Predictive Data Modeling

Predictive Data Modeling’s central building block is the predictor, a single value measured for each consumer. For example, recency, which is based on the span of time since the consumer’s last purchase, has higher values for more recent customers. This predictor is usually a reliable campaign response predictor: you will receive more responses from those consumers more highly ranked by recency. That means that if you contact your customers in order of recency - first, contact the most-recent customer; next, contact the next-most-recent customer; and so on - you will improve your response rate.

For each prediction goal, there are an abundance of predictors that will help rank your customer database. For example, consider a consumer’s online behavior: Consumers who spend less time logged on may be less likely to make a purchase. In this case, retention campaigns can be cost-effectively targeted to consumers with a low monthly usage predictor value.

Combined Predictors Mean Smarter Rankings

It turns out you can do even better by using more than one predictor at a time, combining them with a model. Creating this model is the very purpose of predictive analytics. We create your model based on your consumer data.

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