Nothing is more important to the future of predictive analytics teams than proving their projects have long-term value. Measuring the return on investment (ROI) often can help turn analytics into a visible profit center for your organization. Estimating ROI early—before a project even begins—can also help fast-track approval. Here Keith McCormick shows how to address ROI both before and after the predictive model is built. Learn how to create your estimate before the project starts by estimating the overall size of the problem, assigning value to possible outcomes, and judging the impact of model performance. Keith then shows a different method for calculating ROI after the model is built, during the evaluation and deployment phases, and provides tips for the ongoing monitoring of the project. He also takes a retrospective look assessed one year after model deployment. These two strategies will give you the data you need to get buy-in for your projects and provide ongoing metrics on their performance.
Topics include:
- Review the components of successful analytics in estimating and ensuring ROI.
- Identify types of statistical errors.
- Apply the confusion matrix to business decisions.
- Describe the reasoning in setting propensity score cutoff levels.
- Examine the process of monitoring predictive models.
Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.
This course is in French only. If this is not a problem for you, by all means go ahead and apply.