Decision Support and Online Advertising - Development and Empirical Testing of a Data Landscape

Thomas Hansmann, Florian Nottorf

Abstract

The number of data sources available inside and outside companies and total data points increase, which makes the coordinated data selection in the forefront of decision making with respect to a specific economic goal becomes more and more relevant. To assess the available data and enhance decision support, we develop a framework including a process model that supports the identification of goal-oriented research questions and a data landscape that provides a structured overview of the available data inside and outside the company. We empirically tested the framework in the field of online advertising to enhance decision support in managing display advertising campaigns. The test reveals that the developed data landscape supports the identification and selection of decision-relevant data and that the subsequent analysis leads to economic valuable results.

References

  1. Author (2013). Big Data - Characterizing an Emerging Research Field using Topic Models (under review). International Journal of Technology and Management.
  2. Braun, M. and Moe, W. W. (2013). Online display advertising: Modeling the effects of multiple creatives and individual impression histories. Marketing Science, 32(5):753-767.
  3. Bucklin, R. E. and Sismeiro, C. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40(3):249-267.
  4. Byrd, T., Cossick, K., and Zmud, R. (1992). A synthesis of research on requirements analysis and knowledge acquisition techniques. Mis Quarterly, 16(1):117-138.
  5. Chatterjee, P., Hoffman, D. L., and Novak, T. P. (2003). Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science, 22(4):520- 541.
  6. Chaudhuri, S., Dayal, U., and Ganti, V. (2001). Database technology for decision support systems. Computer, 34(12):48-55.
  7. Cho, C.-H. (2003). Factors influencing clicking of banner ads on the www. CyberPsychology & Behavior, 6(2):201-215.
  8. Danaher, P. J. and Mullarkey, G. (2003). Factors affecting online advertising recall: A study of students. Journal of Advertising Research, 43(3):252-267.
  9. Davis, G. B. (1982). Strategies for information requirements determination. IBM Systems Journal, 21(1):4- 30.
  10. Forcada, M. L., Ginestí-Rosell, M., Nordfalk, J., O'Regan, J., Ortiz-Rojas, S., Pérez-Ortiz, J. A., SánchezMartínez, F., Ramírez-Sánchez, G., and Tyers, F. M. (2011). Apertium: a free/open-source platform for rule-based machine translation. Machine Translation, 25(2):127-144.
  11. Gardner, S. R. (1998). Building the data warehouse. Communications of the ACM, 41(9):52-60.
  12. Giorgini, P., Rizzi, S., and Garzetti, M. (2005). Goaloriented requirement analysis for data warehouse design. Proceedings of the 8th ACM international workshop on Data warehousing and OLAP - DOLAP, page 47.
  13. Golfarelli, M., Maio, D., and Rizzi, S. (1998). The dimensional fact model: A conceptual Model for Data Warehouses. International Journal of Cooperative Information Systems, 7(2-3):215-247.
  14. Hevner, A. R., March, S. T., Park, J., and Ram, S. (2004). Design science in information system research. MIS Quarterly, 28(1):75-105.
  15. Hilbert, M. and L ópez, P. (2011). The world's technological capacity to store, communicate, and compute information. Science (New York, N.Y.), 332(60):60-65.
  16. Inmon, W. H. (2005). Building the Data Warehouse. John Wiley & Sons, Indianapolis, 4th edition.
  17. Kotonya, G. and Sommerville, I. (1998). Requirements Engineering: Processes and Techniques. John Wiley & Sons.
  18. LaValle, S., Lesser, E., and Shockley, R. (2011). Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review, 52(2):21-31.
  19. List, B., Schiefer, J., and Tjoa, A. (2000). Process-oriented requirement analysis supporting the data warehouse design process a use case driven approach. In DEXA 7800 Proceedings of the 11th International Conference on Database and Expert Systems Applications, pages 593-603.
  20. Madnick, S. E., Wang, R. Y., Lee, Y. W., and Zhu, H. (2009). Overview and Framework for Data and Information Quality Research. ACM Journal of Data and Information Quality, 1(1):1-22.
  21. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011). Big data : The next frontier for innovation , competition , and productivity. Technical Report June, McKinsey Global Institute.
  22. Mazón, J., Pardillo, J., and Trujillo, J. (2007). A model-driven goal-oriented requirement engineering approach for data warehouses. In Advances in Conceptual Modeling - Foundations and Applications, pages 255-264. Springer, Berlin Heidelberg.
  23. Moody, D. L. and Kortink, M. A. R. (2000). From Enterprise Models to Dimensional Models : A Methodology for Data Warehouse and Data Mart Design Objectives of Dimensional Modelling. In 2nd DMWD, volume 2000.
  24. Morrison, D. G. (1969). On the interpretation of discriminant analysis. Journal of Marketing Research, 6(2):156-163.
  25. Mudambi, S. and Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 34(1):185-200.
  26. Nottorf, F. (2013). Modeling the clickstream across multiple online advertising channels using a binary logit with bayesian mixture of normals. Electronic Commerce Research and Applications, (Article in Advance).
  27. Nottorf, F. and Funk, B. (2013). The economic value of clickstream data from an advertiser's perspective.
  28. Rossi, P. E., Allenby, G. M., and McCulloch, R. E. (2005). Bayesian statistics and marketing. Wiley, Hoboken, NJ.
  29. Rutz, O. J. and Bucklin, R. E. (2011a). Does banner advertising affect browsing for brands? clickstream choice model says yes, for some. Quantitative Marketing and Economics, pages 1-27.
  30. Rutz, O. J. and Bucklin, R. E. (2011b). From generic to branded: A model of spillover in paid search advertising. Journal of Marketing Research, 48(1):87-102.
  31. Stonebraker, M. and Robertson, J. (2013). Big data is 'buzzword du jour;78 CS academics 'have the best job'. Communications of the ACM, 56(9):10.
  32. Stroh, F., Winter, R., and Wortmann, F. (2011). Method Support of Information Requirements Analysis for Analytical Information Systems. Business & Information Systems Engineering, 3(1):33-43.
  33. Winter, R. and Strauch, B. (2003). 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 section 2.
  34. Winter, R. and Strauch, B. (2004). Information requirements engineering for data warehouse systems. In Proceedings of the 2004 ACM symposium on Applied computing - SAC 7804, New York, New York, USA. ACM Press.
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Paper Citation


in Harvard Style

Hansmann T. and Nottorf F. (2014). Decision Support and Online Advertising - Development and Empirical Testing of a Data Landscape . In Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014) ISBN 978-989-758-043-7, pages 111-122. DOI: 10.5220/0005060401110122


in Bibtex Style

@conference{ice-b14,
author={Thomas Hansmann and Florian Nottorf},
title={Decision Support and Online Advertising - Development and Empirical Testing of a Data Landscape},
booktitle={Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014)},
year={2014},
pages={111-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005060401110122},
isbn={978-989-758-043-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014)
TI - Decision Support and Online Advertising - Development and Empirical Testing of a Data Landscape
SN - 978-989-758-043-7
AU - Hansmann T.
AU - Nottorf F.
PY - 2014
SP - 111
EP - 122
DO - 10.5220/0005060401110122