As for the forms our trackers use? They’re also marvels of data gathering. They have evolved constantly over the three decades we’ve been doing this research and are, without a doubt, the key to the entire enterprise, a great achievement, if I may say so, in the art of information storage and retrieval, nondigital division. We have tried scanning systems, exotic software packages…and we keep going back to the same old system. It works, it’s flexible, and thanks to Wite-Out and a copy machine, it can be changed on a dime and on the fly. Our ability to react to what and whom we find walking through the door of wherever location we go is critical to our success. I’d guess that at least one third of the time we go on location, we end up finding something very different than what our client told us we’d find. The store has six aisles and not seven, the shelf layout has been mysteriously reversed or that interactive machine we were hired to study arrived at the store nearly a month ago and hasn’t worked since.
Our earliest track sheets were able to record maybe ten different variables of shopper behavior. Today we’re up to around forty. The form is reinvented for every research project we undertake, but typically it starts with a detailed map depicting the premises we’re about to study, whether it’s a store, a bank branch, a parking lot (for a drive-thru project) or just a single section—even just one aisle—of a store. The map shows every doorway and aisle, every display, every shelf and rack and table and counter. Also on the form is space for information about the shopper (sex, race, estimate of age, description of attire) and what he or she does in the store. Using the system of shorthand notation we’ve developed over the years, a combination of symbols, letters and hash marks, a tracker can record, for instance, that a bald, bearded man in a red sweater and blue jeans entered a department store on a Saturday at 11:07 a.m., walked directly to a first-floor display of wallets, picked up or otherwise touched a total of twelve of them, checked the price tag on four, then chose one, and moved at 11:16 to a nearby tie rack, stroked seven ties, read the contents tags on all seven, read the price on two, then bought none and went directly to the cashier to pay. Oh, wait, he paused for a moment at a mannequin and examined the price tag on the jacket it wore. We’d mark that down, too, just as we’d note that he (the man, not the mannequin) entered the cashier line at 11:23 and exited the store at 11:30. Depending on the size of the store and the length of the typical shopper’s stay, a tracker can study up to fifty shoppers a day. Usually we’ll have several trackers at a site, and a single project may involve the simultaneous study of three or four locations. For huge stores like a home improvement center or a mass merchandiser, we may put ten or twelve trackers on the floor.
By the end of a job, an incredible amount of information has been crammed onto those sheets. They come back to the office, where an experienced clerk spends another day or so typing all the information, every single notation on every track sheet, into a computerized database. Over the years, we’ve spent tens of thousands of dollars and countless frustrating hours with computer programmers, trying to come up with a database that could handle the kind of work we do. The big problem is that while we crunch the same numbers in the same ways from job to job, each project usually requires us to do something a little differently—to collect different kinds of data or to devise new comparisons of facts we just uncovered. We’ve hired fancy consultants who spend six months at a crack with us, trying to build us a computer system. They ask us to list everything we want our program to do, but every week we add six new things to the list that negate all their work from the previous month. And of course, our turnaround time has to be swift, so there’s no time to change the system completely for each job—we may need to do one new comparison for a project today and then not have to perform that function again for seven months.
In the early ’90s, Microsoft Excel came along. Where had it been all my life? It was designed as a spreadsheet program, intended for accountants to do the relatively simple calculations they require. But Excel’s beauty was its open architecture—you could get in there under the hood and tinker, soup it up, make it purr. It also had a fairly simple way of writing macros, or lines of code, that allowed you to make the alterations easily. Today, while we still use Excel, we’ve moved on to other programs like Access and SPSS—but for years, Excel made our work possible. It’s as though Microsoft built a very nice bicycle, which we then turned into a data-busting all-terrain vehicle. When Microsoft became a client and we showed them what we’d done with Excel, they were amazed.
When the videotapes come back from the sites, it’s someone else’s job to screen every bit of footage. Depending on the size of the store, we may have ten cameras running eight hours a day trained on specific areas—a doorway, for example, or a particular shelf of products. The video produces even more hard data. If, for example, a client wants us to determine in part how a particular cash register design affects worker fatigue, we may use the video and a stopwatch to time how long it takes for a clerk to ring up a sale at ten a.m. as compared to four p.m.
The list of particulars we’re capable of studying—what we call the “deliverables”—grows with every new project we take on. At last count, we’ve measured close to a thousand different aspects of shopper-store interaction. As a result of all that, we know quite a few facts about how human beings behave in stores. We can tell you how many males who take jeans into the fitting room will buy them compared to how many females will (65 percent to 25 percent). We can tell you how many people in an IBM employee cafeteria read the nutritional information on a bag of corn chips before buying (18 percent) compared to those lunching at Subway (2 percent). Or how many browsers actually buy computers on a Saturday before noon (4 percent) as opposed to after five p.m. (21 percent). Or how many shoppers in a mall housewares store use shopping baskets (8 percent), and how many of those who take baskets actually buy something (75 percent) compared to those who buy without using baskets (34 percent). And then, of course, we draw on all we’ve learned in the past to suggest ways of increasing the number of shoppers who take baskets, for the science of shopping is, if it is anything, a highly practical discipline concerned with using research, comparison and analysis to make stores and products more amenable to shoppers.
