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The research on Optimal media selection models Using Data Envelopment Analysis and neural networks

基于数据包络分析和神经网络的最优媒体选择模型研究

基本信息

批准号：
15530293
负责人：
TANAKA Katsuaki
金额：
$ 2.18万
依托单位：
Setsunan University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

项目摘要

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.

我们从数据包络分析和神经网络的角度对有关媒体选择模型的工作进行了调查。我们查阅了一些学术期刊，如 Management Science、Marketing Science、Journal of Marketing Research、Journal of Marketing 等。同时我们还调查了数据包络分析和神经网络本身，以跟踪它们的发展。许多广告活动数据是从多个来源收集的，并进行了重组，以便为我们的研究进行分析。经过一些筛选过程，我们得到了 61 个广告活动数据可供我们分析。我们使用基于随机前沿的数据包络分析模型开发了最优媒体选择模型。我们开发的模型的优点是不需要函数形式的估计。使用我们通过蒙特卡罗模拟生成的人工数据，我们发现我们的模型在隐含柯布-道格拉斯和凹前沿函数以及非柯布-道格拉斯和凹前沿函数的问题上比基于回归分析的参数方法可以获得更好的结果。通过将我们的模型应用于广告活动数据，我们得到了最优解。在日本营销科学研究所的会议上发表后，一些广告公司的成员对我们的方法给予了很高的评价。至于神经网络，我们搜索了很多神经网络的软件包。我们得到了一些，SAS研究所的Enterprise Miner，SPSS的Clementine，S-PLUS和Neuralware的Predict。使用样本数据我们考察了每个包软件的预测能力。其中，从使用的简单性和输出的准确度来看，Neuralware的Predict明显优于其他三个软件。