Putting Data Mining to Work

Youve reached the last chapter of this book, and you are ready to start putting data mining to work for your company. You are convinced that when data mining has been woven into the fabric of your organization, the whole enterprise will benefit from an increased understanding of its customers and market, from better-focused marketing, from more-efficient utilization of sales resources, and from more-responsive customer support. You also know that there is a big difference between understanding something you have read in a book and actually putting it into practice. This chapter is about how to bridge that gap.

At Data Miners, Inc., the consulting company founded by the authors of this book, we have helped many companies through their first data mining projects. Although this chapter focuses on a companys first foray into data mining, it is really about how to increase the probability of success for any data mining project, whether the first or the fiftieth. It brings together ideas from earlier chapters and applies them to the design of a data mining pilot project. The chapter begins with general advice about integrating data mining into the enterprise. It then discusses how to select and implement a successful pilot project. The chapter concludes with the story of one companys initial data mining effort and its success.

Getting Started

The full integration of data mining into a companys customer relationship management strategy is a large and daunting project. It is best approached incrementally, with achievable goals and measurable results along the way. The final goal is to have data mining so well integrated into the decision-making process that business decisions use accurate and timely customer information as a matter of course. The first step toward achieving this goal is demonstrating the real business value of data mining by producing a measurable return on investment from a manageable pilot or proof-of-concept project. The pilot should be chosen to be valuable in itself and to provide a solid basis for the business case needed to justify further investment in analytical CRM.

In fact, a pilot project is not that different from any other data mining project. All four phases of the virtuous cycle of data mining are represented in a pilot project albeit with some changes in emphasis. The proof of concept is limited in budget and timeframe. Some problems with data and procedures that would ordinarily need to be fixed may only be documented in a pilot project.

A pilot project is a good first step in the incremental effort to revolutionize a business using data mining.

Here are the topic sentences for a few of the data mining pilot projects that we have collaborated on with our clients:

Find 10,000 high-end mobile telephone customers customers who are most likely to churn in October in time for us to start an outbound telemarketing campaign in September.

Find differences in the shopping profiles of Hispanic and non-Hispanic shoppers in Texas with respect to ready-to-eat cereals, so we can better direct our Spanish-language advertising campaigns.

Guide our expansion plans by discovering what our best customers have in common with one another and locate new markets where similar customers can be found.

Build a model to identify market research segments among the customers in our corporate data warehouse, so we can target messages to the right customers

Forecast the expected level of debt collection for the next several months, so we can manage to a plan.

These examples show the diversity of problems that data mining can address. In each case, the data mining challenge is to find and analyze the appropriate data to solve the business problem. However, this process starts by choosing the right demonstration project in the first place.

What to Expect from a Proof-of-Concept Project

When the proof-of-concept project is complete, the following are available:

A prototype model development system (which might be outsourced or might be the kernel of the production system)

An evaluation of several data mining techniques and tools (unless the choice of tool was foreordained)

A plan for modifying business processes and systems to incorporate data mining

A description of the production data mining environment

A business case for investing in data mining and customer analytics

Even when the decision has already been made to invest in data mining, the proof-of-concept project is an important way to step through the virtuous cycle of data mining for the first time. You should expect challenges and hiccups along the way, because such a project is touching several different parts of the organization-both technical and operational-and needs them to work together in perhaps unfamiliar ways.

Identifying a Proof-of-Concept Project

The purpose of a proof-of-concept project is to validate the utility of data mining while managing risk. The project should be small enough to be practical and important enough to be interesting. A successful data mining proof-of-concept project is one that leads to actions with measurable results. To find candidates for a proof of concept, study the existing business processes to identify areas where data mining could provide tangible benefits with results that can be measured in dollars. That is, the proof of concept should create a solid business case for further integration of data mining into the companys marketing, sales, and customer-support operations.

A good way to attract attention and budget dollars to a project is to use data mining to meet a real business need. The most convincing proof-of-concept projects focus on areas that are already being measured and evaluated analytically, and where there is already an acknowledged need for improvement. Likely candidates include:

Response models

Default risk models

Attrition models

Usage models

Profitability models