Marketing has undergone a paradigm shift. New tools, new technologies, new approaches and new data have opened marketers’ eyes that there is indeed a cause and effect and predictability in customer’s actions. It is discernable in the marketing data, as Davenport notes: “Most business functions, even those, like marketing, that have historically depended on art rather than science—can be improved with sophisticated quantitative techniques.” 1 In a paper, “CRM from ‘art to science’” Jackson2 sets forth a new framework for treating marketing as a science:

Early research and methods concerning customer relationship work often focus on more intuitive approaches to customer management. Many of the initial theories, such as one to one marketing and value-based management, were less analytical in their approach. Likewise, too often companies that have implemented customer relationship management (CRM) systems have done so with an unstructured approach (art) as opposed to a structured and by-the-numbers approach (science).
 
Historically, marketing is known as a social science, rooted in psychology and sociology. However, as has been recently discovered, customer behavior is actually quite quantitatively predictive:

Marketing…has always been tough to quantify because it is rooted in psychology. But now consumer products companies can hone their market research using multiattribute utility theory–a tool for understanding and predicting consumer behaviors and decisions. Similarly, the advertising industry is adopting econometricsstatistical techniques for measuring the lift provided by different ads and promotions over time.3

It is only recently that the marketers have discovered new data mining methods which are proving to be highly robust and reliable. “Over the last 10 years, a paradigm shift has occurred in the statistical analysis of marketing data.” 4

At the same time, consumers themselves have undergone their own paradigm shift…from being marketed to, to taking control of what messages they hear, when they hear them and what channels of communication that companies are able to use to communicate with them.“ The consumer is deluged with messages. The average consumer sees about one million marketing messages a year-about 3,000 a day. One trip to the supermarket alone can expose you to more than 10,000 marketing messages!” 5Customers will no longer tolerate this mass media or mass customization approach. Customers are individuals, not transactions or demographics. "Customers are demanding that marketers communicate when and how it is convenient for them. Underlying right-time marketing are analytic and predictive capabilities that determine the optimal interaction strategies, automation and incorporation of repeatable best practices” 6

One of the key shifts that has occurred is the need to treat customers as individuals and not as segments or clusters: “Successful direct marketing initiatives require firms to predict the behavior of specific individuals.” 7

Today’s managers are very interested in predicting the future purchasing patterns of their customers. Faced with a database containing information on the frequency and timing of transactions for a list of customers, it is natural to try to make forecasts about future purchasing.These projections often range from aggregate sales trajectories (e.g., for the next 52 weeks), to individual-level conditional expectations (i.e., the best guess about a particular customer's future purchasing, given information about his past behavior). There is a great deal of interest, among marketing practitioners and academics alike, in developing models to accomplish these tasks.

A new approach to customer data analysis is needed. Customers must be analyzed and treated as individuals.Today it is possible to analyze individual customer behavior and act on it with custom marketing materials, with the right message at the right time. It is intuitively obvious: “The secret to achieving a good marketing ROI is simple: Give customers more of what they truly want and less of what they don’t.”9With marketing data analytics and business intelligence, we can figure out what this is and optimize for it.

1 Thomas H. Davenport, "Competing on Analytics," Harvard Business Review 84, no. 1 (2006).

2 Tyrone W. Jackson, "CRM: From 'Art to Science'," Journal of Database Marketing & Customer Strategy Management 13, no. 1 (2005).

3 Davenport, "Competing on Analytics."

4 Greg M. Allenby, David G. Bakken, and Peter E. Rossi, "The HB Revolution," Marketing Research 16, no. 2 (2004).

5 Seth Godin, Permission Marketing (New York: Simon & Schuster, 1999).

6Dan Goldstein and Yuchun Lee, "The Rise of Right-Time Marketing," Journal of Database Marketing & Customer Strategy Management 12, no. 3 (2005).

7 Greg M. Allenby, Robert P. Leone, and Lichung Jen, "A Dynamic Model of Purchase Timing with Application to Direct Marketing," Journal of the American Statistical Association 94, no. 336 (1999).

8 Peter S. Fader, Bruce G. S. Hardie, and Ka Lok Lee, ""Counting Your Customers" The Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science 24, no. 2 (2005).

9 V. Kumar, Rajkumar Venkatesan, and Werner Reinartz, "Knowing What to Sell, When, and to Whom," Harvard Business Review 84, no. 3 (2006