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.