E-Commerce Gets Smarter

Visit the REI flagship store in downtown Seattle for the first time, and you’ll stop in wonder. On the grounds surrounding the store, which spans an entire block of otherwise ordinary urban landscape, a hiking trail and mountain bike test track meander around a waterfall and brook. Inside the main entrance, a 20-meter-tall rock climbing pinnacle looms over shoppers. And on the shelves and display stands that sprawl across two gigantic floors of retail space are stacks of backpacks, hiking boots, canoes, kayaks, tents, jackets, and just about every other outdoor clothing item or accessory you can name. You feel younger, stronger, and more ­adventurous just being here.

On any given day, somewhere between backpacks and winter socks, a man and a woman who are soon to be married will be roaming the aisles. One will be carrying a handheld device about the size of a cell phone and pointing it at something he or she likes. The device is an infrared reader: push a button, and a laser beam reads the bar code of the targeted item. When the reader is synched with a specially equipped cash register, the item is added, instantly, to the couple’s online REI gift registry. Eric Thorson, operations manager at the store, smiles when he thinks about the couples he’s seen. “We have one scanner per couple, and we’ll have the future wife run upstairs to women’s clothing, and [the groom] wants to be downstairs in the climbing department picking out an ice axe,” he says. “It’s almost like it becomes the ultimate shopping adventure for the two of them rather than thinking about what would be a practical wedding gift.” The scanner can record some 300 items, but, Thorson notes, “I’ve seen scanners come back that we have to upload and send back out because they filled the memory.”

It may seem strange, but those couples traversing the aisles—downloading, uploading, and somehow fusing in-store interactions with website maintenance—are the future of e-commerce. Other retailers provide similar scanners, but the resulting Web registries must be manually ­updated. REI is one of those making e-commerce far more interactive—automating updates and using the Web to make registries available to all its stores and business channels.

The benefits for REI customers are real. Any customer can view the registry, either at an in-store kiosk or online. And if an item is purchased—whether through mail order, over the phone, on the Internet, or in any of REI’s 77 stores—the list is instantly updated at all those locations. Customers can buy online but decide to pick up or return at a store. Discounts are the same in all locations, and every item offered on the Web can be ordered through the store or catalogue, and vice versa.

The business jargon for this model of integrated retail sales is “multichanneling”—that is, fusing digital services with in-store, mail-order, and telephone sales, and with any other retail channels. The dige­rati have called it “clicks and mortar” since the Internet boom of the 1990s. No matter the term, it is now the driving force in retail. For while the Internet works fine for some types of goods—such as books, computer products, and music—many shoppers don’t want to purchase and pay shipping costs for things like canoes, cars, clothes, and entertainment systems without trying them out, trying them on, touching them, or maybe even talking to a knowledgeable salesperson.

New technologies and ideas are allowing retailers to remove the wall between online shopping and in-store shopping, and to make the gathering of customer data both easier and more valuable. Advanced data-mining and Web analytics techniques now examine not just what you bought online but what you viewed, helping retailers design promotions that will entice you to shop online and in stores. These enticements will themselves arrive over multiple channels—through magazines, regular mail, e-mail, the Web, and wireless transmissions to your car or shopping cart. By looking at just a few of a customer’s purchases, a retailer will even be able to predict how much she’ll spend over her lifetime, and adjust the deals and promotions it offers her accordingly.

The ultimate goal is more-customized, personal service. The best retailers have always striven to provide the most-­tailored service possible; however, as more and more retailers expand nationally and even internationally, building close relationships with customers is increasingly difficult. “Retailers can’t do that now because they have millions of customers all over the country,” says Dan Hopping, senior consulting manager for IBM’s Retail Store Solutions Division. “So they use technology to make the connection.”

REI Speedwagon

The sales figures for 2004 are in, and e-commerce is on a roll. Online retail spending soared 26 percent last year, to $66.5 billion, according to business analysis and advisory firm Jupiter Research. That’s 4 percent of total retail spending—compared with nothing about 10 years ago, and with 3 percent in 2003. By 2009, Jupiter predicts, online spending will reach 6 percent of total retail sales.

But that’s just a small part of the e-commerce story. Last year, another $355 billion in retail sales took place in physical stores after consumers had done their homework online. Overall, says Jupiter, for every $1 consumers spend online, they spend $6 dollars offline as a result of research conducted on the Internet.

That’s why retailers want to find better ways to exploit the many ways in which people shop, so that customers research and buy from them, not their competitors. “It’s a leaders-and-laggards thing,” says Jonathan Reynolds, director of the Oxford Institute of Retail Management at the University of Oxford. “In nearly every country, you’ve got one or two particular companies that are ahead of the game. The message to the laggards is, you better have a good story or else risk losing market share to those firms who are setting the multichannel standard.”

