Editor’s note: Geoff Ables is Principal Consultant for Customer Connect.Data mining is a little like baseball. You gather the information (that’s the wind up). You apply effort to put the information together (that’s the pitch). Then, you use the data to grow your business (that’s the follow-through). Having a great wind up and pitch falls flat if you do not follow through.
Companies use data mining to take the information they already have about their customers and put it to work for them.
Descriptive data mining is essentially reporting. It tells you who bought what, when they bought it, what type of business they use it for, and whether they were satisfied.
Predictive data mining involves taking that information and using software programs or mathematical models to form an educated guess about what your customer will want or do next.
You gathered the information because you intended to do something with it. That “something” involves learning more about your customer so that you can make a decision based on that knowledge. Your decision may be to change the information in your catalog or on your website. It could be a change to product features, or handling your order processing differently. Whatever decision you make will involve costs, so be sure you know how to make a good decision based on data mining information.
Whether you use descriptive or predictive data mining, there are four rules to avoid making a bad decision based on your data mining efforts:
Use these guidelines for your data mining follow-through, and you increase your odds of hitting a home run.
Geoff Ables is Principal Consultant for Customer Connect, which provides CRM planning services and implementation of customer management campaigns, technologies, and analysis. He can be reached at firstname.lastname@example.org or 704-892-2633.