Intelligent Enterprise

Better Insight for Business Decisions

RSS
Webcasts
Whitepapers
Subscribe
Home

July 18, 2003

CRM: The Power of Prediction

By making smart use of data and information, predictive modeling and analytics can lead to vastly improved customer relationships — and for your organization, intelligent, cost-efficient sales and marketing

by Hussain Sabri

Empowering the sales force with information tools continues to be a prime concern for commercial enterprises across the products and services spectrum. Organizations have transformed the term "customer acquisition and retention" from an internal performance assessment metric for sales associates into a critical bottom line of competitiveness for firms of all disciplines.

However, this transformation begs an important question: How do you strike a perfect balance between customer incentives and corporate profitability? A lesson many corporations now know is that "one size fits all" doesn't work as a model for customer care. With service personalization becoming the means of responding to this lesson — and defining competitive success in many industries — corporations are struggling with how to make personalization a reality.

The answer is predictive modeling. Using the wealth of information the organization already possesses on its clients and customers, which may be augmented with purchased data to help better understand lead potential and prospective clients, businesses can predict the range of products and services that best suit particular customers. Another benefit of predictive modeling is that the techniques only get better with time. The margin of error (that is, deviation between expected and actual results) gets fed back into the predictive modeling system as a secondary input and works as a calibration factor. The result is a predictive CRM system that continues to improve, especially as customers take advantage of the package of products and services that are personalized based on demographic and characteristic attributes and refined over time (see Figure 1).

Value Metrics

A customer lifetime value (CLTV) metric is a net present value calculation that illustrates the relationship between a customer's revenues, expenses, and expected life of the relationship between the customer and the company. CLTV focuses on customer behavior and often incorporates the following factors:

Initial services bought by the customer
Future sales of products and services
Customer service costs
Relationship marketing expenses
Cross-sell revenues
Probability of future purchases and customer retention
Credits, discounts, or other incentives used to keep an account.

You can calculate the CLTV value as the difference between revenues and expenses minus the cost of promotional marketing used to retain an account; all values are discounted back to the present. The CLTV model in its most basic form is shown in Table 1.