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Adaptive Decision Manager

Pega Adaptive Decision Manager (ADM) is a component that allows the business to build self-learning, adaptive models and continuously improve predictions about customer behavior. A key capability of Pega Decision Management, ADM automatically detects change and acts on it in real time, enabling business processes and customer interactions to be instantly adapted to account for changing customer interests and needs.

Instead of forcing businesses to rely on static models that quickly lose touch with the real world, ADM ensures predictions are as accurate, reliable and up-to-date as possible. It can establish customer preferences without any need for historical data. In addition, working with Pega Predictive Analytics Director, it can automatically adapt predefined predictive models during customer interactions.

Because ADM makes predictive models “self-learning,” you can rapidly and continuously increase the accuracy of your decisions. The result is automated processes that deliver the most relevant responses, offers and actions to each customer.

Key Features

Increase the Success of Your Customer Strategies

  • Self-learning keeps predictive models up-to-date, leveraging customer responses to adjust planned offers and actions as interactions take place.
  • Automatic prioritization takes into account such factors as predicted propensity to buy, number of cases on which the propensity is based and propositions that have proven effective with the particular customer to recommend the Next-Best-Action or offer.

Enhance Your Operations

  • Real-time adjustment to predictive decisions lets more processes be automated.
  • Multiple factors such as company policies, margin, marketing weights and eligibility rules can be used to adjust Next-Best-Actions and more successfully align customer intent with business objectives.
  • Extensive real-time monitoring and reporting provide visual insight into the performance of propositions.

Quickly Reap the Value of Adaptive Models in Your Business Processes

  • Business-oriented design tools provide a quick, simple way to define adaptive models and specify potential inputs, response history and window size.
  • Multiple adaptive models can be run concurrently for the same proposition to best capture changing trends and rapidly scale up the number of models that can be used with a proposition.
  • A multi-dimensional analytical profiler, auto-grouping, correlation detection, adaptive prioritization and alerts that signal the opportunity to analyze new data enable robust, non-linear predictive models to be systemgenerated, without the need for on-call statisticians or IT staff.
  • Automated data preparation includes binning, grouping, recoding and predictor selection to enhance the accuracy of models developed using real-time data.
  • Easy integration leverages third-party models to increase the value of current investments in modeling technology.
  • Combining adaptive models with other types in a strategy supports model training, combined short- and long-term adaptation and overrides for quick reaction to competitive, supply or price changes.

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