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International Conference on Business Information Systems

BIS 2016: Business Information Systems Workshops pp 36–43Cite as

Targeting Advertising Scenarios for e-Shops Surfers

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 263))

Abstract

Buying goods in e-shops turns to be a very attractive activity for internet surfers. The shopping behaviour of online customers holds attention not only of e-shop businesses, but also the researchers analysing perspectives of online marketing. The most serious consideration is given to discovery of consumer interest patterns, visualization of customer online shopping behaviour or evaluation of advertising campaigns with the goal to attract more e-shop visitors and to grow sales. Unfortunately, selection of the most potentially promising customers draws less attention in the research works. The paper proposes new method of selecting the best e-shop clients for which we can offer the personalized advertising campaign. This study demonstrates how by using only e-shop clickstream data we can identify the potential buyers, select the best suitable advertising campaign and increase the e-shop sales level. The research is based on real clickstream data of two e-shops.

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Author information

Authors and Affiliations

  1. Department of Informatics, Vilnius University, Universiteto Str.3, Vilnius, Lithuania

    Dalia Kriksciuniene & Virgilijus Sakalauskas

Authors
  1. Dalia Kriksciuniene
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  2. Virgilijus Sakalauskas
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Corresponding author

Correspondence to Virgilijus Sakalauskas .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. Leipzig University, Leipzig, Germany

    Rainer Alt

  3. Leipzig University, Leipzig, Germany

    Bogdan Franczyk

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Kriksciuniene, D., Sakalauskas, V. (2017). Targeting Advertising Scenarios for e-Shops Surfers. In: Abramowicz, W., Alt, R., Franczyk, B. (eds) Business Information Systems Workshops. BIS 2016. Lecture Notes in Business Information Processing, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-319-52464-1_4

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