Because this science is being invented as we go along, it’s a living, breathing field of study—meaning we never quite know what we’ll find until we find it, and even then, we sometimes have to stop to figure out what it is we’re seeing. Yes, for a lot of work now, after more than thirty years we have a good sense of what we are going to find, but what makes the science of shopping interesting is that things change and we still get surprised. I like to think of retail as the dipstick of our evolution. As we change as a species, those changes show up both in how we shop and what we shop for. That said, there are constants that relate to what we are biologically, and much of this book is about those constants.
For example, we discovered a phenomenon that journalists love to report—what’s become known as the “butt-brush” effect—completely as the result of a happy accident. As part of a department store study, we trained a video camera on one of the main ground-floor entrances, and the lens just happened also to take in a rack of neckties positioned near the entrance, on a main aisle. While reviewing the tape to study how shoppers negotiated the doorway during busy times, we began to notice something weird about the tie rack. Shoppers would approach it, stop, and shop until they were bumped once or twice by people heading into or out of the store. After a few such jostles, most of the shoppers would move out of the way, abandoning their search for neckwear. We watched this over and over until it seemed clear that shoppers—women especially, though it was also true of men, to a lesser extent—don’t like being brushed or touched from behind. They’ll even move away from merchandise they’re interested in to avoid it. When we checked with our client, we learned that sales from that tie rack were lower than they expected from a fixture located on a main thoroughfare. The butt-brush factor, we surmised, was why that
rack was an underperformer.
And in fact, when we delivered our findings to the store’s president, he jumped up from his chair, grabbed the phone, and ordered someone to move that tie rack to a spot just off the main aisle. A few weeks later, we heard that sales from the rack had gone up quickly and substantially. Since that day we’ve found countless similar situations in which shoppers have been spooked by too-close quarters. In every case, a quick adjustment was all that was needed. So the idea of a body bubble gets applied to shopping—and we can push the idea even farther. It isn’t that we hate crowds. A teeming cluster of people can be exhilarating. At Yankee Stadium, or even a sale at the local fashion emporium, we show up expecting company, and a lot of it. Sure, we can get claustrophobic and sometimes even scared, but after all, we’re the ones who put ourselves there. Where butt-brush kicks in big time is where we get bumped and we don’t expect it.
Another such “accident” of patient observation and analysis happened during a supermarket study we performed for a dog food manufacturer. While staking out the pet aisle, we noticed that while adults bought the dog food, the dog treats—liver-flavored biscuits and such—were more often being picked out by children or senior citizens. After giving it some thought, we realized that for the elderly, pets are like children, creatures to be spoiled with sweets. And while feeding Fido may not be any child’s favorite chore, filling him up with doggie cookies can be loads of fun. Parents indulged their little ones’ pleas for treats here just as they did over in the cookie aisle.
Because no one had ever noticed who exactly was buying pet treats, however, they were typically stocked near the top of the supermarket shelves. As a result, our cameras caught children actually climbing the shelving to reach the treats. We witnessed one elderly woman using a box of aluminum foil to knock down her brand of dog biscuits. Move the treats to where kids and little old ladies can reach them, we advised the client. They did so, and sales went up instantly.
Even the plainest truths can get lost in all the details of planning and stocking a store. A phrase I find myself using over and over with clients is this: The obvious isn’t always apparent.
While studying the cosmetics section of a drugstore chain, we watched a woman in her sixties approach a wall rack, study it carefully and then kneel before it so she could find the one item she needed: concealer cream, which, due to its lack of glamour, was kept at the very bottom of the display. Similarly, in a department store we watched an overweight man trying to find his size of underwear at a large aisle display—and saw him stooping dangerously low to reach them, down near the floor. In both cases, logic should have dictated that the displays be tailored to the shoppers who use them, not to the designers who made them. Move the concealer up, we advised, and put something aimed at younger shoppers down near the floor. Young shoppers will find their products wherever they’re stocked.
In some studies, we synthesize every bit of information we can possibly collect into a comprehensive portrait of a store or a single department. A major jeans manufacturer wanted to know how its product was sold in department stores, so in one weekend we descended on four sites, two in Massachusetts and two in the Los Angeles area. Each department was similar—the jeans section was a square area that held from eight to twelve tabletop displays and some wall shelving. We started by drawing a detailed map of each, showing the displays and the aisles leading into and out of the sections but also where any signs or other promotional materials were posted. During that weekend we tracked a total of 727 shoppers and observed many more on camera. We paid particular attention to the “doorways,” our term for any path leading into or out of an area of a store. Until the client knew which paths were most popular, it was impossible to make informed decisions about where to stock what or where to place the merchandising materials meant to lure shoppers.