Few companies are better at such integration than REI. Case in point: in June 2003, the company began offering customers the option of ordering products online and picking them up at stores. The concept grew out of an examination of the in-store Web kiosks that REI began using in 1998. The kiosks had proven a good source of product information to supplement what the sales staff could provide, but customers also used the kiosks to place orders when stores didn’t have items they wanted—which meant they would have to pay shipping costs for the goods they had just come into the stores to buy. Says Joan Broughton, REI’s vice president of multichannel programs, “You don’t want people to feel penalized by the fact the store doesn’t happen to carry that item they’re looking for.”

Providing in-store pickup seemed a good way to minimize that frustration, and also to serve other online customers leery about paying to have, say, canoes delivered to their doors. Still, REI trod cautiously. The program wasn’t advertised, so shoppers found out about it only when it came time for checkout on the REI website: in-store pickup was offered alongside shipping options. REI had 66 stores at the time. On the first day, 60 of those stores received pickup orders. Today, such orders are trucked out of REI’s central warehouse on distinctively colored pallets and are packaged in special dot-com wrapping, so that when a shipment arrives at a store, employees can easily tell what should be held for customer pickup.

When an item comes in, its bar code is scanned to register its arrival. An e-mail notification is sent to the buyer. During a normal week, 600 products ordered online come into REI’s flagship store. Over the holidays, says Thorson, the number is four times that. That represents $2.2 million, about 4 percent of annual store sales. But online customers who choose to pick up their orders in stores spend an average of $30 more once inside.

The principle behind REI’s approach—understand what people want and use technology to make shopping easier— is recognized by retailers worldwide. Change, however, has come slowly. Many companies set up online stores in the mid- to late 1990s, often building proprietary systems that were not integrated with other parts of their operations. Later, harmonizing operations seemed expensive and difficult. It’s only since the economy has improved that some retail executives have been investing more heavily in integrating their sales channels.

In the labs and strategy rooms where the next generation of e-commerce is being shaped, firms are looking at new ways to use technology to become more profitable. Here’s a look at what’s in the works.

Check Out the Supermarket

Five years ago, online shopping was something you did from a home or office PC. You didn’t expect to find the future of e-commerce in the aisles of your neighborhood supermarket. Indeed, dot-com upstarts such as Netgrocer, Peapod, and Webvan—all of which delivered goods ordered online to people’s doors—aimed to put a serious hurt on their physical-store counterparts, not to work with them.

Now, a different approach to supermarket e-commerce is emerging. The most successful retailer in the United Kingdom is the Tesco supermarket chain. An estimated one of every eight pounds spent in Britain on retail, whether in stores or online, goes to Tesco, says Reynolds.

In contrast with companies such as the now defunct Webvan, which supplied ­online orders from central warehouses, Tesco services Internet orders in its stores. This arrangement is extremely profitable, because it builds on spare capacity within the store network, and orders are filled by store staff during quiet periods. Whether customers purchase online or in stores, the data about what they buy is linked to Tesco’s loyalty card, so the company “knows who you are irrespective of the channel you come in on,” Reynolds says. If you log onto the website through a home computer or PDA, it lists your favorite or recently purchased items—whether you bought them in a store or online.

In this manner, Tesco has amassed a mountain of data about its customers, which it uses in various ways. Regular-mail statements to all loyalty card customers include quasi-personalized coupons tailored to their buying habits. Some coupons might provide discounts on products a customer has recently purchased. Others offer discounts enticing customers to try new items Tesco thinks they might like. In addition, Tesco puts out five editions of a quarterly hard-copy “magazine,” each of them tailored to a broad audience segment: students; young adults without dependents; young families with children; people age 40 to 60; and those over 60. ­Finally, the retailer offers a number of further segmentations, or clubs—World of Wine, Baby and Toddler, and so on—that customers choose to join, and which enable even more precise delivery of promotional offers.

A farther-out approach—bringing e-commerce to the supermarket shopping cart—is being tested by Stop and Shop of Quincy, MA, which operates 350 supermarkets in the Northeast. Dubbed Shopping Buddy, the technology consists of a wireless computer and data management system developed by IBM in partnership with the supermarket chain and software maker Cuesol, also of Quincy.

