Cost Behavior and Price Policy: A Study Prepared by The Committee on Price Determination for the Conference on Price Research.

Abstract The study aims at highlighting the link between educational marketing (product/service and price determination for educational services) and investment in education, using empirical models and customization of classic approaches (interpolation method) addressed to individual educational investment. The methodology discussed in the paper, considers essential invariants of these educational investments, such as seniority - part of the work experience and period of studies. In the models presented, the level and the period of studies are quantified through transferable credits, expressing units of time, normal volume of working alleged student learning. It is also used a parameter which introduces an essential element of the quality of work - the psycho-physical characteristics of the fellow that are correlated with age. Empirical study materializes on developing, while testing and validation of the models show that the rate of return to investment in education is a rationale for individuals to decide investing in their education. The study offers some customized recommendation to improve reverse marketing price policy of the educational services. The study results lead us to the conclusion that education providers (colleges, universities, other training entities) and clients should take into account that education is an investment. The private return of investment in education - as argument of educational marketing (price policy) is increasingly important in the context of a fragmentary and dynamic market, led by strong competition.

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Book Review: Cost Behavior and Price Policy

Journal of Marketing ◽

10.1177/002224294400900113 ◽

1944 ◽

Vol 9 (1) ◽

pp. 76-77

Author(s):

Kenneth D. Hutchinson

Keyword(s):

Price Policy ◽

Cost Behavior

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Cost Behaviour and Price Policy. A Study Prepared by the Committee on Price Determination for the Conference on Price Research.

Journal Of The Royal Statistical Society ◽

10.2307/2981358 ◽

1944 ◽

Vol 107 (1) ◽

pp. 65

Author(s):

F. E. R.

Keyword(s):

Price Policy ◽

Price Determination ◽

Cost Behaviour

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Cost Behavior and Price Policy.

Journal of the American Statistical Association ◽

10.2307/2279969 ◽

1944 ◽

Vol 39 (226) ◽

pp. 264

Author(s):

C. B. Nickerson

Keyword(s):

Price Policy ◽

Cost Behavior

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Fair Price Policy for Long Term Business

Global Journal For Research Analysis ◽

10.15373/22778160/july2014/66 ◽

2012 ◽

Vol 3 (7) ◽

pp. 188-189

Author(s):

Vidhusekhar P Vidhusekhar P ◽

Keyword(s):

Price Policy ◽

Fair Price

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Welfare and Poverty Impacts of Cocoa Price Policy Reform in Cote d'Ivoire

10.1596/29625 ◽

2017 ◽

Author(s):

Roy Katayama ◽

Andrew Dabalen ◽

Essama Nssah ◽

Guy Morel Amouzou Agbe

Keyword(s):

Cote D'ivoire ◽

Côte D’Ivoire ◽

Policy Reform ◽

Price Policy ◽

Cote D’Ivoire ◽

Côte D'ivoire ◽

Welfare And Poverty

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Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

Studia Universitatis Babe-Bolyai Oeconomica ◽

10.2478/subboec-2019-0009 ◽

2019 ◽

Vol 64 (2) ◽

pp. 53-71

Author(s):

Botond Benedek ◽

Ede László

Keyword(s):

Neural Network ◽

Decision Tree ◽

Naive Bayes ◽

Insurance Industry ◽

Naïve Bayes ◽

Sql Server ◽

Categorical Variables ◽

Automobile Insurance ◽

Price Determination ◽

Mining Tool

Abstract Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.

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