Do your customers look favorably on their interactions with your company’s call center? What do they like and dislike the most? Conversations with call center representatives are usually less gratifying than a face-to-face meeting, but most customers still breathe a deep sigh of relief when they reach a real human being. They often complain about the long time spent on hold, the time to resolution, and the need to re-explain issues after every transfer. Furthermore, enduring a long call won’t guarantee that the original problem will get resolved. Call center managers conduct satisfaction surveys to track customer ratings on employee performance and call volume. One of their goals is to ensure that the call center has a sufficient number of staff and resources to handle the workload. But historical data only offers conclusions about the past or, at best, the present. What managers really want is the data to help them look forward.
Predictive models extrapolate from historical data so that managers can anticipate future situations and plan for adequate staffing. They analyze call volume and individual performance metrics, such as service level, time to resolution, and resulting customer satisfaction. Workflows tie those analytics into other areas so managers can predict the impact of product launches, price changes, new features, and holiday spikes in volume to optimize staffing levels. Successful outcomes include greatly reduced on-hold times and more time for call center representatives to solve problems. With augmented machine learning, managers can build predictive models without coding. Predictive models can help increase levels of customer satisfaction and improve net promoter scores.