Not surprisingly, software vendors have been quick to jump on the analytics bandwagon (see “The Vendor Landscape,” at the end of this article). Some vendors — prominently Business Objects, Hyperion, and Cognos — are flogging programs that collect and measure sales data. Others, such as business-software giants Oracle, PeopleSoft, SAP, and Siebel, offer software that analyzes buying trends. Still others (including Teradata and SAS) market predictive-modeling packages. And a number of vendors sell applications designed to group customers by categories, including profit potential.
Eventually, analysts believe, there will be a blurring of the lines, with software makers offering analytics products that measure, analyze, and cluster. But CFOs, some of whom saw elaborate call-center initiatives go awry, will take a bit of convincing. Says Rick McMahon, CFO of Sunstar Butler, a Chicago-based oral-care products company that is contemplating buying an analytics program: “It’s just way too much money and time to end up being a toy.”
Science of Selling
Finance chiefs like McMahon have been down this road before. During the go-go days of corporate call-center spending, vendors hurriedly rolled out different customer-service applications. The goal was to provide a 360-degree view of a consumer, but more often than not, the view was less than ideal.
Case in point: Jonathan Wu, senior principal at Chicago — based professional — services firm Knightsbridge Solutions LLC, recalls one client that operated six different applications that interfaced with customers — but not with one another. The siloed systems generated a substantial amount of conflicting and duplicate data. No big surprise, then, that the company’s management had a slightly overblown opinion of how business was going. “They thought they were growing by 23,000 customers per month,” remembers Wu. “It wasn’t even close.”
Such stories have not exactly burnished the reputation of CRM software vendors. Nevertheless, the promise of analytics — better selling through science — appears to be gaining converts. Indeed, some companies are already engaged in predictive modeling and segmenting.
With predictive modeling, companies attempt to forecast future customer behavior based on analytic models. Segmenting, on the other hand, involves grouping customers by common traits or behavior patterns; clustering is one common analytic technique to help achieve this. Generally, businesses segment customers into groups to help them devise the most cost-effective way to market and to service those groups.
Segmenting is not limited to existing customers, however. At Irvine, California-based Volvo Cars of North America, Phil Bienert, manager of the automaker’s CRM & E-business group, says his department is currently in the middle of a segmentation project involving prospective customers. According to Bienert, Volvo is breaking current customers into segments, and then comparing the patterns of those groups with those of prospective buyers. The patterns can be obvious — customers moving up the auto food chain, for example, from compacts to midsize cars to SUVs — or hidden, the kind that companies need analytics to uncover.
The goal is to identify behavior that indicates a propensity for buying a Volvo down the road. “You can apply these owner characteristics to hand-raisers [those who request information about Volvo products] and cluster them,” explains Bienert. “Then you can prequalify people who haven’t even entered into communications with the company.” (Like many large companies, Volvo buys consumer data from third parties.)