Proprietary model prioritizes and recommends offers based on historical and contextual usage behavior of customers.
Supervised machine learning model predicts customer propensity for attrition based on historical usage patterns.
This machine learning model forecast revenue estimates for on-going campaigns based on historical performance.
This model allows automated grouping of customers based on different attributes and provides an interactive view of clusters formed.
Machine learned recommendation of next best product that a customer is most likely to buy based on his historical and contextual usage behaviour.
Aspect based sentiment analysis model identifies positive and negative sentiments of customers by analyzing unstructured text like social media posts, customer reviews and email.