The paperback-book-sized device, introduced early last year at three stores near Boston, is installed in shopping cart handles. To use it, a shopper scans in his loyalty card; a simple graphical interface then appears, displaying such features as sale items and a customer favorites list. On the favorites list are the names of the things the shopper buys most frequently, whether he buys them in the store or has them delivered to his house by Peapod—which, in a neat post-bubble twist, Stop and Shop’s parent company now owns. The device creates a map of the store and displays a suggested route. Infrared beacons on the ceiling track the cart’s location, so the device can automatically alert the customer if any of his favorite items are on sale in the aisle he is currently browsing. The interface also lets the shopper wirelessly order cold cuts from the deli; an alert sounds when they are ready. Finally, an attached imaging scanner lets the shopper scan items as he puts them in the cart; as the cart fills, a running total is displayed. When it comes time for checkout, the cashier scans the shopper’s loyalty card, and all of the items in the cart are listed on the register screen. This saves time for both the shopper and the cashier.

Stop and Shop is expanding the program to 20 more stores in Massachusetts and Connecticut, says marketing director Peg Merzbacher. The company is also working on new features—one of which will allow customers to create online shopping lists that will automatically appear on the Shopping Buddy when they arrive at a store. Merzbacher says integrating physical-store presence with Peapod helps both businesses by adding convenience and building loyalty. “Hardly anybody converts to total online shopping,” she says. “They go back and forth, [and] when you get people to use both channels, they spend more.” IBM is also pushing the limits of the Shopping Buddy tech­nology, hoping to better tailor advertisements and promotions, and generally improve the shopping experience. Rakesh Mohan, senior manager of IBM Research’s Industry Solutions group, says there’s no reason such a device can’t suggest a wine to go with a meal or provide dietary guidance by reporting an item’s fat or carbohydrate content. It could even sound a warning if a product that a shopper scanned contained ingredients to which he or she was allergic.

This technology could do for in-store advertising what Google did for online advertising. As search technology has ­improved, Web-based advertising has evolved to include paid contextual advertisements linked to search terms. If, for instance, you use Google to search for digital cameras, paid advertisements from camera makers will probably appear on the right-hand side of the Google page. Similarly, Mohan says, when a shopper is in a supermarket’s laundry detergent section, that’s the time for detergent ads to appear on the shopping cart screen. “It’s really bringing the Google-type activities into the physical environment,” says Mohan.

But is that something we really want? Mohan thinks it is. He argues that these new ads will not feel intrusive, because they will be directly related to what the customer is doing at any given moment. What’s more, they have the potential to be far more effective than online ads, because they can be tailored to the person’s buying history. And best of all, Mohan adds, they simply appear on-screen without the shopper’s ever having to click a mouse. Nor is their application limited to supermarkets; IBM believes such services will be attractive to any retailer.

Gleanings

IBM’s Industry Solutions group building in Hawthorne, NY, where Mohan works, houses a big customer demonstration area showcasing new concepts and technolo­gies, many of them aimed at e-commerce. A telematics demo shows how directions, traffic alerts, and promotions can be sent over the Internet to cars, based on their Global Positioning System location. And all around are systems for payment and authentication—from a cryptographic chip for wireless transactions to conversational biometrics, which perform voiceprint analysis while asking questions only authorized users should be able to answer.

The e-commerce technologies pursued here, and at other labs worldwide, cover an ever expanding range of areas. However, underlying virtually all the personalization and customization efforts are Web analytics and data mining. “We are paying close attention to the tendency of shoppers to visit our online store and then, within a week or so, come in and buy at our retail stores,” says REI’s Broughton. “We want to get better at providing our online customers whatever they need—product information, store locations, articles about the activities they’d like to enjoy—so that they shop our retail stores as well.”

Even when customers don’t hand retailers detailed information about themselves, a lot can be gleaned from what they do online. For nstance, geolocation and data-mining company Digital Envoy of Norcross, GA, tracks two billion Internet addresses a day, culling demographic data that advertisers and retailers love. Working like a search engine that studies the Web’s infrastructure rather than its content, the firm’s system can track an Internet transaction backward from a website to the network node at which it originated in order to answer two questions: what city is the user in, and how fast is her In­ternet connection? From that information, the system can make a good guess about what business the user is in. Digital Envoy can also identify a person’s local area code, time zone, and zip code—and determine what language she speaks.

The hunger for such information is growing fast, in large part because of e-commerce, notes Digital Envoy cofounder Sanjay Parekh. For one thing, the information improves fraud detection. If a buyer claims to be in Florida, but his Internet address shows he’s in Wisconsin, that’s a tip-off that something is amiss. Even more to the point, Parekh notes, if a retailer knows that a customer is in New York, not Palm Springs, it might display a different style of clothes on its home page—or dispatch a coupon good at a store in Manhattan. But the biggest force driving data mining is the push to provide better context for the paid keyword ads linked to search terms. Online marketing and advertising service provider DoubleClick, for instance, uses Digital Envoy’s technology to help companies create ads that are location specific. The goal, says Parekh: “Drive to a sale much quicker. That’s what everything is about.”

