ecosystem.Ai Dynamic Recommenders

Dynamic recommenders use real-time feedback to learn how to more effectively rank offers.

The real-time feedback learning system can be activated with or without data, how this is done depends on the availability of your data. Whether there is none available, or using data on user context (demographic, behavioral, etc.) and historical behavior.

Each time a user interacts with the real-time feedback learning system, that interaction is logged. Every logged interaction then advances the state of knowledge of the system. This knowledge can be further used to enhance the effectiveness of the traditional data science process, if running in parallel. Ensuring effective learning without focusing too extensively on a single option through testing.

The system uses an experimentation based methodology, which is a testing approach to presenting offers to customers. Rather than selecting just one solution and missing the opportunity to explore the rest, experimentation allows you to run multiple tests at the same time. The offers to experiment with could be in the form of products, customer engagement messages, design constructs, special offers, and more.

About Data

Dynamic recommendation does not require any data to be available, but can incorporate additional data as and when it becomes available.

Starting without data does not affect the activation of the recommender, all that is required is a list of offers. Having no data could be due to opting in for a cold-start scenario, such as if a new product is being launched and there is no historical data available. It could also be due to capacity and/or technical constraints associated with access to the needed data.

If data is available in addition to the offer list, it can help to improve the effectiveness of the system’s learning. If segmentation variables are available those can be used to add context to the learning. Context can also be set at a customer level for truly personalized predictions. If historical data is available it can be used to provide a more informed starting point.

Time Dependence

Systems involving human behavior will always be affected by time.

Time alters human behavior for a number of reasons: evolving trends, communal rituals, personal events and environmental changes. These changes are often not consistent, and will therefore happen at varying points in the time scale.

Dynamic recommenders incorporates a range of functionality, allowing human changes to be captured and effectively taken into account:

• Real-time learning

  • In the moment capture of activity.

• Sophisticated forgetfulness

  • Offer level options that can be set based on applications, and adjusted as the application is running.

• Repeated customer interactions

  • Sophisticated options that account for human ritual. Tuned based on applications, and adjusted as the application is running.