Multistage Performance Modelling in Digital Marketing Management

• Autorzy: Jarosław Wątróbski , Jarosław Jankowski , Paweł Ziemba
• Języki publikacji: EN

Abstrakty

EN

Effective online marketing management requires developing new research methods supporting campaign performance evaluation. The paper presents a multistage approach with performance modelling based on Dynamic Multi Criteria Decision Analysis. The crisp and fuzzy versions of the TOPSIS method were used in the process of dynamic modelling. The evaluation was performed in terms of marketing management, taking into account several conflicting criteria (user experience and intensity of advertising content). Real data from advertising servers were used during the evaluation and example decision processes were performed. (original abstract)

Słowa kluczowe

EN

Marketing management; Advertising campaign; e-marketing; User experience testing (UX); Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)

PL

Zarządzanie marketingowe; Kampanie reklamowe; Marketing internetowy; Badania user experience; Metoda TOPSIS

• Czasopismo: Economics & Sociology
• Rocznik: 2016
• Tom: 9
• Numer: nr 2
• Strony: 101--125

Twórcy

autor

Jarosław Wątróbski

• West Pomeranian University of Technology in Szczecin

autor

Jarosław Jankowski

• Wrocław University of Technology, Poland

autor

Paweł Ziemba

• The Jacob of Paradyż University of Applied Sciences in Gorzów Wielkopolski

Bibliografia

• Agrell, P. J., Wikner, J. (1996), An MCDM framework for dynamic systems, International Journal of Production Economics, Vol. 45, No. 1, pp. 279-292.
• Amiri, A., Menon, S. (2003), Efficient Scheduling of Internet Banner Advertisements, ACM Transactions on Internet Technology, Vol. 3, No. 4, pp. 334-346.
• Balcerzak, A. P. (2016), Multiple-criteria Evaluation of Quality of Human Capital in the European Union Countries, Economics & Sociology, Vol. 9, No. 2, pp. 11-26.
• Balcerzak, A. P. (2009), Effectiveness of the Institutional System Related to the Potential of the Knowledge Based Economy, Ekonomista, Vol. 6, pp. 711-739.
• Balcerzak, A. P., Pietrzak, M. B. (2016a), Human Development and Quality of Institutions in Highly Developed Countries, In: M. H. Bilgin, H. Danis, E. Demir, and U. Can (eds.), Financial Environment and Business Development. Proceedings of the 16th Eurasia Business and Economics Society, Springer International Publishing.
• Balcerzak, A. P. Pietrzak, M. P. (2016b), Application of TOPSIS Method for Analysis of Sustainable Development in European Union Countries, In: T. Loster & T. Pavelka (eds.), The 10th International Days of Statistics and Economics, Conference Proceedings, September 8-10, 2016, Prague.
• Behzadian, M., Otaghsara, S. K., Yazdani, M., Ignatius, J. (2012), A state-of the-art survey of TOPSIS applications, Expert Systems with Applications, Vol. 39, No.17, pp. 13051-13069.
• Bouyssou, D., Roy, B. (1993), Aide Multicritere a la decision: Methodes et Cas, Paris, Economica.
• Brajnik, G., Gabrielli, S. (2010), A Review of Online Advertising Effects on the User Experience, International Journal Of Human - Computer Interaction, Vol. 26, No. 10, pp. 971-997.
• Brans, J. P., Mareschal, B. (2005), Promethee Methods, In: Multiple Criteria Decision Analysis. State of the Art Surveys, J. Figueira, S. Greco, M. Ehrgott (ed.), Boston: Springer, 2005, pp. 163-195.
