Identification of influencers - Measuring influence in customer networks

  • Authors:
  • Christine Kiss;Martin Bichler

  • Affiliations:
  • Internet-based Information Systems, Department of Informatics, TU München, Germany;Internet-based Information Systems, Department of Informatics, TU München, Germany

  • Venue:
  • Decision Support Systems
  • Year:
  • 2008

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Abstract

Viral marketing refers to marketing techniques that use social networks to produce increases in brand awareness through self-replicating viral diffusion of messages, analogous to the spread of pathological and computer viruses. The idea has successfully been used by marketers to reach a large number of customers rapidly. If data about the customer network is available, centrality measures provide a structural measure that can be used in decision support systems to select influencers and spread viral marketing campaigns in a customer network. Usage stimulation and churn management are examples of DSS applications, where centrality of customers does play a role. The literature on network theory describes a large number of such centrality measures. A critical question is which of these measures is best to select an initial set of customers for a marketing campaign, in order to achieve a maximum dissemination of messages. In this paper, we present the results of computational experiments based on call data from a telecom company to compare different centrality measures for the diffusion of marketing messages. We found a significant lift when using central customers in message diffusion, but also found differences in the various centrality measures depending on the underlying network topology and diffusion process.