Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables

Theresa Maria Rausch,Daniel Baier,Tobias Albrecht

doi:10.1007/s11573-021-01075-4

Abstract

Modern call centers require precise forecasts of call and e-mail arrivals to optimize staffing decisions and to ensure high customer satisfaction through short waiting times and the availability of qualified agents. In the dynamic environment of multi-channel customer contact, organizational decision-makers often rely on robust but simplistic forecasting methods. Although forecasting literature indicates that incorporating additional information into time series predictions adds value by improving model performance, extant research in the call center domain barely considers the potential of sophisticated multivariate models. Hence, with an extended dynamic harmonic regression (DHR) approach, this study proposes a new reliable method for call center arrivals’ forecasting that is able to capture the dynamics of a time series and to include contextual information in form of predictor variables. The study evaluates the predictive potential of the approach on the call and e-mail arrival series of a leading German online retailer comprising 174 weeks of data. The analysis involves time series cross-validation with an expanding rolling window over 52 weeks and comprises established time series as well as machine learning models as benchmarks. The multivariate DHR model outperforms the compared models with regard to forecast accuracy for a broad spectrum of lead times. This study further gives contextual insights into the selection and optimal implementation of marketing-relevant predictor variables such as catalog releases, mail as well as postal reminders, or billing cycles.

Highlights

In the retail industry, typical stages along the customer journey like order taking, after-sales service, and complaint resolution can be completed online (Gensler et al 2012; Verhoef et al 2015)
We extend the established Dynamic Harmonic Regression (DHR) model, which utilizes a sum of sinusoidal terms (i.e., Fourier terms) as predictors to handle periodic seasonality and an autoregressive integrated moving average (ARIMA) error to capture short-term dynamics, by including predictor variables in the considered information space to generate predictions
Combining the strengths of different model types investigated by previous research, this study proposes a new method for call center arrivals’ forecasting that is able to capture the dynamics of time series and, at the same time, include contextual information in the form of predictor variables

Summary

Introduction

Typical stages along the customer journey like order taking, after-sales service, and complaint resolution can be completed online (Gensler et al 2012; Verhoef et al 2015). To provide the correct number of call center agents as customer service representatives at the right time and to evaluate their required areas of expertise, call arrival volumes in different queues have to be predicted reliably in advance. In this regard, preceding literature in the fields of operations management and forecasting so far focused on optimizing the opposite tendency of staffing costs and customer waiting times by enhancing forecast accuracy of predominant time series models (Dean 2007; Gans et al 2003). Such time series models generate predictions based on the time series’ previous values without including any contextual data or other additional information available

Results

Discussion

Conclusion