• Brans, J. P., Mareschal, B., Vincke, P. (1984), Promethee: A new family of outranking methods in multicriteria analysis, Operational Research OR'84, pp. 408-421.
• Cannon, H. M. (2001), Addressing New Media With Conventional Media Planning, Journal of Interactive Advertising, Vol. 1, No. 2, pp. 28-42.
• Chakrabarti, D., Kumar, R., Radlinski, F., Upfal, E. (2009), Mortal multi-armed bandits, In: Advances in Neural Information Processing Systems, pp. 273-280.
• Campanella, G., Ribeiro, R. A. (2011), A framework for dynamic multiple-criteria decision making, Decision Support Systems, Vol. 52, No. 1, pp. 52-60.
• Chen, C. T. (2000), Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems, Vol. 114, No. 1, pp. 1-9.
• Chen, C. T., Lin, C. T., Huang, S. F. (2006), A fuzzy approach for supplier evaluation and selection in supply chain management, International Journal of Production Economics, Vol. 102, No. 2, pp. 289-301.
• Chen, Y., Li, K. W., He, S. (2010), Dynamic multiple criteria decision analysis with application in emergency management assessment, Systems Man and Cybernetics (SMC), 2010 IEEE International Conference, IEEE, pp. 3513-3517.
• Chickering, D., Heckerman, D. (2000), Targeted advertising with inventory management, Minneapolis, ACM Special Interest Group on E-Commerce (EC00).
• De Montis, A., De Toro, P., Droste-Franke, B., Omann, I., Stagl, S. (2000), Criteria for quality assessment of MCDA methods, Vienna, Proc. 3rd Biennial Conference of the European Society for Ecological Economics, ESEE.
• Du, H., Xu, Y. (2012), Research on Multi-Objective Optimization Decision Model of Web Advertising - Takes Recruitment Advertisement as an Example, International Journal of Advancements in Computing Technology, Vol. 4, No. 10, pp. 329-336.
• Endress, T., Gear, T. (2015), Stock Prices: Are Intuitive or Deliberate Persons Better Forecasters? Economics & Sociology, Vol. 8, No. 4, pp. 43-50.
• Fishburn, P. C. (1974), Exceptional Paper - Lexicographic Orders, Utilities and Decision Rules: A Survey, Management Science, Vol. 20, No. 11, pp. 1442-1471.
• Giuffrida, G., Reforgiato, D., Tribulato, G., Zarba, C. (2011), A banner recommendation system based on web navigation history, IEEE Symposium on Computational Intelligence and Data Mining, pp. 291-296.
• Guha, S., Cheng, B., Francis, P. (2010), Challenges in measuring online advertising systems, Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, ACM, pp. 81-87.
• Guitouni, A., Martel, J. M. (1998), Tentative guidelines to help choosing an appropriate MCDA method, European Journal of Operational Research, Vol. 109, No. 2, pp. 501-521.
• Gupta, N., Khurana, U., Lee, T., Nawathe, S. (2011), Optimizing Display Advertisements Based on Historic User Trails, ACM SIGIR 2011 Workshop: Internet Advertizing.
• Hashemkhani Zolfani, S., Maknoon, R., Zavadskas, E.K. (2016), An introduction to Prospective Multiple Attribute Decision Making, Technological and Economic Development of Economy, Vol. 22, No. 2, pp. 309-326.
• Hoffman, D. L., Novak, T. P. (1995), Marketing in Hipermedia Computer-Mediated Environment: Conceptual Foundations, Nashville, Vanderbilt University.
• Hoffman, D. L., Novak, T. P. (2000), When Exposure-Based Advertising Stops Making Sense (And What CDNOW Did about It). URL: http://www2000.ogsm.vanderbilt.edu/papers/CDNOW.jan2001.htm.
• Hwang, C. L., Yoon, K. (1981), Multiple Attribute Decision Making: Methods and Applications, Berlin, Springer.
• Ishizaka, A., Nemery, P. (2013), Multi-Criteria Decision Analysis: Methods and Software, Chichester, Wiley.
