The New Reality for Customer Engagement
Not every prediction is important.
Kurt Schlegal, a research director at Gartner Inc., points out that to provide a good ROI, predictive-analytics applications have to be aligned with the corporate strategy. "Find out what drives the corporation's bottom line and implement a system that makes predictions that affect that," he says.
Take Aegon Direct Marketing Services Inc., the direct-to-consumer insurance subsidiary of Netherlands-based Aegon N.V., with $20 billion in 2004 sales. One of the largest direct marketers of insurance in the U.S., Aegon sells a number of brands, the most well known being Transamerica.
The firm's corporate strategy is clear and unambiguous. "We sink or swim on our ability to identify, with a great deal of specificity, people who will buy from us," says Mark Rude, senior vice president of marketing services. Last fall, when he was CIO, Rude implemented marketing optimization software, from SAS Institute Inc., to enhance that strategy.
The company pitches its life, health and disability products through direct mail, telemarketing, Web ads, inbound calls to its call center and other direct-to-consumer avenues. For years, it used a variety of business-intelligence and data-mining applications, some homegrown. But these applications generated prospect lists that had the dual deficit of being too long while also missing some of the most likely candidates. "Those problems were putting a lot of pressure on our bottom line," Rude says.
The new software accompanied a major shift in marketing strategy aimed at taking advantage of Aegon USA's list of current customers, which Rude felt was being overlooked by the previous process. Simply put, the company's lack of predictive analytics had precluded a robust cross-marketing strategy.
Aegon USA's previous marketing strategy involved a series of discrete efforts, each focused on a product or a campaign, not on customers. But Rude wanted to look at marketing from the customer's perspective. "For each customer, I wanted to know all the products he or she is likely to buy, in the order of likelihood," he says.
The software provides a predictive score for each potential sales opportunity the company has for each customer. The score changes monthly, as customers' situations change. For example, as a customer grows older, has a child or buys another Aegon insurance product, their score may change accordingly.
Rude points out that predictive-analytics applications also allow organizations to add business rules, which consider strategic factors that may affect customers the organization wants to solicit. The customer most likely to purchase a product may not always be the best one to sell to. In fact, Aegon includes a number of such constraints when generating its prospect lists. For example, the company may decide not to pitch a low-return product to a customer, despite his or her high likelihood of buying it, because the same customer may be likely to purchase a more profitable product. "There are a lot of balls to juggle. It would be virtually impossible without predictive analytics," says Rude.
Can we identify our most profitable products?
Is our CRM system up to the task of providing accurate information on customers?
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