In a recent webinar, Nexidia’s Ryan Pellet and Brett Forman, along with Richard Crowe, director of customer insights for Comcast Corporation, discussed how customer interactions from the contact center – combined with the structured data most companies are already collecting and tracking – are perfectly suited for delivering valuable, actionable customer insights. Leading companies like Comcast are successfully mining these insights through predictive analytics to deliver increased customer loyalty and a stronger bottom line.
In case you missed out on the event, we compiled a list of answers to the top three attendee questions:
Webinar Q & A
Q: Why is speech important to predictive analytics?
A: Predictive analytics models require vast amounts of data from multiple sources to deliver predictions of future events based on past examples. The more information that can be fed into the machine learning model, the more precise it becomes. Speech data is a particularly rich source of information, because it conveys what’s on the mind of the customer in words and emotion. Combining these insights with the other source data adds context to form a more robust predictive model.
Q: Why is it important that my company use insights gathered from 100% of our voice data in predictive analytics modeling?
A: The old saying garbage in garbage out applies here. If you feed a predictive analytics model with incomplete or inaccurate data, then the basis for its predictions will be skewed, and you won’t receive reliable, actionable information. If your speech analytics solution can’t scale to investigate 100% of your customer calls, then you can’t quantify the speech data that is helping form the basis for your predictive modeling. Outlying customer events not captured in call samples can hold game changing information that can severely handicap the viability of your predictive model
Q: How do the insights gained from predictive analytics get applied into an organization’s day-to-day operations?
A: In a variety of ways – from real time agent support to realigning operational issues that are negatively affecting customer satisfaction. For example, a leading communications corporation used predictive analytics to forecast customer churn. Now they can respond in real time to caller issues that indicate imminent cancellation by providing their agents with on-screen guidance when churn-associated events are identified during a call. Predictive analytics findings have helped them improve ongoing business operations too, by showing them other opportunities for improving the customer experience that they can prioritize and implement over time.
If you’d like to learn more about what combining speech with predictive analytics can do for your business, follow the link below to listen to the webinar in its entirety.