Predictive Analytics Best Practices
Gain practical guidance from leading users, analysts and experts on how to successfully scope and implement Predictive Analytics initiatives and projects. Includes implementation examples, guidance, checklists, mistakes and tips.
| How Will Predictive Modeling Change the P&C Industry in the Next 5-10 Years? |
| Predictive analytics-related predictions: - More Property & Casualty insurers' competitive strategies will be tied to analytics. - Data will be explicitly recognized as an asset. - Analytics will be applied to better understand consumer behavior and market-based (not merely cost-based) pricing. - Analytics will also be used to better understand employee behavior. - Technical actuaries will find many new opportunities beyond the pricing department. |
| James Guszcza, Deloitte |
| Bridging The Data Gap: Leveraging Electronic Medical Record and Consumer Business Data to Improve Predictive Segmentation in Healthcare |
| An in-depth Resource that provides many technical and functional pointers and suggestions for successful predictive analytics initiatives. Example: the Resource covers the following as part of a detailed look at the predictive modeling process: - Variable creation - Data exploration - Variable transformation - Multivariate modeling - Building the model - High level process - Building the model - Considerations - Core modeling techniques - Regression - Generalized Linear Models - Specifying a GLM - Popular GLMs - Member retention modeling - GLM bottom line |
| James Guszcza, Deloitte |
| The Cookbook for Predictive Analytics |
| A tactical, practitioner's look at succeeding with predictive analytics, full of examples and tips. From the Resource: "The most important factor that can lead to successful implementation of predictive analytics is the availability of useful information. This is not something that exists everywhere. Only certain types of industries have right data useful for predictive analytics, e.g., insurance, credit finance, direct marketing, etc. The second factor is intimate knowledge of business domains and data. The third factor, which may be most important one, is intuition and insight driven by expericence. It is noted that this cannot be obtained without the second factor." |
| Rosella |