| Project/Area Number |
15530293
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Commerce
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| Research Institution | Setsunan University |
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Principal Investigator |
TANAKA Katsuaki Setsunan university, Faculty of Business Administration and Information, professor, 経営情報学部, 教授 (20155120)
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| Co-Investigator(Kenkyū-buntansha) |
TAKEDA Eiji Osaka University, Graduate School of Economics, Professor, 大学院・情報学研究科, 教授 (80106624)
瀬戸口 香 社会法人日本広告審査機構, 次長
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2005: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2004: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2003: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | advertising planning / Data Envelopment Analysis / Neural Network / media selection planning / ニューラルネットワーク / 媒体選択計画 / 最適化 |
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| Research Abstract |
We surveyed the works concerning about media selection models from the point of data envelopment analysis and neural networks. we checked some academic journals like Management Science, Marketing Science, Journal of Marketing research, Journal of Marketing etc. At the same time we also surveyed data envelopment analysis and neural networks themselves so as to follow their development.
Many advertising Campaign data were collected from several sources and they are restructured in order to be analysed for our research. After some screening processes, 61 advertising campaign data were available for our analysis.
We developed the optimal media selection model using data envelopment analysis model based on Stochastic frontier. The merit of model that we developed does not need the estimation of functional form. Using the artificial data we generate by monte-carlo simulation, we found our model can obtain better result than parametric method based on the regression analysis in the problem with implicitly not only Cobb-Douglas and concave frontier function but also non-Cobb-Douglas and concave frontier function.
By applying our model to advertising campaign data, we got the optimal solution. After the presentation at the conference of The Japan Institute of Marketing Science, some member of advertising agency set a high valuation on our approach.
As for neural network, we searched many package soft for neural network. we were able to get some, the Enterprise Miner of SAS Institute, Clementine of SPSS, S-PLUS and the Predict of Neuralware.
Using sample data we investigate the prediction ability of each package soft. Among them, it become clear that the Predict of Neuralware outperform the other three softs from the point of the simplicity to use and the accurate output from it.
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