Increasing the ROI on CRM with Predictive Analytics
By Colin Shearer (cshearer@spss.com) VP Customer Analytics, SPSS Inc.
Source: www.spss.com
Copyright SPSS, Inc. 2004

Since the advent of business - perhaps a prehistoric man selling the latest in clubs to his fellow Neanderthals - the guiding principle to success has been to keep the customer happy. This simple, straightforward philosophy has evolved along the way, just as man has, to become a more complex, costly process we now call customer relationship management (CRM). No one disputes the importance of CRM. The debate, however, often centers around the return on the investment in technology supporting CRM.

What a cave man did with a grunt or a nod, modern man must do with elaborate systems and processes that require a variety of disciplines to implement, oversee and decipher. Too often the bottom line information that is needed to make the decisions that will keep customers happy gets lost in the CRM maelstrom. The goal of CRM is to gain enough information to increase customer lifetime value, but the essence of CRM is data. The data gathered by CRM systems is growing exponentially. Every customer contact, event, transaction and Web site hit generates data. Data is good and the more data the better, but data by itself has no value if it is not turned into information.

According to the Gartner Group, a technology analyst firm in Stamford, Conn., the rate at which data is actually used in decision-making is increasing much more slowly than the rate at which decisions need to be made and data are increasing. The upshot of what Gartner calls the Fact Gap is that today's businesses are data rich but information poor. Yet it's information that drives the return on the CRM investment; it's what is needed to make the decisions necessary to keep the customer happy.

Turning data into useful information is where analytical CRM technology comes into play. A host of CRM analytical products discover the information in data by summarizing what has happened in the past. In other words, these tools can tell you who the best customers were last month and this month. This kind of traditional business intelligence is important, but it's all about historical information. In order to increase customer lifetime value, predictive analytics, like data mining, are needed to provide a clear picture of what is going to happen in time to change it. For example, who the best customers could have been, or which customers are likely to defect. Predictive analytics enable businesses to drive revenue and succeed in the market.

Data mining, a predictive analytic process, discovers the meaningful patterns and relationships in data - separating signals from noise - and provides decision-making information about the future. Traditional data mining focuses on extrapolating intelligence from structured, numeric data, and text mining analyzes unstructured textual data by finding and discovering the patterns and relationships within thousands of documents such as e-mails, call reports, Web sites and other information sources. Estimates suggest that more than 80 percent of the information available today exists in some type of text format. By combining text mining and traditional data mining technologies businesses have a wealth of undiscovered, predictive information from which to improve profits.

Standard Life, a leading global mutual financial services company, needed to expand its share of the increasingly competitive mortgage market, and a major part of their efforts was to develop models that could identify customer characteristics relevant to any mortgage product. Data mining enabled Standard Life to better understand the characteristics of its mortgage customers so it could more accurately search for potential new clients. As a result, the company achieved a nine times greater response to offers and has secured approximately $50 million worth of mortgage application revenue.

Standard Life and other successful companies have gained new insights into their customer relationships through data mining and other predictive analytical tools. These tools have been used by these companies to acquire new customers, cross-sell/up-sell, retain customers, acquire new customers, increase store traffic, grow Website profitability and to positively impact the bottom line in a host of other ways. Predictive analytics give the companies that use these technologies new insights into their businesses and their customers.

As appealing as the simplicity of doing business with a grunt or a handshake is, the truth is those days have gone the way of the dinosaurs and the only way to compete effectively in today's complex marketplace is with modern CRM technology. To be fully effective and drive revenue, operational CRM needs analytical CRM with predictive analytics at its core. With these pieces in place, businesses realize a tangible return on their CRM investment and increase profitability.