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Agent Based Customer Modelling


  • David Collings

    () (BT Laboratories)

  • A. A. Reeder

    () (BT Laboratories)

  • Iqbal Adjali

    () (BT Laboratories)

  • P. Crocker

    (BT Laboratories)

  • M. H. Lyons

    () (BT Laboratories)


Understanding the rate of adoption of a telecommunications service in a population of customers is of prime importance to ensure that appropriate network capacity is provided to maintain quality of service. This problem goes beyond assessing the demand for a product based on usage and requires an understanding of how consumers learn about a service and evaluate its worth. Field studies have shown that word of mouth recommendations and knowledge of a service have a significant impact on adoption rates. Adopters of Internet can be influenced through communications at work or children learning at school. In this paper we present an agent based model of a population of customers, with rules based on field data, which is being used to understand how services are adopted. Of particular interest is how customers interact to learn about the service through their communications with other customers. We will show how the different structure, dynamics, and distribution of the social networks affect the diffusion of a service through a customer population. Our model shows that real world adoption rates are a combination of these mechanisms which interact in a non-linear and complex manner. This complex systems approach provides a useful way to decompose these interactions. The paper concludes by suggesting improvements to the model and directions for future work.

Suggested Citation

  • David Collings & A. A. Reeder & Iqbal Adjali & P. Crocker & M. H. Lyons, 1999. "Agent Based Customer Modelling," Computing in Economics and Finance 1999 1352, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:1352

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