Abstract

By advancement in digital marketing, business-to-business (B2B) buyers carry out over half of the buying process through digital touchpoints before they establish any significant contact with the B2B seller. Knowing the buying stage of a potential buyer can bring a substantial advantage to the B2B seller given the complexity of the transaction and the associated value. In this paper, the authors propose a machine learning approach to infer the stages of the B2B buying journey by observing the online browsing behavior of buyer companies. It is shown that observing the buyer's online behavior effectively allows us to estimate the buying stages with high accuracy by utilizing the hidden Markov models. Managers in B2B seller companies may use these techniques for adjusting their marketing efforts to improve the fit with the information demands of the B2B buyer prospects along with their buying journey, and thus, improve the hit rate of marketing and sales activities.

Original languageEnglish
Pages (from-to)126-133
Number of pages8
JournalIndustrial Marketing Management
Volume97
Early online date13 Jul 2021
DOIs
Publication statusPublished - Aug 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • B2B buyer journey
  • Hidden buying stage
  • Hidden Markov model
  • Internet browsing

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