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Decision Support in the Field of Online Marketing - Development of a Data Landscape

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E-Business and Telecommunications (ICETE 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 554))

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Abstract

The relevance of decision support and the related potential has increased in the past years fostered by the rising number of data sources available inside and outside companies and total data points. The available data sources, especially company-external differ in their explanatory power and the effort needed to extract and process the data. To structure the available data and enhance the decision support process, we develop a construction model based on the principles of design science research for the development of a data landscape, which enables the definition of goal-oriented research questions and the identification of related available data in- and outside of the company. The framework is empirically tested in the field online advertising. The application reveals the landscapes contribution to the decision making which leads to economic valuable results.

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Notes

  1. 1.

    “search” refers to clicks on paid search advertisements, “social” to clicks on advertisements on Facebook, “display” to clicks on generic banner advertisements, “affiliate” to clicks on banner advertisements of the affiliate networks, “newsletter” to clicks on emails sent to consumers, and “other” to further advertisement interactions that do not belong to one of the previous groups.

  2. 2.

    For a more detailed description of preparing the clickstream data for the analysis, please see [30].

  3. 3.

    In RTB, display advertising impressions are bought in an auction-based process and displayed in real time on the individual consumer level. In other words, the knowledge of a consumer’s success probability (such as a click or a conversion) at any given time is vital for accurately evaluating each advertising type and appropriately adjusting financial resources.

  4. 4.

    We do so following Nottorf and Funk (2013) and [6] with respect to [27], who suggested ranking observations in decreasing order of predicted probabilities and classifying the first x as clicks (where x is the total number of clicks observed in the holdout sample) because the behavior to be predicted is relatively rare and the base probability of the outcome is very low. As Chatterjee et al. also emphasize, with a large number of nonevents (no conversions) and very few events (conversions), logistic regression models can sharply underestimate the probability of the occurrence of events.

  5. 5.

    In a real setting, these expected CPOs should be calculated repeatedly because the parameter estimates may change over time and it is necessary to analyze the probabilities of new consumers.

  6. 6.

    Note that there are additional costs (i.e., costs for data storage or for analyzing consumer-level data) that should also have been considered in the calculation above. For demonstration purposes, these costs are negligible. For example, the size of the initial dataset of 500,000 consumers is approximately 150 MB, and the data storage prices for 1 GB of data are less than €0.10 at Amazon web services. While estimating the model is computationally expensive, determining the conversion probabilities is not. Therefore, we can neglect the costs for the computation of the expected conversion probability for an individual advertising exposure.

  7. 7.

    \(loss_{exp} = 470.906*0.25*0.50-962*75\).

References

  1. Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)

    Article  Google Scholar 

  2. Braun, M., Moe, W.W.: Online display advertising: modeling the effects of multiple creatives and individual impression histories. Market. Sci. 32(5), 753–767 (2013)

    Article  Google Scholar 

  3. Bucher, T., Riege, C., Saat, J.: Evaluation in der gestaltungsorientierten Wirtschaftsinformatik - Systematisierung nach Erkenntnisziel und Gestaltungsziel. Technical report, Westflische Wilhelms-Universitt Mnster (2008)

    Google Scholar 

  4. Bucklin, R.E., Sismeiro, C.: A model of web site browsing behavior estimated on clickstream data. J. Market. Res. 40(3), 249–267 (2003)

    Article  Google Scholar 

  5. Byrd, T., Cossick, K., Zmud, R.: A synthesis of research on requirements analysis and knowledge acquisition techniques. MIS Q. 16(1), 117–138 (1992)

    Article  Google Scholar 

  6. Chatterjee, P., Hoffman, D.L., Novak, T.P.: Modeling the clickstream: implications for web-based advertising efforts. Market. Sci. 22(4), 520–541 (2003)

    Article  Google Scholar 

  7. Chaudhuri, S., Dayal, U., Ganti, V.: Database technology for decision support systems. Computer 34(12), 48–55 (2001)

    Article  Google Scholar 

  8. Cho, C.-H.: Factors influencing clicking of banner ads on the WWW. CyberPsychology Behav. 6(2), 201–215 (2003)

    Article  Google Scholar 

  9. Clarke, A.: Evaluation Research: An Introduction to Principles, Methods and Practice. Sage Publications, London (1999)

    Google Scholar 

  10. Cleven, A., Gubler, P., Hüner, K.M.: Design alternatives for the evaluation of design science research artifacts. In: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology, pp. 19:1–19:8 (2009)

    Google Scholar 

  11. Danaher, P.J., Mullarkey, G.: Factors affecting online advertising recall: a study of students. J. Advertising Res. 43(3), 252–267 (2003)

    Article  Google Scholar 

  12. Davis, G.B.: Strategies for information requirements determination. IBM Syst. J. 21(1), 4–30 (1982)

    Article  Google Scholar 

  13. Forcada, M.L., Ginestí-Rosell, M., Nordfalk, J., ORegan, J., Ortiz-Rojas, S., Pérez-Ortiz, J.A., Sánchez-Martínez, F., Ramírez-Sánchez, G., Tyers, F.M.: Apertium: a free/open-source platform for rule-based machine translation. Mach. Trans. 25(2), 127–144 (2011)

    Article  Google Scholar 

  14. Gardner, S.R.: Building the data warehouse. Commun. ACM 41(9), 52–60 (1998)

    Article  Google Scholar 

  15. Giorgini, P., Rizzi, S., Garzetti, M.: Goal-oriented requirement analysis for data warehouse design. In: Proceedings of the 8th ACM International Workshop on Data Warehousing and OLAP - DOLAP, p. 47 (2005)

