A predictive analytics project generally has three cyclical phases:
- Assessment phase is used to explore the business question. Analysts ask questions such as, what are the key customer interactions that influence the desired outcome? And, for those interactions that are most important, what are some of the decisions, policies, or processes that influence that interaction?
- Analysis phase is focused on gathering relevant data from multiple sources and applying advanced analytic techniques. During this phase, data scientists are building predictive models and working with the business to operationalize prescriptive actions. This stage includes setting up systems and processes to optimize the prediction.
- Test and learn phase involves learning through the changes that have been made and validating the business question. It includes tuning the prediction through additional data sources, insight and feedback from subject-matter experts, and intelligent experiments involving multiple treatments and controls to see which actions yield optimum outcomes.