Editor's note: Big-box retailers have learned that standardization has reached a point of diminishing returns, concludes a recent article in Harvard Business Review written by two Bain & Company consultants. Local customization is an effective way for retailers to benefit from mass production while tailoring product offerings based on regional, seasonal, and cultural tastes. This excerpt discusses the customization-by-clusters strategy, pioneered by Bain in 1995.
Thinking in clusters
As Wal-Mart and other leaders have discovered, successful localization hinges on getting the balance right. Too much localization can corrupt the brand and lead to ballooning costs. Too much standardization can bring stagnation, dooming a company to dwindling market share and shrinking profit.
Striking the right balance means understanding which elements of a business should be considered for localization, how costly they are to customize, and how much impact they will have from one store to another. Far from being an all-or-nothing game, localization can take place in myriad of ways. For one retailer, it might make sense to have a highly localized staffing approach but a standardized product mix, while another retailer may warrant the opposite. Similarly, a manufacturer might localize product features in one area and retailer incentives in another. While it may be prohibitively expensive to customize a product to many locations, it may be possible to gain similar benefits by tailoring the product's packaging or promotions at a far lower cost. Wal-Mart found that while ant and roach killer sells well in the southern United States, consumers in the northern states are turned off by the word "roach." After labeling the pesticide as "ant killer" in northern states, the company has seen sales increase dramatically, according to John Westling, senior vice president.
Of course, customization has its limits. Even with rich data, a company can't customize every element of its business in every location. The sheer complexity would be overwhelming, leading to spiraling costs, if not paralysis. That's why leading localizers have begun using clustering techniques to simplify and smooth decision making, focusing their efforts on the relatively small number of variables that usually drive the bulk of consumer purchases.
Rather than letting local mangers' decentralized decisions fragment economies of scale, the pioneering companies have developed a science of analyzing data on local buying patterns to identify communities that exhibit similarities in demand. For example, American Eagle Outfitters, a retailer of fashionable casual wear with 740 U.S. stores, found that customers in western Florida exhibited seasonal purchasing patterns and price elasticities that closely matched those of certain communities in Texas and California. By tailoring assortments and promotions to such clusters of locations rather than to individual stores, companies like American Eagle can benefit from customization while holding on to most of the efficiencies of standardization.
The customization-by-clusters strategy, which Bain first applied to grocery stores in 1995, has proven effective in drugstores, department stores, mass merchants, big-box retailers, restaurants, apparel companies, and a variety of consumer goods manufacturers. Clustering sorts things into groups, or clusters, so that the associations are strong between members of the same cluster and weak between members of different clusters. Clusters enable manageable, modular operationsthink again of Wal-Mart's store templatesthat capture most of the benefits of customization while also simplifying decisions and protecting economies of scale. Consider a merchandise manager who has to decide how to stock 100,000 items in 1,500 stores for 365 days each year. If she wanted to customize the mix, she would have to make 54.8 billion decisions (100,000 x 1,500 x 365), many of which would be based on such small sample sizes that the predictions of even sophisticated models would be meaningless. If, however, the merchandise could be clustered into 2,500 classifications, the stores could be clustered into twenty similar types (for example, Latino border locations or upscale suburban places), and the timing (back to school, winter holidays) could be broken into 52 weeks, the number of decisions would be reduced to 2.6 million, which a modern computer model can optimize fairly easily.
The pioneering companies have developed a science of analyzing data on local buying patterns. |
Best Buy is using clustering to move away from a standardized big-box strategy. It has revamped close to 300 of its 700 U.S. stores, introducing "customer-centric" formats to appeal to local shoppers. The company identified five representative types of customers. First, there's "Jill," a busy mother who is the chief buyer for her household and wants quick, personalized help navigating the world of technology. In Eden Prairie, Minnesota, the company designed a store that caters to the needs of this suburban moms segment. The company found that this group of previously untapped consumers offered the best opportunity for expansion in the region. To attract this group, the store has an uncluttered layout with wider aisles and warmer lighting, and technology-related toys for children. Personal shopping assistants educate technology neophytes about products, and there's more floor space allocated to household appliances. Although the store still serves other, more traditional electronics shoppers, the company hopes the store can boost its sales by attracting a set of local customers that have felt overwhelmed inside a Best Buy store.
Other stores are being designed around the remaining four types of customers and are based on local demand patterns. For example, there's "Buzz," a technological junkie who wants the latest gear for entertainment and gaming. Stores catering to Buzz have lots of interactive displays that allow shoppers to try out new equipment and media. Then there is "Barry," an affluent, time-pressed professional who is looking for high-end equipment and personalized service. Stores tailored to his needs feature a store-within-a-store for pricey home-theater setups. Stores made with "Ray" in mind emphasize moderately priced merchandise with attractive financing plans and loyalty programs for the family man on a budget who wants technology that can enhance his home life. Finally, for small-business customers, there's a set of stores with specially trained staffs, extensive displays of office equipment, and mobile "Geek Squads" of service technicians.
While the chain plans to phase out these individual names beneath its banner, the terminology helped Best Buy crystallize the vision of each target customer for each cluster of stores.
By customizing stores in clusters, rather than individually, Best Buy has been able to maintain many of the scale economies that have long underpinned its success. So far, the new strategy is delivering strong results. The eighty-five Best Buy stores that had been localized as of early 2005 posted sales gains two times the company's average. Encouraged, the company is accelerating the conversion, with plans to change over all its U.S. stores in three years and localize outlets in other countries as well.
So how do you get started with clustering? Begin by collecting as many data as possible on key elements of your business for each store. If some information is missing or hard to get, don't wait for it to be collected. Use what's readily available to launch the analysis, recognizing that clustering always gets better over time. Use the data to develop clusters and identify customization opportunities. Then estimate the economics (including both sales and costs) of localizing the most promising elements of the customer offeringusing as few clusters as possible. A clothing retailer, for example, might find that localized markdown policies offer attractive returns and that climate is the key variable influencing markdown decisions. Further analysis may determine that a small number of store clustersthree, say will be sufficient to gain the optimum economic benefit. For merchandise mix, by contrast, the key variable might be customer lifestyle, which may require a dozen clusters to get the maximum payoff.