Abstract

E-Advertisements have made possible to allow marketers for approaching target segments in the most measurable, interactive and more essentially, cost-effective ways. However, Neural Network is a forecasting tool for dynamic and changing market environments. A Strong advantage of neural networks is that a properly trained network can be considered experts with regard to the particular output project for which it was designed to examine. This paper gives brief view about various e-advertisement Payment trends. Various sector wise e-advertisement related data from 2008 to 2013 have been collected from IAB (Internet Advertisement Bureau) and applied the Back Propagation technique of Neural Network for predicting ratio of cost models in E-advertisements. Effective use of data mining will ear mark of E-advertisement in various industries like consumer service, retail, auto, travel, computing, media, financial service, telecommunication etc.

References

• Pradeep Chaudhari, Ravindra Dingankar, and Roshan Chaudhari, "Prediction of CPC Using Neural Networks for Minimization of Cost", International Journal of Computer Theory and Engineering, Vol. 5, No. 4, August 2013
• Nikolay Archak, Vahab S. Mirrokni, S. Muthukrishnan, " Mining Advertiser-specific User Behavior Using Adfactors", WWW 2010, April 26–30, 2010, Raleigh, North Carolina, USA, ACM 978-1-60558-799-8/10/04.
• Haykin, S. (2009). Neural Networks and Learning Machines. 3rd edition, Pearson Education, Inc. ,New Jersey
• Beale, M. , Hagan, M. & Demut, H. (2010). Neural Network Toolbox User's Guide, 14. 03. 2011, Available from http://www. mathworks. com/ help/pdf_doc/nnet/nnet. pdf
• Simon Haykin, 2005, "Neural Networks", Second edition by, Prentice Hall of India.
• Hill, T. and Ramus, W. , 'Neural network models for intelligent support of managerial decision making', Decision Support Systems, 11, 1994.
• Poh, H. L. , A Neural Network Approach for Marketing Strategies Research and Decision Support, PhD thesis, Stanford University, March 1991.
• Werbos, P. J. , Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, PhD Thesis, Harvard University, November, 1974.
• PricewaterhouseCoopers, "IAB Internet Advertising Revenue Report" 2008 Full-Year Results, March 2009
• PricewaterhouseCoopers, "IAB Internet Advertising Revenue Report" 2009 Full-Year Results, April 2010
• PricewaterhouseCoopers, "IAB Internet Advertising Revenue Report" 2010 Full-Year Results, March 2011
• PricewaterhouseCoopers, "IAB Internet Advertising Revenue Report" 2011 Full-Year Results, May 2012
• PricewaterhouseCoopers, "IAB Internet Advertising Revenue Report" 2012 Full-Year Results, March 2013
• PricewaterhouseCoopers, "IAB Internet Advertising Revenue Report" 2013 Full-Year Results, April 2014
• Hill, T. and Ramus, W. , 'Neural network models for intelligent support of managerial decision making', Decision Support Systems, 11, 1994.
• Poh, H. L. , A Neural Network Approach for Marketing Strategies Research and Decision Support, PhD thesis, Stanford University, March 1991.
• Lippmann, R. P. , 'An introduction to computing with neural nets', IEEE ASSP Magazine, April, 1987, 4–22.
• Weigend, A. S. , Rumelhart, D. E. and Huberman, B. A. , 'Predicting the future: a connectionist approach', International Journal of Neural Systems, 1, 3, 1990,193–209.
• Jas?ic, T. , A Neural Network Approach to Retail Marketing Analysis and its Implications for Decision Support, Masters thesis, National University of Singapore, 1993.
• S. N. Sivanandam, Sumathi & Deepa, 2006, "Introduction to neural networks using MATLAB 6. 0",Tata McGraw-Hill Education.
• Hornik, K. , Stinchcombe, M. and White, H. , 'Multilayer feedforward networks are universal approximators', Neural Networks, 2, 1989, 359–366.
• White, H. , 'Connectionist nonparametric regression: multilayer feedforward neural networks can learn arbitrary mappings', Neural Networks, 3, 1990.
• Ulf Johansson, Lars Niklasson, "Predicting the Impact of Advertising: a Neural Network Approach", Department of Computer Science, Skövde, Sweden
• Beale, M. , Hagan, M. & Demut, H. (2010). Neural Network Toolbox User's Guide, 14. 03. 2011, Available from http://www. mathworks. com/ help/pdf_doc/nnet/nnet. pdf