model's recommendations. For case, insisting that a rep deliver a specific offer in every case may actually reduce both customers' likelihood of accepting the offer and their postpurchase satisfaction. The investment house T. Rowe Cost provides phone call-centre representatives with targeted offers, but it has ended that if a rep delivers the offers in more than 50% of interactions, he or she probably isn't tuning in to customers' needs.
Learn and Evolve
Creating NBOs is an inexact but constantly improving scientific discipline. Like any science, it requires experimentation. Some offers will piece of work better than others; companies must measure the performance of each and apply the resulting lessons. As 1 CVS executive said to us, "Think of every offer as a test."
Companies tin develop rules of thumb from their NBOs' performance to guide the creation of future offers—until new information require a modification of the rules. These rules volition differ from i visitor to the side by side. In our inquiry we identified some that leading companies utilise:
Footlocker:
Promote only manner-forrard shoes through social media.
CVS:
Provide discounts on things a client has bought previously.
Sam'south Club:
Provide individually relevant offers for categories in which a customer has not yet purchased, and reward customer loyalty.
Nordstrom:
Provide offers through sales associates in contiguous customer interactions.
Rules of thumb should be derived from data-driven and fact-based analyses, not convention or lore. The rules above have been tested, but they will need to be challenged and retested over time to ensure continued effectiveness.
Meanwhile, legal, ethical, and regulatory issues associated with NBO strategies are evolving fast, as the collection and use of customer data get increasingly sophisticated. When companies enthusiastically experiment with NBOs, they should be wary of unwittingly crossing legal or ethical boundaries.
It would be hard for any company to incorporate every possible customer, product, and context variable into an NBO model, just no retailer should fail to gather basic demographics, psychographics, and customer buy histories. Almost retailers need to accelerate their piece of work in this area: Their customers are not impressed by the quality or the value of offers thus far. Variables and available delivery channels will just abound in number; companies that aren't rapidly improving their offers will just autumn further behind.
A version of this article appeared in the December 2011 effect of Harvard Business Review.
Source: https://hbr.org/2011/12/know-what-your-customers-want-before-they-do
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