Adaptive models are self-learning models that you can use in strategies to improve predictions about changing customer interests and needs.

In a standard configuration of an adaptive model rule, the rule context reflects the proposition hierarchy so that a unique adaptive model is generated for every combination of Business issue, Group, Proposition, Direction, and Channel values. In many situations, you might need to add or remove particular model identifiers from the adaptive model rule to increase or decrease model granularity.

This tutorial explains how to create feature-based adaptive models for two groups of consumer financial products instead of having six adaptive models for each type of loan and credit card. Adaptive models for particular products in a group are so similar that is preferable to have one adaptive model at the group level.

Strategy designers can change the standard setup of the adaptive model rule and customize model identifiers in a decisioning application. Model identifiers that are customized in the adaptive model rule are called flexible partitions and can be used in the following situations:

• You introduce new offers frequently. If you create offer-level models, you experience the cold-start problem. Because of the lack of initial evidence for adaptive models, the models need some time before they can collect customer responses.
• Your propositions are not offered very often (for valid reasons), which results in slowly learning models.​ At the same time, you might have similar propositions with many more responses and you would like to consolidate all the responses for one model.
• You have propositions that are similar to each other so they will be perceived by customers in a very similar way.
• The proposition hierarchy that you use does not match naturally with the organization of products or offers.
• You want to create more accurate models and have enough response data, so you introduce another level in the model partitioning. For example, in marketing strategies you can add model identifiers such as Treatment or Call Reason.
• You need to disregard the standard hierarchy in favor of feature-based models. For example, you need adaptive models for product attributes such as brand, price range, or color.
• You need to limit the number of models because there are too many propositions to handle with the Adaptive Decision Manager cluster, or you want to monitor and review models that have very similar behavioral patterns.​