Statistical modeling in an MCIF.

• Author: Coffey, John J.
• Position: Database Marketing

There are three sets of items that do not mix well naturally: Oil and water. Gasoline fumes and an open flame. Bank marketing and statistical modeling!

However, when something causes these items to mix, powerful forces are unleashed. When detergent is added to water, it can wash stubborn oil stains out of your clothing. When gasoline fumes are contained in an engine, an open flame from a spark plug can power your car. When user-friendly modeling software gets into the hands of bank marketers, it can enable them to become enormously effective in their marketing efforts!

The purpose of modeling

The main purpose of modeling data is to optimize the targeting and selection of prospects and customers for direct marketing applications. Targeting only the best candidates will improve response rates, increase average revenue per piece mailed, or per call made, and improve the profitability of marketing programs.

In its simplest form, modeling is a lot like profiling. Profiling is nothing more than examining a group of customers with certain characteristics (e.g., product use, demographics, etc.), constructing a profile from this data, and finding other customers who look like the profile.

For instance, you may have a highly profitable group of customers who have high balances in their money market account and also have a mortgage with you. Profiling these customers could involve researching their money market balance, age, income and home value. Then, you would construct a profile of desired characteristics and look for other customers who had similar characteristics--like customers who have high balances in their money market accounts and also have the same age, income and home values as the profile but do not have a mortgage with your bank.

Modeling extends the results of this simple process by taking it one further step. While modeling reveals the identifying characteristics in a profile, it also predicts and computes a numerical score that reflects the degree of resemblance between the profile and a targeted customer.

Types of modeling

There are many types of modeling but we'll tackle three of the most common modeling methods on the market today: regression analysis, neural network analysis...