A data-driven method to predict service level for call centers

In call centers, the service level is an important metric to measure the reasonability of the staffing schedule. Traditional service level calculation methods are based on the queue theory, which has very strict restrictions and is not suitable for real scenarios. Therefore, in this paper, a data-driven method to solve the service level prediction problem is proposed to be used. To this end, the relationship between service level and other factors, such as number of calls, number of agents, time, is explored. Then some features are extracted based on empirical analyses and propose to use decision tree based ensemble methods, like random forest and GBDT, to model the relationship between service level and input features. Finally, extensive experimental results show that the proposed method outperforms other baselines significantly. Especially compared with the traditional queue theory methods, our method improves the performance by 6% and 9% in terms of MAE and MAPE.