Forecasting demand for telecommunications products from cross-sectional data

A crucial planning problem for many telecommunications companies is how best to forecast changes in demand for their products over the next several years. This paper presents a new approach to demand forecasting that performs well compared to even sophisticated time series methods but that re quires far less data. It is based on the following simple idea: Divide units of analysis (census blocks, customers, etc.) into groups with relatively homogeneous behaviors, forecast the behavior of each group (which can be done easily, by construction), and sum over all groups to obtain aggregate forecasts. Identifying groupings of customers to minimize forecast errors is a difficult combinatorial challenge that we address via the data-mining technique of classification tree analysis. Product acquisition rates are modeled as transition rates in a multi-state simulation model. The dynamic simulation model is used to integrate the transition rate and covariate information and to predict the resulting changes in product demands over time.