AI for Customer Care Routing at Wix - Making The First Step of Reinforcement Learning

Please notice: This post is part 2 of "AI for Revolutionizing Customer Care Routing System at Wix".

Call centers, similar to most queueing systems, are traditionally optimized to minimize customers waiting time. Nevertheless, in reality, additional aspects can also tilt the routing decision balance, such as Quality of Service (QoS), fairness and workload distribution. For optimizing the customer experience at Wix, we developed the Expert Smart Routing. It is a data-driven, end-to-end, Reinforcement Learning (RL) system for completely redesigning the way customers get served, resulting in significant improvement in overall customer satisfaction.

In the first post, we walked you through the journey of developing and deploying this solution. We faced the complexities of balancing different objectives and hypothesized that focusing solely on waiting time would lead to suboptimal customer satisfaction. Our findings supported this hypothesis. Now, we are about to dive deeper into the first solution we developed, prior to the RL model, which we call “The Greedy.”

This post will explore the practical value of deploying an intermediate solution when working toward complex deep learning or reinforcement learning models. We’ll cover the motivations behind the Greedy model, along with its logic and algorithm. Furthermore, we will review the development process of this highly interpretable solution which enabled us to deliver value quickly, test project assumptions, and gain valuable experience with real data.

For a full read on Wix Engineering click here