A hybrid predictive model for high-frequency and multi-periodic data in call center of online travel agency

Ctrip.com is a leading online travel company in China, providing travel services including accommodation and flight reservations. With high quality customer service in mind, Ctrip.com has made substantial investments in establishing its own in-house call center. In order to optimize customer satisfaction while controlling operational costs, call center management must determine staffing level at least a week in advance. Our paper addresses this issue by proposing a methodology that predicts the upcoming week's call volume in half-hour increments. The proposed method is a hybrid of a time series TBATS model (Trigonometric seasonal formulation, Box-Cox transform, ARIMA errors, Trend, and Seasonal components), a model of processing time series, and boosting tree theory. For high prediction accuracy, we also considered the effects of related events such as flight delays and ticket sales, and illustrated how to incorporate them as exogenous variables in the tree model part. With this approach, call center management can arrange staffing availability efficiently based on call volume predictions.