Skip to content

Question: How are contact centers and their systems using predictive analytics?


Contact centers utilize predictive analytics in a number of ways to anticipate the probability of future behaviors or occurrences, and their potential impact on the customer and employee experience and bottom line. Predictive analytics is a flexible AI-based technology that is being leveraged by a growing number of contact center solutions (e.g., intelligent virtual agents (IVAs), workforce management (WFM), analytics-enabled quality management (AQM), predictive behavioral routing, knowledge management, interaction analytics, and more). Here are some of the current contact center use cases:

  • Understanding customer intent and providing agents with real-time guidance and/or customers with proactive assistance, e.g., offering IVA support or the option to chat with a live agent to a customer browsing a website 
  • Anticipating when to escalate a customer from an IVA to a live agent 
  • Routing interactions to the agent who is the best match for the customer 
  • Generating quality scores for 100% of customer interactions, based on data from a historical analysis of manually scored interactions
  • Predicting customer satisfaction or Net Promoter Score (NPS) for all digital and voice interactions, based on results from similar contacts
  • Building customized predictive models for customer loyalty, churn, likelihood to purchase, and other business outcomes 
  • Predicting propensity for customers to pay or the probability an account will decline into further stages of delinquency or return to good standing, and determining the most cost-effective settlement offers and their timing
  • Building a predictive model to identify the drivers of employee churn to enable early intervention and improve retention, as well as to assist with hiring recommendations for the future
  • Automatically finding the “best fit” algorithm to apply to each WFM forecast and evaluating long-term “what-if” planning forecasts
  • Delivering the most appropriate knowledge assets to employees and customers (via self-service applications)