Ivy Jun 10, 2015 No Comments
Analytics aims to understand the ‘why’ if you lose a customer. Predictive analytics moves beyond this to prevent you from losing a customer before it happens.
Predictive analytics supports insight, not hindsight. You discover meaningful trends and patterns in the data, and make predictions of unknown future outcomes. Using real world models that leverage mathematical and/or statistical techniques, you can predict possible events with a fair degree of accuracy.
Models, in predictive analytics, represent meaningful relationships among variables, response or explanatory variables. Response variable refers to the quantity about which a query is made, while explanatory variable is the factor influencing the response variable. Explanatory variables are useful in predictive models – for observation, manipulation and control in relation to response. Patterns in historical and transactional data are studied for such variables to draw conclusions and make forecasts or identify opportunities.
Predictive modeling is the creating, testing, validating and evaluating of a model to predict an outcome with a given input data.
A predictive model has predictors, i.e. variables likely to influence future outcomes. For instance, the gender, age group, location and purchase history of a customer adds up to the probability of a repeat transaction. These are ‘predictors‘.
What does a Predictive model do?
Approaches of Predictive Modelling
Common predictive models are regression and classification.
Look out for our next post on “Common Statistical techniques used in Predictive modelling”.