Strategic Thinkers: Gabriele Piccoli and Richard T. Watson Credentials: Piccoli is Associate Professor of Information Systems at the University of Sassari, Italy. He is also an associate editor at MIS Quarterly. Watson is the J. Rex Fuqua Distinguished Chair for Internet Strategy and interim Head of Information Systems at the Terry College of Business, the University of Georgia. He has authored numerous books and articles and delivered seminars in more than 30 countries. Big Idea: There are four data-driven strategies a firm can use to increase the value of its customer data, but the choices are influenced by the repurchase frequency and customizability of its products or services.
Two professors recently presented their framework for identifying the best approach to using customer data. Gabriele Piccoli and Richard Watson presented their ideas in the September issue of MIS Quarterly Executive. They identify four strategies for exploiting customer information: minimize costs; reward loyalty; personalize interactions; and acquire customers. Each of these options has different data strategies. There are also two basic characteristics of goods and services that also influence your data strategy-the potential repurchase frequency of customers and the degree of customizability of your offerings.
The authors say that an organization should focus on a data strategy of minimizing costs when "there is little likelihood of repeat business and few options for customization." In this scenario there is little to be gained from exploiting customer data. Since there is a slim chance of repeat business or the opportunity to customize your product offering the best option is to focus on efficiency and low prices. An example of this strategy is a food vendor next to a major tourist attraction. With a mostly transient customer base there is little advantage to collecting customer data. Any data collection effort with this strategy should focus on finding ways to reduce costs. Cost allocation and reporting systems can help with revenue management and price optimization, the authors say.
If your company's products and services are purchased frequently and are standardized, without customization, then your data efforts should tilt toward a reward loyalty strategy. In this case, customer data is used to evaluate the profitability of each customer. Those deemed most profitable or most likely to be profitable are singled out for rewards designed to increase loyalty. Data is analyzed and reports are generated that can help stores improve operations. This requires an understanding of individual customer profitability and the likelihood of their repurchasing without an incentive.
A company often cited for using this type of rewards strategy is Harrah's Entertainment. Its Total Rewards loyalty points program provides the company with extensive knowledge about its customers' gaming behavior. They don't offer a customized product-one slot machine is very much like another-but can offer special treatment to its preferred customers.
When highly personalized products or services are the norm, where repurchase frequency is also high, collecting significant amounts of data also makes sense. This data is needed and highly valued since it provides information that helps companies tailor products or services specifically to the needs of repeat customers. The Ritz Carlton deploys this type of highly personalized service strategy to its affluent customers. Over time the chain has developed an elaborate database of guest preferences in its 70 hotels worldwide. The goal is to offer each guest a unique experience created just for her.