• Jankowski, J., Watrobski, J., Piwowarski, M. (2011), Fuzzy Modeling of Digital Products Pricing in the Virtual Marketplace, Proceedings of 6th International Conference on Hybrid Artificial Intelligent Systems, Springer, Heidelberg, pp. 338-346.
• Jankowski, J., Ziemba, P., Wątróbski, J., Kazienko, P. (2016), Towards the Tradeoff Between Online Marketing Resources Exploitation and the User Experience with the Use of Eye Tracking. In: Intelligent Information and Database Systems, Berlin Heidelberg, Springer, pp. 330-343.
• Jantoń-Drozdowska, E., Majewska, M. (2016), Investment Attractiveness of Central and Eastern European Countries in the Light of New Locational Advantages Development, Equilibrium. Quarterly Journal of Economics and Economic Policy, Vol. 11, No. 1, pp. 97-119.
• Jantoń-Drozdowska, E., Majewska, M. (2015), Social Capital as a Key Driver of Productivity Growth of the Economy: Across-countries Comparison, Equilibrium. Quarterly Journal of Economics and Economic Policy, Vol. 10, No. 4, pp. 61-83.
• Jassbi, J. J., Ribeiro, R. A., Varela, L. R. (2014), Dynamic MCDM with future knowledge for supplier selection, Journal of Decision Systems, Vol. 23, No. 3, pp. 232-248.
• Kelly, L., Kerr, G., Drennan, J. (2010), Avoidance of advertising in social networking sites: The teenage perspective, Journal of Interactive Advertising, Vol. 10, No. 2, pp. 16-27.
• Keeney, R. L., Raiffa, H. (1976), Decisions with Multiple Objectives: Preferences and Value Tradeoffs, New York, Wiley.
• Kornbluth, J. S. H. (1992), Dynamic multi-criteria decision making, Journal of Multi-Criteria Decision Analysis 1.2, pp. 81-92.
• Lambrecht, A., Tucker, C. (2013), When does retargeting work? Information specificity in online advertising, Journal of Marketing Research, Vol. 50, No. 5, pp. 561-576.
• Lang, A. (2000), The limited capacity model of mediated message processing, Journal of Communication, Vol. 50, No. 1, pp. 46-70.
• Langheinrich, M., Nakamura, A., Abe, N., Kamba, T., Koseki, Y. (1999), Unintrusive customization techniques for web advertising, Computer Networks, Vol. 31, No. 11-16, pp. 1259-1272.
• Leyva-Lopez, J.C., Fernandez-Gonzales, E. (2003), A new method for group decision support based on ELECTRE III methodology, European Journal of Operational Research, Vol. 148, No. 1, pp. 14-27.
• Łyszczarz, B. (2016), Public-private Mix and Performance of Health Care Systems in CEE and CIS Countries, Oeconomia Copernicana, Vol. 7, No. 2.
• MacCrimmon, K. R. (1968), Decision Making Among Multiple-Attribute Alternatives: A Survey and Consolidated Approach, RAND Memorandum, RM-4823-ARPA.
• Mareschal, B., Brans, J. P. (1988), Geometrical representations for MCDA, European Journal of Operational Research, Vol. 34, No. 1, pp. 69-77.
• Mikhailov, L., Tsvetinov, P. (2004), Evaluation of services using a fuzzy analytic hierarchy process, Applied Soft Computing, Vol. 5, No 1, pp. 23-33.
• Morselli, A. (2015), The decision-making process between convention and cognition, Economics & Sociology, Vol. 8, No. 1, pp. 205-221.
• Motlagh, S. M. H., Behzadian, M., Ignatius, J., Goh, M., Sepehri, M. M., Hua, T. K. (2015), Fuzzy PROMETHEE GDSS for technical requirements ranking in HOQ, The International Journal of Advanced Manufacturing Technology, Vol. 76, No. 9, pp. 1993-2002.
• Munda, G. (1995), Multicriteria Evaluation in a Fuzzy Environment. Theory and Applications in Ecological Economics, Heidelberg, Physica-Verlag.
