E-loyalty networks in online auctions

Wolfgang Jank, Inbal Yahav

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Creating a loyal customer base is one of the most important, and at the same time, most difficult tasks a company faces. Creating loyalty online (e-loyalty) is especially difficult since customers can "switch" to a competitor with the click of a mouse. In this paper we investigate e-loyalty in online auctions. Using a unique data set of over 30,000 auctions from one of the main consumer-to-consumer online auction houses, we propose a novel measure of e-loyalty via the associated network of transactions between bidders and sellers. Using a bipartite network of bidder and seller nodes, two nodes are linked when a bidder purchases from a seller and the number of repeat-purchases determines the strength of that link. We employ ideas from functional principal component analysis to derive, from this network, the loyalty distribution which measures the perceived loyalty of every individual seller, and associated loyalty scores which summarize this distribution in a parsimonious way. We then investigate the effect of loyalty on the outcome of an auction. In doing so, we are confronted with several statistical challenges in that standard statistical models lead to a misrepresentation of the data and a violation of the model assumptions. The reason is that loyalty networks result in an extreme clustering of the data, with few high-volume sellers accounting for most of the individual transactions. We investigate several remedies to the clustering problem and conclude that loyalty networks consist of very distinct segments that can best be understood individually.

Original languageEnglish
Pages (from-to)151-178
Number of pages28
JournalAnnals of Applied Statistics
Issue number1
StatePublished - Mar 2012
Externally publishedYes


  • Clustering
  • Electronic commerce
  • Functional data
  • Model assumptions
  • Online auction
  • Principal component analysis
  • Random effects model
  • Weighted least squares


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