The right journey for every customer with AI decisioning
Find the perfect offer to move customers to the latest products, speeds, and services
Optimize upsell campaigns to drive customers to the latest packages and plans
React quickly to emergency situations or outages with personalized customer communications
Drive customer loyalty with personalized service campaigns
Create targeted campaigns that increase customer lifetime value
Reduce call volume and drive digital engagement
From models that predict to agents that decide
Marketers have typically used a type of machine learning (ML) called supervised learning to build predictive models. These models are used for applications like churn prediction, category affinity, cross-sell propensity, and next best offer. In this white paper, we explain how predictive models need to be supplemented with reinforcement learning (RL) models for decisioning.
Read the paper
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