Building Data Mining Applications for CRM

Chapter 1: Customer Relationships

The way in which companies interact with their customers has changed dramatically over the past few years. A customer's continuing business is no longer guaranteed. As a result, companies have found that they need to understand their customers better, and to quickly respond to their wants and needs. In addition, the time frame in which these responses need to be made has been shrinking. It is no longer possible to wait until the signs of customer dissatisfaction are obvious before action must be taken. To succeed, companies must be proactive and anticipate what a customer desires.

It is now a cliche that in the days of the corner market, shopkeepers had no trouble understanding their customers and responding quickly to their needs. The shopkeepers would simply keep track of all of their customers in their heads, and would know what to do when a customer walked into the store. But today's shopkeepers face a much more complex situation. More customers, more products, more competitors, and less time to react means that understanding your customers is now much harder to do.

A number of forces are working together to increase the complexity of customer relationships:

• Compressed marketing cycle times. The attention span of a customer has decreased and loyalty is a thing of the past. A successful company needs to reinforce the value it provides to its customers on a continuous basis. In addition, the time between a new desire and when you must meet that desire is also shrinking. If you don't react quickly enough, the customer will find someone who will.
• Increased marketing costs. Everything costs more. Printing, postage, specialoffers (and if you don't provide the special offer, your competitors will).
• Streams of new product offerings. customers want things that meet their exact needs, not things that sort-of fit. This means that the number of products and the number of ways they are offered have risen significantly.
• Niche competitors. Your best customers also look good to your competitors. These competitors will focus on small, profitable segments of your market and try to keep the best for themselves.

Successful companies need to react to each and every one of these demands in a timely fashion. The market will not wait for your response, and customers that you have today could vanish tomorrow. Interacting with your customers is also not as simple as it has been in the past. Customers and prospective customers want to interact on their terms, meaning that you need to look at multiple criteria when evaluating how to proceed. You will need to automate:

• The Right Offer
• To the Right Person a At the Right Time
• Through the Right Channel

The right offer means managing multiple interactions with your customers, prioritizing what the offers will be while making sure that irrelevant offers are minimized. The right person means that not all customers are cut from the same cloth. Your interactions with them need to move toward highly segmented marketing campaigns that target individual wants and needs. The right time is a result of the fact that interactions with customers now happen on a continuous basis. This is significantly different from the past, when quarterly mailings were cutting-edge marketing. Finally, the right channel means that you can interact with your customers in a variety of ways (direct mail, email, telemarketing, etc.). You need to make sure that you are choosing the most effective medium for each particular interaction.

The purpose of this book is to provide you with a thorough understanding of how a technology like data mining can help solve vexing issues in your interactions with your customers. We describe situations in which a better understanding of your customers can provide tangible benefits and a measurable return on investment.

It is important to realize, though, that data mining is just a part of the overall process. Data mining needs to work with other technologies (for example, data warehousing and marketing automation), as well as with established business practices. If you take nothing else from this book, we hope that you will appreciate that data mining needs to work as part of a larger business process (and not the other way around!).

What Is Data Mining?

Data mining, by its simplest definition, automates the detection of relevant patterns in a database. For example, a pattern might indicate that married males with children are twice as likely to drive a particular sports car than married males with no children. If you are a marketing manager for an auto manufacturer, this somewhat surprising pattern might be quite valuable.

However, data mining is not magic. For many years, statisticians have manually "mined" databases, looking for statistically significant patterns.

Data mining uses well-established statistical and machine learning techniques to build models that predict customer behavior. Today, technology automates the mining process, integrates it with commercial data warehouses, and presents it in a relevant way for business users.

The leading data mining products are now more than just modeling engines employing powerful algorithms. Instead, they address the broader business and technical issues, such as their integration into today's complex information technology environments.

In the past, the hyperbole surrounding data mining suggested that it would eliminate the need for statistical analysts to build predictive models. However, the value that an analyst provides cannot be automated out of existence. Analysts will still be needed to assess model results and validate the plausibility of the model predictions. Because data mining software lacks the human experience and intuition to recognize the difference between a relevant and an irrelevant correlation, statistical analysts will remain in high demand...