SLRM node

The Self-Learning Response Model (SLRM) node enables you to build a model that you can continually update, or reestimate, as a dataset grows without having to rebuild the model every time using the complete dataset. For example, this is useful when you have several products and you want to identify which product a customer is most likely to buy if you offer it to them. This model allows you to predict which offers are most appropriate for customers and the probability of the offers being accepted.

The model can initially be built using a small dataset with randomly made offers and the responses to those offers. As the dataset grows, the model can be updated and therefore becomes more able to predict the most suitable offers for customers and the probability of their acceptance based upon other input fields such as age, gender, job, and income. The offers available can be changed by adding or removing them from within the node dialog box, instead of having to change the target field of the dataset.

When coupled with IBM® SPSS® Collaboration and Deployment Services, you can set up automatic regular updates to the model. This process, without the need for human oversight or action, provides a flexible and low-cost solution for organizations and applications where custom intervention by a data miner is not possible or necessary.

Example. A financial institution wants to achieve more profitable results by matching the offer that is most likely to be accepted to each customer. You can use a self-learning model to identify the characteristics of customers most likely to respond favorably based on previous promotions and to update the model in real time based on the latest customer responses.