Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals' forecasting

Machine learning (ML) techniques within the artificial intelligence (AI) paradigm are radically transforming organizational decision-making and businesses' interactions with external stakeholders. However, in time series forecasting for call center management, there is a substantial gap between the potential and actual use of AI-driven methods. This study investigates the capabilities of ML models for intra-daily call center arrivals' forecasting with respect to prediction accuracy and practicability. We analyze two datasets of an online retailer's customer support and complaints queue comprising half-hourly observations over 174.5 weeks. We compare practically relevant ML approaches and the most commonly used time series models via cross validation with an expanding rolling window. Our findings indicate that the random forest (RF) algorithm yields the best prediction performances. Based on these results, a methodological walk-through example of a comprehensive model selection process based on cross-validation with an expanding rolling window is provided to encourage implementation in individual practical settings.

Automated summary

This study investigates the capabilities of ML models for intra-daily call center arrivals' forecasting, finding that the random forest algorithm yields the best prediction performances. A methodological walk-through example of model selection process is provided to encourage implementation in practical settings.