For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed opportunities for coaching, follow-up, and rectification. Therefore, call centers can benefit from a model predicting customer satisfaction on calls where the customer did not complete the survey. Given that CSAT is a closely monitored KPI, it is critical to minimize any bias in the average predicted CSAT (pCSAT). In this paper, we introduce a method such that predicted CSAT (pCSAT) scores accurately replicate the distribution of survey CSAT responses for every call center with sufficient data in a live production environment. The method can be applied to many multiclass classification problems to improve the class balance and minimize its changes upon model updates.

Automation covers technologies across many processes and fields. In regard to customer service it's using technologies instead of people to accomplish both customer facing and back end tasks. Customers are leaning into automation according to McKinsey research. Though the majority surveyed (79%) use the telephone to connect to service, it's also the channel that most (75%) don't want to use in the future. Instead, 81% are more interested in using email and 62% in website/self-service in the future.

As the grip of COVID-19 intensifies on an increasingly splintering world and more and more people shelter themselves indoors for work, life, and everything in between, businesses large and small are finding themselves struggling with an overbearing demand for answers. People are looking for a semblance of clarity amid the confusion and uncertainty surrounding the pandemic. And service managers are finding it increasingly difficult to navigate the surging demand as support agents turn as panicked, anxious, and unsure as customers themselves. Businesses spanning restaurants, food delivery, meal kits, healthcare, gaming, education, technology, streaming, e-commerce, and online subscription services are registering alarmingly high volumes of support tickets. Where the support reps are finding themselves overwhelmed by the burgeoning numbers, frustrated customers are experiencing hours-long wait times and turning more distraught.

Building on my April 2018 column "Using AI, Bots, Big Data, and Analytics to Reduce Demand for Support"1, one of the seven vexing challenges facing customer experience (CX) leaders, today I'll address the second challenge to increase First Contact Resolution (FCR). The power of AI, bots, Big Data, and analytics can now enable us to address the overall goal "How can we create and sustain a consistent and awesome customer experience across multiple channels & touch points?" and thereby increase sustainable revenues, realize higher margins, and sustain greater levels of customer satisfaction and loyalty. Let's dig into the second challenge, Increase FCR. Study after study has confirmed that first contact resolution in the contact center and other support functions is the biggest driver of customer satisfaction2. However, most companies rely overly much on point statistics like average FCR, and they neglect the fact that customers today start their inquiry or search for support online, failing that in the IVR system, only to find themselves speaking, emailing, or chatting with a customer service agent.