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.

Loop-invariant synthesis is the basis of every program verification procedure. Due to its undecidability in general, a tool for invariant synthesis necessarily uses heuristics. Despite the common belief that the design of heuristics is vital for the effective performance of a verifier, little work has been performed toward obtaining the optimal heuristics for each invariant-synthesis tool. Instead, developers have hand-tuned the heuristics of tools. This study demonstrates that we can effectively and automatically learn a good heuristic via reinforcement learning for an invariant synthesizer PCSat. Our experiment shows that PCSat combined with the heuristic learned by reinforcement learning outperforms the state-of-the-art solvers for this task. To the best of our knowledge, this is the first work that investigates learning the heuristics of an invariant synthesis tool.