Planning Monitoring and Maintenance

In a full-fledged deployment and integration of modeling results, your data mining work may be ongoing. For example, if a model is deployed to predict sequences of e-basket purchases, this model will likely need to be evaluated periodically to ensure its effectiveness and to make continuous improvements. Similarly, a model deployed to increase customer retention among high-value customers will likely need to be tweaked once a particular level of retention is reached. The model might then be modified and re-used to retain customers at a lower but still profitable level on the value pyramid.

Task List

Take notes on the following issues and be sure to include them in the final report.

• For each model or finding, which factors or influences (such as market value or seasonal variation) need to be tracked?
• How can the validity and accuracy of each model be measured and monitored?
• How will you determine when a model has "expired"? Give specifics on accuracy thresholds or expected changes in data, etc.
• What will occur when a model expires? Can you simply rebuild the model with newer data or make slight adjustments? Or will changes be pervasive enough as to require a new data mining project?
• Can this model be used for similar business issues once it has expired? This is where good documentation becomes critical for assessing the business purpose for each data mining project.