Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn recent years, artificial intelligence and cognitive technologies are actively being adopted in
industries that use conversational marketing. Workforce managers face the constant challenge
of balancing the priorities of service levels and related service costs. This problem is especially
common when inaccurate forecasts lead to inefficient scheduling decisions and in turn result in
dramatic impact on the customer engagement and experience and thus call center’s profitability.
The main trigger of this project development was the Company X’s struggle to estimate the
number of inbound phone calls expected in the upcoming 40 days. Accurate phone call volume
forecast could significantly improve consultants’ time management, as well as, service quality.
Keeping this goal in mind, the main focus of this internship is to conduct a set of experiments
with various types of predictive models and identify the best performing for the analyzed use
case. After a thorough review of literature covering work related to time series analysis, the
empirical part of the internship follows which describes the process of developing both,
univariate and multivariate statistical models. The methods used in the report also include two
types of recurrent neural networks which are commonly used for time series prediction. The
exogenous variables used in multivariate models are derived from the Media Planning
department of the company which stores information about the ads being published in the
newspapers. The outcome of the research shows that statistical models outperformed the neural
networks in this specific application. This report covers the overview of statistical and neural
network models used. After that, a comparative study of all tested models is conducted and one
best performing model is selected. Evidently, the experiments showed that SARIMAX model
yields best predictions for the analyzed use-case and thus it is recommended for the company
to be used for a better staff management driving a more pleasant customer experience of the
call center