IBM researchers tackle many of the same issues, though differently. One of the company’s projects involves recording a customer’s mouse clicks and tracking what was viewed—a red blouse, say. From that data, it’s possible to get a good idea of a visitor’s feelings about price, color, and size preferences—even his or her gender. If the shopper makes a second visit, the retailer might offer a discount on an item already examined, or something similar. Every purchase gives the store more information about its customer.

Such efforts, of course, raise privacy concerns. In 2001, Big Blue founded the IBM Privacy Management Council, a coalition of privacy and security leaders from health care, finance, retail, and government that seeks to find ways—through technology, standards setting, and business practices—to get ahead of looming privacy issues. A big push is related to database management—so that when you enter personal information like your name, income, and tastes in lingerie into a company database, it is stored in such a way that none of the company’s employees can put all the pieces together and trace them to you. Instead, customers are profiled in the aggregate and grouped into broader categories that allow retailers to tailor (and even personalize) offers to people as members of a class but not as specific individuals. Privacy concerns are at the top of the list for retailers, says Hopping, of IBM’s Retail Store Solutions Division, “because if privacy blows up, that’s the kiss of death for a retailer.”

Retailers must also make the gathering of information about shoppers worthwhile to the shoppers themselves. “To get the customer to opt in, the retailer’s going to have to give them something,” says Hopping. That can be a discount, he says, but often it is something else—special parking or other services, or a piece of technology like Shopping Buddy that makes shopping easier.

One of the most intriguing areas of research involves figuring out which customers are worth the trouble of wooing in the first place. In its Haifa, Israel, research lab, IBM is designing advanced statistical and machine-learning models that will differentiate customers according to their future value.

Researcher Amit Fisher developed one such model by studying a year’s worth of ac­tivity at one of Israel’s leading e-auction sites. From such factors as the number, frequency, and value of their transactions, Fisher was able to classify Internet users into different categories—along the lines of bargain hunter, repeater, one-timer, defector, valuable customer—and assign an economic value to each category. The model then sought to predict from just a few visits where a new user was likely to end up a year or more down the road. “We compared the customer ranking that was generated by our model to the true ranking of the customer according to their purchases,” says Fisher. In trials involving groups of more than 1,000 users, he notes, the model correlated almost perfectly with actual data collected from the auction site. Programs that seek to assess a customer’s lifetime value are not new; however, IBM says that Fisher’s model, which is being developed for commercial use through several of IBM’s businesses, is the first to make an accurate assessment of a customer’s future value based on just a few visits. What’s more, such a model is “domain adjustable,” Fisher says. It could be used in banking to determine whether to issue a loan or a credit card. Or it could be employed by retailers to target promotions to potential best customers and give priority to those customers during times of peak demand.

Fisher’s model works with minimal personal data and takes into account only a few variables. But for retailers bent on amassing much more complete data about their customers—and then using that information to “maximize” lifetime customer value through highly targeted ads and promotions—the data-mining challenge is far trickier, says Edwin Pednault, a staff researcher in IBM’s data analytics research group. “Now I want to take much more information into account,” says ­Pednault—who has been working on a model that would do just that.

Instead of looking at effects of marketing campaigns separately, as traditional data mining has done, Pednault’s model examines the patterns of a customer’s activity, such as the types of products she likes, how she responds to promotions, and her price sensitivity. When a company has that kind of information about its customers, says Pednault, it can begin to ask, “How are my actions motivating them to change from one [buying] state to another?” In studies of one major department store chain, IBM showed that using Pednault’s model to predict the effects of snail mail marketing—alerting customers about sales, store events, and new items from their favorite product lines or styles—resulted in a 7 to 8 percent increase in store revenues.

Self-Control

Some of what customers want can be deduced from their activity alone, but when a store can get people to willingly tell it what they are seeking, its returns can be even better. “You can do it passively, but if you have to buy in, that gives extra value,” says Pednault.

That’s a key point. In the end, says Oxford’s Reynolds, whether companies successfully adapt to the changing face of e-commerce will depend on how well they employ new technologies to go beyond personalization to customization, which means letting their clients shape their own profiles and classifications. “Personalization is what companies do to us,” Reynolds explains. “Customization is what we want to do.”