Required data

After you define what you want to optimize, you must confirm that you have all the data that you need to implement your plan.

Contact Optimization works with Campaign and requires the following data:

  • Response tracking and analysis, including contact and response history. To manage contact fatigue by ensuring you do not send too many offers to the same person, you must track what you send. To monitor how effective your campaigns and optimizations are, you must track customer response. You can compare the results of contacting a target group versus not contacting members of a statistically similar control group. To evaluate the effectiveness of using Contact Optimization, you might want to hold out a group of proposed contacts that do not undergo optimization and compare that to the results of optimized contacts. Measurement of the benefit of optimization might take multiple forms, for example, increased response rate or ROI, fewer opt-outs, or greater customer satisfaction.
  • Defined offers. You need a list of all the offers that are included in your optimizations as you design your rules and constraints. You might apply your rules and constraints to specific groups of offers. Groups of offers are defined as offer lists, typically based on the offer attributes or types of offers. If you plan to manually enter scores in the centralized score matrix, you need a list of the offers for which you plan to enter score values.
  • Defined segments. You must understand all the segments that you want to optimize across as you design your rules and constraints, as you can constrain the applicability or scope of rules and constraints to specific segments. If you plan to manually enter scores in the centralized score matrix, you need a list of the segments for which you plan to enter score values.
  • Defined scores. As you planned your implementation, you chose a scoring method. You must have a process for generating these scores. For example, if you are populating the scoring matrix with constants, you must choose for which offers and segments you define the scores and determine the actual score values. If you are using computations, you must define the equations. For example, using a derived field to calculate the profitability of a proposed credit limit increase offer that is based on the average carried balance of an individual. If you are using predictive models, you need to collect, aggregate, pre-process, and model your data in your modeling applications.