Predictive Analytics

Predicting customer churn requires collecting and analyzing vast amounts of experiential learning, social posts, primary research with current and former customers, and transactional and financial data.  

Using the power of GenAI and Machine Learning (ML), our clients have identified customers most likely to churn or to reduce their purchase rate; applied targeted interventions and experiments; and learned continuously to improve the predictive accuracy. 

This approach is now leading our clients to replace quality audits with a complete analysis of agent (and team) performance and to augment low response rate surveys with customer interaction and transaction predictive metrics.

We can also collect input and data to identify and quantify pain points and friction from the frontline team members using the WOCAS tool and other methods.