Demand Forecasting and Capacity Planning

Failure to forecast demand growth with  sufficient accuracy has recently led many  experienced providers to inadequate capacity  provisioning, held orders, and sometimes  regulatory penalties or churn of customers  away from competitors.


Many telecommunications companies are placing an increasingly high economic value on the ability to accurately forecast demand for local loop and access products, ranging from primary residential lines (1FRs) to wireless, data, and bundled local/long-distance services, over a planning horizon of several years. Failure to forecast demand growth with sufficient accuracy has recently led many experienced providers to inadequate capacity provisioning, held orders, and sometimes regulatory penalties or churn of customers away to competitors.

In response to the need for more accurate demand and network load forecasts, Cox Associates has developed and successfully applied a new approach to demand forecasting that can radically improve forecast accuracy compared to even the most sophisticated time series methods.

Two pragmatic complexities have previously prevented straight-forward implementation of this strategy. They are:

  • The almost infinite number of ways to partition the units of analysis into groups. Identifying groupings that minimize forecast errors has been a continuing challenge for marketers and statisticians.
  • Difficulties in predicting the flow of units among the different groups over time.

Our technique provides constructive solutions to both problems, as follows.

  • Individuals are automatically partitioned into groups (defined by conjunctions of attribute values) to minimize forecast errors. This partitioning is accomplished via the data-mining and artificial intelligence technique of classification tree analysis. The quantities forecast — product acquisition, penetration, usage, and retention/attrition/churn rates — are modeled as transition rates in a multi-state, semi- Markov process with covariates that include demographic, billing, and usage history information. Transition rates are estimated as functions of these covariates via the classification tree algorithm.
  • Transitions of individual units among groups are modeled by applying the transition rates estimated in part (a) to the groups. This determines the evolution of the distribution of individuals among groups over time. A dynamic simulation model is used to integrate the transition rate and covariate information and to predict the resulting changes in product demands over time.

More technical references can be found here


The new technique, which we call Classification-Tree Compartmental Forecasting, is based on the following simple steps, implemented with the help of sophisticated data- mining and stochastic simulation algorithms.

Several features make classification trees particularly well suited to avoid many of the threats to valid causal inference identified in this white paper. Specifically:

  • Divide units of demand- or load-analysis (households, firms, subscribers, base stations, etc.) into groups with relatively homogeneous demand behaviors, e.g., for acquisition, usage, and churn or attrition of local access products.
  • Forecast the behavior of each group. This can be done accurately, by construction.
  • Sum forecasts over all groups to obtain aggregate forecasts

Related Publications

  • Cox LA Jr, Wong C. State transition model for customer relationship management.   Direct Marketing Analytics Journal. May, 2006, 9-15.
  • Cox, LA Jr. Predicting and optimizing customer behaviors. Chapter 12 in A. Labbi (Ed.), Handbook of Integrated Risk Management for E-Business: Measuring, Modeling, and Managing Risk. J. Ross Publishing. February, 2005.
  • Cox, L.A. Jr., 2002. Data mining and causal modeling of customer behaviors. Telecommunications Systems. 21(2-4):349-381.
  • Cox, L.A., Jr., and Popken, D.A., 2002. A hybrid system-identification method for forecasting telecommunications product demands. International Journal of Forecasting Volume 18, Issue 4, October-December 2002, Pages 647-671
  • Cox, L.A. Jr., 2001. Forecasting demand for telecommunications products from cross-sectional data. Telecommunications Systems, 16:3, 439-456.