Sex and the Schoolboy: Predictive Modeling - Who’s Doing It? Who’s Doing it Right?
Forgive the borrowed interest, but predictive modeling is to marketers as sex is to schoolboys.
They're all talking about it, but few are doing it. And among those who are, fewer are doing it right.
In customer relationship marketing (CRM), predictive modeling uses data to predict the likelihood of a customer taking a specific action. It's a three-step process:
1. Examine the characteristics of the customers who took a desired action
2. Compare them against the characteristics of customers who didn't take that action
3. Determine which characteristics are most predictive of the customer taking the action and the value or degree to which each variable is predictive
Predictive modeling is useful in allocating CRM resources efficiently. If a model predicts that certain customers are less likely respond to a specific offer, then fewer resources can be allocated to those customers, allowing more resources to be allocated to those who are more likely to respond.
A predictive model will only be as good as the input data that's used in the modeling process. You need the data that define the dependent variable; that is, the outcome the model is trying to predict (such as response to a particular offer). You'll also need the data that define the independent variables, or the characteristics that will be predictive of the desired outcome (such as age, income, purchase history, etc.). Attitudinal and behavioral data may also be predictive, such as an expressed interest in weight loss, fitness, healthy eating, etc.
The more variables that are fed into the model at the beginning, the more likely the modeling process will identify relevant predictors. Modeling is an iterative process, and those variables that are not at all predictive will fall out in the early iterations, leaving those that are most predictive for more precise analysis in later iterations. The danger in not having enough independent variables to model is that the resultant model will only explain a portion of the desired outcome.