    Google Scholar 

  16. Hansmann, T., Niemeyer, P.: Big data - characterizing an emerging research field using topic models. In: Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), pp. 43–51 (2014)

    Google Scholar 

  17. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information system research. MIS Q. 28(1), 75–105 (2004)

    Google Scholar 

  18. Hilbert, M., López, P.: The world’s technological capacity to store, communicate, and compute information. Science (New York, NY) 332(60), 60–65 (2011)

    Article  Google Scholar 

  19. Inmon, W.H.: Building the Data Warehouse, 4th edn. Wiley, Indianapolis (2005)

    Google Scholar 

  20. Kotonya, G., Sommerville, I.: Requirements Engineering: Processes and Techniques. Wiley, New York (1998)

    Google Scholar 

  21. LaValle, S., Lesser, E., Shockley, R.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21–31 (2011)

    Google Scholar 

  22. List, B., Schiefer, J., Tjoa, A.M.: Process-oriented requirement analysis supporting the data warehouse design process - a use case driven approach. In: Ibrahim, M., Küng, J., Revell, N. (eds.) DEXA 2000. LNCS, vol. 1873, pp. 593–603. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  23. Madnick, S.E., Wang, R.Y., Lee, Y.W., Zhu, H.: Overview and framework for data and information quality research. ACM J. Data Inf. Qual. 1(1), 1–22 (2009)

    Google Scholar 

  24. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: the next frontier for innovation, competition, and productivity. Technical report June, McKinsey Global Institute (2011)

    Google Scholar 

  25. Mazón, J., Pardillo, J., Trujillo, J.: A model-driven goal-oriented requirement engineering approach for data warehouses. Advances in Conceptual Modeling Foundations and Applications, pp. 255–264. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  26. Moody, D.L., Kortink, M.A.R.: From enterprise models to dimensional models: a methodology for data warehouse and data mart design objectives of dimensional modelling. In: 2nd DMWD, vol. 2000 (2000)

    Google Scholar 

  27. Morrison, D.G.: On the interpretation of discriminant analysis. J. Market. Res. 6(2), 156–163 (1969)

    Article  Google Scholar 

  28. Mudambi, S., Schuff, D.: What makes a helpful online review? a study of customer reviews on Amazon.com. MIS Q. 34(1), 185–200 (2010)

    Google Scholar 

  29. Nottorf, F.: Modeling the clickstream across multiple online advertising channels using a binary logit with bayesian mixture of normals. Electron. Commer. Res. Appl. (Art. Adv.) 13(1), 45–55 (2013)

    Article  Google Scholar 

  30. Nottorf, F., Funk, B.: The economic value of clickstream data from an advertiser’s perspective (2013)

    Google Scholar 

  31. Rossi, P.E., Allenby, G.M., McCulloch, R.E.: Bayesian statistics and marketing. Wiley, Hoboken (2005)

    Book  MATH  Google Scholar 

  32. Rutz, O.J., Bucklin, R.E.: From generic to branded: a model of spillover in paid search advertising. J. Market. Res. 48(1), 87–102 (2011)

    Article  Google Scholar 

  33. Rutz, O.J., Bucklin, R.E.: Does banner advertising affect browsing for brands? clickstream choice model says yes, for some. Quant. Market. Econ. 10(2), 231–257 (2011)

    Article  Google Scholar 

  34. Stonebraker, M., Robertson, J.: Big data is ‘buzzword du jour;’ CS academics ‘have the best job’. Commun. ACM 56(9), 10 (2013)

    Article  Google Scholar 

  35. Stroh, F., Winter, R., Wortmann, F.: Method support of information requirements analysis for analytical information systems. Bus. Inf. Syst. Eng. 3(1), 33–43 (2011)

    Article  Google Scholar 

  36. Winter, R., Strauch, B.: A method for demand-driven information requirements analysis in data warehousing projects. In: Proceedings of the 36th Annual Hawaii International Conference on System Sciences, Number Sect. 2 (2003)

    Google Scholar 

  37. Winter, R., Strauch, B.: Information requirements engineering for data warehouse systems. In: Proceedings of the 2004 ACM Symposium on Applied computing - SAC 2004. ACM Press, New York (2004)

    Google Scholar 

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Authors and Affiliations

  1. Leuphana University Lüneburg, Scharnhorststr. 1, 21335, Lüneburg, Germany

    Thomas Hansmann

  2. Adference GmbH, Am Urnenfeld 5, 21335, Lüneburg, Germany

    Florian Nottorf

Authors
  1. Thomas Hansmann
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  2. Florian Nottorf
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Correspondence to Thomas Hansmann .

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Editors and Affiliations

  1. Rose Hill and Lincoln Center Campuses, Fordham University, Bronx, New York, USA

    Mohammad S. Obaidat

  2. Medical Informatis, Statistics, Document, Medical University Graz, Graz, Austria

    Andreas Holzinger

  3. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

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Hansmann, T., Nottorf, F. (2015). Decision Support in the Field of Online Marketing - Development of a Data Landscape. In: Obaidat, M., Holzinger, A., Filipe, J. (eds) E-Business and Telecommunications. ICETE 2014. Communications in Computer and Information Science, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-319-25915-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-25915-4_5

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