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Dynamic Decision Support in the Internet Marketing Management

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Transactions on Computational Collective Intelligence XXIX

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 10840))

Abstract

The article deals with the problem of selecting an advertisement variant on the basis of dynamically-changing values of evaluation criteria. Therefore, a framework, used in an online environment, of a dynamic multi-criteria decision analysis (DMCDA) has been prepared. The framework was based on the PROMETHEE method which makes it possible to carry out a very detailed analysis of a decision process and obtained solutions. While applying the prepared framework, a number of ad variants were considered on the basis of the data collected during a subjective study and a field experiment. In the course of solving the decision problem, the advertiser’s and website operator’s perspectives as well as two aggregation strategies of dynamic data were considered. As a result, the following was obtained: partial rankings of variants, global rankings considering the advertiser’s and publisher’s points of view, GDSS rankings pointing to compromise solutions by merging the two points of view. The obtained solutions were verified by means of: examining correlation coefficients, a GAIA analysis and an analysis of ranking robustness to preference changes. The end result was that the most satisfying advertiser and publisher were determined.

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Author information

Authors and Affiliations

  1. The Jacob of Paradies University, Teatralna 25, 66-400, Gorzów Wielkopolski, Poland

    Paweł Ziemba

  2. West Pomeranian University of Technology, Żołnierska 49, 71-210, Szczecin, Poland

    Jarosław Jankowski

  3. University of Szczecin, Mickiewicza 64, 71-101, Szczecin, Poland

    Jarosław Wątróbski

Authors
  1. Paweł Ziemba
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  2. Jarosław Jankowski
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  3. Jarosław Wątróbski
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Corresponding author

Correspondence to Paweł Ziemba .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, Australia

    Ryszard Kowalczyk

Appendices

Appendix 1

See Table 8

Table 8. PROMETHEE GDSS rankings
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Appendix 2

See Table 9.

Table 9. Values of variants for individual criteria
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Appendix 3

See Tables 10 and 11.

Table 10. Rankings of variants for subsequent time periods – advertiser’s perspective
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Table 11. Rankings of variants for subsequent time periods – publisher’s perspective
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Appendix 4

See Table 12.

Table 12. Global rankings of variants –TPEA and TPDA strategies
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Ziemba, P., Jankowski, J., Wątróbski, J. (2018). Dynamic Decision Support in the Internet Marketing Management. In: Nguyen, N., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence XXIX. Lecture Notes in Computer Science(), vol 10840. Springer, Cham. https://doi.org/10.1007/978-3-319-90287-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-90287-6_3

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