• Novak, T. P., Hoffman, D. L. (1996), New Metrics for New Media: Toward the Development of Web Measurement Standards, World Wide Web Journal, Vol. 2, No. 1, pp. 213-246.
• Olczyk, M. (2014), Structural Heterogeneity Between EU 15 and 12 New EU Members - the Obstacle to Lisbon Strategy Implementation? Equilibrium. Quarterly Journal of Economics and Economic Policy, Vol. 9, No. 3, pp. 21-43.
• Opricovic, S., Tzeng, G. H. (2004), Compromise Solution By Mcdm Methods: A Comparative Analysis Of Vikor And Topsis, European Journal Of Operational Research, Vol. 156, No. 2, pp. 445-455.
• Ozturk, M., Tsoukias, A., Vincke, P. (2005), Preference Modelling, In: Multiple Criteria Decision Analysis: State of the Art Surveys, J. Figueira, S. Greco, and M. Ehrgott (ed.), Boston, Springer, pp. 27-71.
• Pavlou, P. A., Stewart, D. W. (2000), Measuring the Effects and Effectiveness of Interactive Advertising: A Research Agenda, Journal of Interactive Advertising, Vol. 1, No. 1, pp. 62-78.
• Pietrzak, M. B., Balcerzak, A. P. (2016a), Assessment of Socio-Economic Sustainability in New European Union Members States in the years 2004-2012, In: M. Papież & S. Śmiech (eds.), The 10th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena. Conference Proceedings, Cracow: Foundation of the Cracow University of Economics, pp. 120-129.
• Pietrzak, M. B., Balcerzak, A. P. (2016b), Quality of Human Capital and Total Factor Productivity in New EU Member States, In: T. Loster & T. Pavelka (eds.), The 10th International Days of Statistics and Economics, Conference Proceedings. September 8-10, 2016, Prague.
• Promentilla, M. A. B., Furuichi, T., Ishii, K., Tanikawa, N. (2008), A fuzzy analytic network process for multi-criteria evaluation of contaminated site remedial countermeasures, Journal of Environmental Management, Vol. 88, No 3, pp. 479-495.
• Rosenkrans, G. (2009), The creativeness and effectiveness of online interactive rich media advertising, Journal of Interactive Advertising, Vol. 9, No. 2, pp. 18-31.
• Roy, B. (1968), Classement et choix en presence de points de vue multiples (la methode Electre), Revue Francaise d'Informatique et de Recherche Operationnelle, Vol. 8, pp. 57-75.
• Roy, B. (1978), Electre III: un algorithme de classement fonde sur une representation floue des preferences en presence de criteres multiples, Cahiers du CERO, Vol. 20, pp. 3-24.
• Roy, B. (1996), Multicriteria Methodology for Decision Aiding, Dordrecht, Springer.
• Roy, B. (1991), The Outranking Approach and the Foundations of Electre Methods, Theory and Decision, Vol. 31, No. 1, pp. 49-73.
• Roy, B. (2005), Paradigms and Challenges, In: Multiple Criteria Decision Analysis: State of the Art Surveys, J. Figueira, S. Greco, and M. Ehrgott (ed.), Boston, Springer, pp. 3-24.
• Roy, B., Bertier, P. (1973), La methode Electre II - une application au media planning, Amsterdam, North-Holland Publishing Company, pp. 291-302.
• Roy, B., Hugonnard, J. C. (1982), Ranking of suburban line extension projects on the Paris metro system by a multicriteria method, Transportation Research, Vol. 16A, pp. 301-312.
• Roy, B., Skalka, J. M. (1984), Electre IS - aspects methodologiques et guide d'utilisation, Document du LAMSADE 30.
• Saaty, T. L. (1994), Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, Pittsburgh, RWS Publications.
• Saaty, T. L., Vargas, L. G. (2006), Decision Making with the Analytic Network Process, New York, Springer.
