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Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows

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Abstract

The widespread availability of Customer Relationship Management applications in modern organizations, allows companies to collect and store vast amounts of high-detailed customer-related data. Making sense of these data using appropriate methods can yield insights into customers’ behaviour and preferences. The extracted knowledge can then be explored for marketing purposes. Social Network Analysis techniques can play a key role in business analytics. By modelling the implicit relationships among customers as a social network, it is possible to understand how patterns in these relationships translate into competitive advantages for the company. Additionally, the incorporation of the temporal dimension in such analysis can help detect market trends and changes in customers’ preferences. In this paper, we introduce a methodology to examine the dynamics of customer communities, which relies on two different time window models: a landmark and a sliding window. Landmark windows keep all the historical data and treat all nodes and links equally, even if they only appear at the early stages of the network life. Such approach is appropriate for the long-term analysis of networks, but may fail to provide a realistic picture of the current evolution. On the other hand, sliding windows focus on the most recent past thus allowing to capture current events. The application of the proposed methodology on a real-world customer network suggests that both window models provide complementary information. Nevertheless, the sliding window model is able to capture better the recent changes of the network.

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Notes

  1. A video version of this figure is available online at http://www.youtube.com/watch?v=eEQpjspkj_8 (landmark window) and http://www.youtube.com/watch?v=X4_jI8Q4cWQ (sliding window).

  2. A video version of this figure is available online at http://www.youtube.com/watch?v=SyR5jmU6OUk.

  3. http://www.ii.pwr.wroc.pl/~brodka/ged.php.

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Acknowledgments

The authors acknowledge the support of the European Commission through the project MAESTRA (Grant Number ICT-750 2013-612944) and projects “NORTE-07-0124-FEDER-000056/59”, financed by the North Portugal Regional Operational Programme (ON.2—O Novo Norte), under the National Strategic Reference Framework (NSRF), through the Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT). Márcia Oliveira was also funded by FCT, under the PhD grant SFRH/BD/81339/2011. We also thank the anonymous reviewers for their valuable comments and suggestions on earlier versions of this manuscript. A special thanks to Rui Sarmento for helping us run the GED method script in Microsoft SQL Server Express 2012.

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Authors and Affiliations

  1. FEP, School of Economics and Management, University of Porto and LIAAD/INESC TEC, Rua Dr. Roberto Frias, 4200-464, Porto, Portugal

    Márcia Oliveira & João Gama

  2. FEP, School of Economics and Management, University of Porto, Rua Dr. Roberto Frias, 4200-464, Porto, Portugal

    Américo Guerreiro

Authors
  1. Márcia Oliveira
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  2. Américo Guerreiro
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  3. João Gama
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Correspondence to Márcia Oliveira.

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Oliveira, M., Guerreiro, A. & Gama, J. Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows. Soc. Netw. Anal. Min. 4, 208 (2014). https://doi.org/10.1007/s13278-014-0208-2

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