Until recently, a multinational retailer was a model of old-school mass marketing with its various distribution channels ­ stores, catalog, call centers and Web site ­ operating as independent silos. Each channel maintained its own contact lists and gathered its own customer spending data. As a result, customers often received multiple copies and competing offers. Costs were high, customers were confused and responses were low.

In late 1999, the retailer became aware that two other leading retailers were making great strides in understanding and segmenting customers. In addition, dot-coms were creating a whole new shopping experience based on one-to-one marketing. In this competitive climate, the retailer needed to get to know its customers quickly and find a cost-effective way to reach them.

Fortunately, it had high- quality customer information at its fingertips. It just didn't know how to use it. Then it started working with the Center for Data Insight (CDI).

How to Recognize Your Most Valuable Customers

CDI is an applied business intelligence (BI) research center located at Northern Arizona University. BI, which helps businesses obtain knowledge from large databases, includes such technologies as online transaction processing (OLTP), online analytical processing (OLAP), quantitative analytics and data mining. The center houses one of the most complete collections of business intelligence tools in the world ­ all of which were donated by leading software and hardware vendors such as IBM, SAS, SGI, SPSS, Unica, Compaq and others.

The center used OLAP to help the retailer answer questions such as, "What are the most popular products sold in each region during the Christmas season?" It used data mining to examine the retailer's customer base of 50 million people and segment it into groups with similar characteristics ­ such as size of house, marital status, job type, number of children and past shopping behavior.

The retailer had long assumed that its best customers were 50- to 60-year- olds in the low-to-middle-income bracket. As a result of CDI's analysis, the retailer learned something strikingly different: its best customers were white- collar, upper-income families in their late 30s and 40s with children over the age of 12. The center also created profiles of next-best and middle-ground customers.

Knowledge into Action

Though the retailer now had a better understanding of its client base, the key to selling would be to link its newfound knowledge with marketing campaigns through customer relationship management (CRM).

Working closely with outside consultants, the retailer used CRM programs to retain the best customers, acquire customer prospects who looked like best customers, and grow or migrate next-best customers and middle-grounders into upper levels of loyalty. For example, retention customers received "points" to encourage them to continue doing what they were already doing. Growth customers received various types of discounts. Acquisition customers, who looked like best customers, received even more aggressive discounts.

As a result of the retailer's deployment of business intelligence combined with CRM, the retailer left mass marketing behind and took a more personalized approach to interacting with its customers. By doing so, it raised its requirements for ongoing data retrieval and management on the back end.

One View of the Customer

As long as the retailer maintained autonomous marketing divisions for each of its channels, it undermined its own CRM effort. Unless it could unify its customer data, the best customers ­ the upper-income family with husband and wife in their 30s and 40s ­ would fail to appear on the radar screen.

The retailer used knowledge management (KM) technologies, including data warehousing, to link data from each channel in real time, extract important knowledge and eliminate extraneous information. The result: a single view of the retailer's customers across the entire enterprise.

Now, when a member of that all-important family buys a utensil at one of the retailer's stores, and another buys clothing through the retailer's catalog, and yet another buys a CD online, their shopping experiences register in the aggregate. The previously invisible family now shows its true colors as a best customer and can be targeted as such going forward.

In the end, what brought the retailer's marketing into the digital age was neither BI nor CRM nor KM alone, but a seamless linking of these methodologies.

Special thanks to Ken Collier, senior vice president, and Linda Morris, senior manager, of KPMG Consulting's Knowledge Management practice for their contributions to this piece.

As the provider of Internet integration services, KPMG Consulting, LLC helps clients achieve sustainable competitive advantage in the new e-business economy. Services include strategy, branding, design/media, infrastructure, technology enabling and hosting/outsourcing; solutions comprise customer relationship management, supply chain management, knowledge management, world class finance, e-learning and world class human resources.

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