To build a model we need information that is available on both customers and non-customer records. Customers could be businesses in a B2B environment, or could be individual consumers. Potential predictor variables for businesses include ones such as, geography, company size, business classification, turnover, etc. or for individual consumers these potential predictor variables might be age, income, household size, location, etc. Together with a response indicator (e.g. Y for customers, N for non-customers) this forms the “training data”.
Some thought is required in cyprus mobile phone numbers choosing which variables to use. In some cases you may need to derive new, more relevant variables. For example, you may need to calculate the total spend of customers in the last year, or calculate the ratio of this year’s spend to last year’s spend.
Build the model
This information is fed into the model building process and used to build a formula or set of rules, which will be applied to identify likely customers. There are a wide variety of model building techniques, but at the simplest level, the model formula or rules are a way of describing the key characteristics of your customers which distinguish them from a background consumer or business universe. The model can then predict likely prospects by seeing if they share these key characteristics.
Model building involves validating alternative models by applying them to data-sets in which we already know the customer status and measure how successfully the model classifies known customers. A useful model will give predominantly high scores to customers known to be in the target group. To avoid testing the model directly on the data used to build it, it is common to reserve a proportion of the data as a holdout sample to be used for testing.