Machine Learning applications with Dataiku: Emergency Caller Prediction for More Accurate Contact - Clariba website

Optimizing the customer experience with better resource allocation is often a critical objective for businesses or governmental agencies. The following case study concerns the Emergency Services Contact Center of a large European metropolitan city. Their objective was to accurately predict the volume of inbound calls to be able to improve agent rostering and ensure optimal response times for callers in serious need of assistance. The solution had to offer high reliability thresholds, as it would be fatal if there were peaks where the supply of operators (police, fire, ambulance, etc.) could not meet the demand for incoming calls. In this article, we will describe the solution used to achieve this purpose, based on real examples applied to the use case.