Can You Predict the Future?
Predictive models address two categories of problems. First, there are circumstances where the marketer must establish a prediction of a quantity. Typical questions answered here focus on a customer's potential balance, his or her spending, or orders. Second, "yes/no-type" problems can also be successfully resolved with models. These usually include response (Will a customer respond?) and attrition (Will a customer defect?) issues. The first problem solved results in a prediction of a quantity; the second problem delivers a probability of an event (such as a response) happening.
The formula for the regression model provides its user with three critical pieces of information. First, by direct examination of the algorithm, we notice which data elements play a role in defining the dependent variable.
From this, we can conclude that spending, age and the number of mailings influence our prediction of individual response.
Second, by looking at the sign of each variable in the equation, we can ascertain the direction of the relationship between dependent and independent variables. So "spending" has a positive (+) sign, implying that as spending increases, probability of response also increases. Age and number of mailings are negatively related. That is as age increases or number of mailings rises, probability of response declines.
Lastly, although not always apparent from examining the algorithm, the relative impact of each predictor on the dependent variable also can be estimated. So, we would be able to rank our three variables by their impact on the likelihood of responding.
Along Came Neural Networks
While marketers began to embrace regression technologies in the 1970s, artificial neural networks (ANN) became a buzzword only about a decade ago. ANNs are somewhat more complex than regression tools. Essentially they are multi-input, nonlinear models. Weights connect the input and output layers.
The INPUTS box, or layer, houses the predictors or independent variables. Weights connect this layer to a middle, or hidden, layer. A nonlinear process occurs at this stage. It is this point in the technology that many refer to as the "black box." In this middle layer, many seasoned practitioners cannot always assess what has transpired to each of the independent variables. Something clearly has occurred. We just are not always sure what that is, and its outcome is different and significant. The next step leads to the output layer, which is what we are trying to predict.