By the time our study was completed, we could say what percentage of customers used which paths into each of the sections. Once we knew that, it was clear, for instance, that much of the signage was misplaced—common sense dictated that it be positioned to face the main entrance of the store, when in fact most jeans shoppers came upon the section from a completely different direction. Even the client’s big neon logo and a monitor showing rock videos were facing the wrong way if their job was to signal to the greatest number of shoppers. We tracked shoppers from table to table, seeing where they stopped, what signs they read, whether they noticed the video monitors, and how they handled the merchandise, including if they took anything to the dressing rooms. If they seemed to be showing jeans to a companion, we noted that, too. Our interviewers also questioned some of the shoppers captured on video so that their demographic information and their attitudes and opinions could be correlated with their behaviors—to see, for example, whether young shoppers with high school educations who say they depend on brand name when choosing jeans read price tags. After the research is done and the numbers are crunched and analyzed, we see what sense can be made of what we’ve learned.
For example, if we were to find that a high percentage of male shoppers buys from the first rack of jeans they encounter, and that these shoppers tend to enter the section through the aisle leading from men’s accessories rather than from the women’s side of the store or from the escalator, then we would advise our client to ask for the display table nearest men’s accessories.
Or maybe there’s another determining factor—maybe men who are accompanied by females and entering the section from the women’s department buy more jeans than men who are alone. In that case, the best table would be nearest the women’s merchandise. But no one knows for sure until we collect the data.
In other instances, we’re hired to study some small retail interaction in great detail. A premium shampoo maker who wanted to know about the decision-making process of women shoppers who buy generic, or store-brand, beauty products commissioned one such project. The client was interested in the “value equation” women bring to each shopping experience—how does the shopper who buys from the generics section at the supermarket in the morning and then from Bloomingdale’s in the afternoon decide which product she’ll buy where? Does she judge that her skin deserves the premium brand but her hair can settle for the generic? Once upon a time, only the budget-conscious bought store brands, but now you find them in everyone’s shopping basket. What’s the secret?
Let’s call her shopper number 24, a thirtysomething woman in yellow pants and a white sweater, accompanied by a preschool-age girl, who enters the health and beauty aisle of a supermarket at 10:37 a.m. on a Wednesday morning. She has a handbasket, not a shopping cart, and has already selected store-brand vitamin C capsules and a large container of Johnson’s baby powder. She is also holding a shopping list and a store circular. She goes directly to the shampoo shelves and picks up a bottle of Pantene brand, reads the front label, then picks up a bottle of the store brand and reads the front label, then reads the price tag on the Pantene, then reads the price on the store brand, and then puts the store brand in her basket and exits the section forty-nine seconds after she entered it. In that brief encounter, there was lots of data to collect—what she touched, what she read, and in what order—about twenty-five different data points in all. If, in one day, we track a hundred shoppers in that store’s health and beauty aisle, it amounts to twenty-five hundred separate data entries. As the woman exits the section, we interview her, asking twenty questions in all. So each of the twenty-five data points has to be cross-tabulated with each of her twenty answers—a cross-tab challenge, take it from me. Until quite recently no university ever attempted such a study, and so it was left to the world’s businesses—its retailers, banks, restaurant chains, manufacturers and designers of displays and packaging—to underwrite the creation of this science, which they did and continue to do by hiring us and sending us out into the field.
I make much of the accidental nature of the science of shopping, and perhaps it’s because this all began almost by accident when I was a stude
nt and admirer of one of America’s most esteemed social scientists, William H. Whyte, author of such highly influential books as The Organization Man, The Last Landscape, City: Rediscovering the Center and The Social Life of Small Urban Spaces. He was also the founder, in 1974, of the Project for Public Spaces, or PPS, which still exists and is still a magnificent contribution to the preservation and ongoing good health of the urban landscape.
William H. Whyte, or “Holly,” as his friends called him, was, in his active days, a quixotic, beloved figure (he died in 1999). He had the white hair and aristocratic mien of a WASP banker, yet he had fallen in love with the streets of New York City and worked hard to learn how people might best use them. Whyte’s greatest contribution was his research into how people use public spaces—streets, parks, plazas and so on. Using time-lapse photography, hidden trackers and interviews, he and his associates would stake out some urban plaza or minipark, say, and study it, minute by minute, over the course of several days. By the time they finished, they could tell you everything about every bench, ledge, path, fountain and shrub, and especially how people interacted with them, using them as places to lunch, sun, socialize, people-watch, nap or just happily and peacefully loiter. Whyte and his colleagues would measure everything—the ideal width of a ledge for sitting; how sunlight, shade and wind affect park use; and how a public space’s surroundings, the office towers or construction sites or schools or neighborhoods, determined the quality of life there.
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