• Shih, H. S., Shyur, H. J., Lee, E. S. (2007), An extension of TOPSIS for group decision making, Mathematical and Computer Modelling, Vol. 45, No. 7-8, pp. 801-813.
• Tang, L., Rosales, R., Singh, A., Agarwal, D. (2013), Automatic ad format selec-tion via contextual bandits, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, ACM, pp. 1587-1594.
• Tomlin, J. A. (2000), An entropy approach to unintrusive targeted advertising on the Web, Computer Networks, Vol. 33, No. 6, pp. 767-774.
• Turatto, M., Galfano, G. (2000), Color, form and luminance capture attention in visual search, Vision Research, Vol. 40, No. 13, pp. 1639-1643.
• Urban, G. L., Liberali, G., MacDonald, E., Bordley, R., Hauser, J. R. (2014), Morphing Banner Advertising, Marketing Science, Vol. 33, No. 1, pp. 27-46.
• Wang, T. C., Chen, L. Y., Chen, Y. H. (2008), Applying Fuzzy PROMETHEE Method for Evaluating IS Outsourcing Suppliers, Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Vol. 3, pp. 361-365.
• Wątróbski, J., Jankowski, J. (2015), Knowledge Management in MCDA Domain, Annals of Computer Science and Information Systems, Vol. 5, pp. 1445-1450.
• Wątróbski, J., Jankowski, J. (2016a), An Ontology-Based Knowledge Representation of MCDA Methods, Lecture Notes in Artificial Intelligence, Vol. 9621, pp. 54-64.
• Wątróbski, J., Jankowski, J. (2016b), Guideline for MCDA Method Selection in Production Management Area, New Frontiers in Information and Production Systems Modelling and Analysis, Intelligent Systems Reference Library 98, Berlin Heidelberg, Springer, pp. 119-138.
• Wu, G. (2007), Applying the Rossiter-Percy Grid to Online Advertising Planning: The Role of Product/Brand Type in Previsit Intentions, Journal of Interactive Advertising, Vol. 8, No. 1, pp. 14-22.
• Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y., Chen, Z. (2009), How much can behavioral targeting help online advertising? Proceedings of the 18th international conference on World wide web, ACM, pp. 261-270.
• Yoo, C. Y., Kim, K. (2005), Processing of Animation in Online Banner Advertising: The Roles of Cognitive and Emotional Responses, Journal of Interactive Marketing, Vol. 19, No. 4, pp. 18-34.
• Yoo, C. Y., Kim, K., Stout, P. A. (2004), Assessing the effects of animation in online banner advertising: Hierarchy of effects model, Journal of Interactive Advertising, Vol. 4, No. 2, pp. 49-60.
• Yu, P. L., Chen, Y. C. (2010), Dynamic MCDM, Habitual Domains and Competence Set Analysis for Effective Decision Making in Changeable Spaces. In: Trends in Multiple Criteria Decision Analysis. Springer US, 2010. pp. 1-35. Greco, Salvatore, Matthias Ehrgott, and José Rui Figueira, eds. Trends in multiple criteria decision analysis. Vol. 142, Springer Science & Business Media.
• Yuan, S., Wang, J., Zhao, X. (2013), Real-time bidding for online advertising: measurement and analysis, Proceedings of the Seventh International Workshop on Data Mining for Online Advertising, ACM, p. 3.
• Zha, W., Wu, H. D. (2014), The Impact of Online Disruptive Ads on Users' Comprehension, Evaluation of Site Credibility, and Sentiment of Intrusiveness, American Communication Journal, Vol. 16, No. 2, pp. 15-28.
• Zorn, S., Olaru, D., Veheim, T., Zhao, S., Murphy, J. (2012), Impact of animation and language on banner click-through rates, Journal of Electronic Commerce Research, Vol. 13, No. 2, pp. 173-183.

Identyfikatory

DOI

10.14254/2071-789X.2016/9-2/7