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dc.contributor.author
Lithoxoidou, Evdoxia-Eirini
en
dc.date.accessioned
2015-06-25T08:39:22Z
dc.date.available
2015-09-27T06:04:22Z
dc.date.issued
2015-06-25
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/519
dc.rights
Default License
dc.title
Sales Data Analysis for Price Elasticity Modeling and Pricing Optimization
en
heal.type
masterThesis
heal.keyword
Management information systems
en
heal.keyword
Business planning
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heal.keyword
Managerial accounting
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heal.keyword
Elasticity (Economics)
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heal.keyword
Data mining
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heal.keyword
Business--Data processing
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heal.keyword
Dissertations, Academic
en
heal.language
en
heal.access
free
el
heal.license
http://creativecommons.org/licenses/by-nc/4.0
heal.recordProvider
School of Science and Technology, MSc in Information & Communication Technology Systems
heal.publicationDate
2012-10
heal.bibliographicCitation
Lithoxoidou Evdoxia-Eirini, 2012, Sales data analysis for price elasticity modeling and pricing optimization ,Master's Dissertation, International Hellenic University
en
heal.abstract
Price elasticity is one major factor of businesses which want to be competitive in mar-ketplace and make the most of the products‘ profit. The term price elasticity means the range of price values which can refer to a product depending on financial and social sit-uations. Willingness to pay defines the elasticity of goods and it varies in many levels which are concerning most of the companies worldwide. In order for a company to suc-ceed, there are many pricing strategies used to find the best corresponding prices, suita-ble for a new-entry product to a product which is stock. These strategies are reclaimed via models which work with large data series and are able to provide a competitive ad-vantage and long term stability. Elasticity has three types: Elastic, Unitary and Inelastic. The method of least squares is very commonly used for this purpose and there are many techniques to apply for better results. Business intelligence is the section which deals with handling amounts of information and transforms it to knowledge. It supports deci-sion making due to the statistics it is based on that can predict and forecast. Time to time, even more companies turn to business intelligence because data mining techniques and analysis can benefit from the Enterprise Resource Planning implementations and achieve current trends on behalf of profit. A certain challenge for the 21st century busi-ness analysts are an absolute important competency for an IT company for being able to reduce complexity and optimize business process.
en
heal.tableOfContents
ACKNOWLEDGEMENTS: ........................................................................................... 3 ABSTRACT .................................................................................................................... 4 CONTENTS .................................................................................................................... 5 1 INTRODUCTION ...................................................................................................... 7 1.1 STRUCTURE OF THE THESIS .............................................................................. 8 2 PRICING STRATEGIES ....................................................................................... 11 2.1 BACKGROUND .................................................................................................. 12 2.2 WILLINGNESS TO PAY (WTP).......................................................................... 13 2.3 THE CURRENT STATE OF PRICING STRATEGIES ............................................... 16 2.3.1 Penetration Pricing ............................................................................ 16 2.3.2 Influence of elasticity ........................................................................ 17 2.3.3 Cost-Plus Pricing ............................................................................... 20 2.3.4 Contribution Margin-based Pricing ................................................. 21 2.3.5 Target Pricing..................................................................................... 23 2.3.6 Marginal Cost Pricing ....................................................................... 26 2.3.7 Absorption/Full cost Pricing ............................................................. 28 2.3.8 Market Skimming............................................................................... 30 2.3.9 Value Pricing ...................................................................................... 32 2.3.10 Loss Leader ....................................................................................... 36 2.3.11 Psychological Pricing ........................................................................ 37 2.3.12 Going Rate (Price Leadership) ....................................................... 39 2.3.13 Tender Pricing ................................................................................... 40 2.3.14 Price Discrimination .......................................................................... 41 2.3.15 Destroyer Pricing/Predatory Pricing ............................................... 44 3 OPTIMIZATION ALGORITHM ............................................................................ 47 PRICE ELASTICITY MODELING ............................................................................. 47 -6- 3.1 REGRESSION ANALYSIS .................................................................................. 49 3.2 LEAST-SQUARES REGRESSION ...................................................................... 50 3.2.1 Gaussian Model ................................................................................ 53 3.2.2 Exponential Model ............................................................................ 54 3.2.3 Power Model ...................................................................................... 55 3.2.4 Fourier Model .................................................................................... 56 3.2.5 Linear-Polynomial Model ................................................................. 57 4 IMPLEMENTATION PROCESS ......................................................................... 61 4.1 PRE-PROCESSING DATA ................................................................................. 61 4.2 DATA ANALYSIS OF MEASUREMENTS AND RESULTS ...................................... 64 4.3 EVALUATION OF MODELS ................................................................................ 80 5 ELASTICITY THROUGH TIME ........................................................................... 85 6 CONCLUSION AND FUTURE WORK .............................................................. 92 REFERENCES ............................................................................................................. 95 APPENDIX ................................................................................................................... 99
en
heal.advisorName
Tsoumakas, Dr. Grigorios
en
heal.committeeMemberName
Tsoumakas, G.
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heal.committeeMemberName
Ass. Prof. Bassileiades, N.
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heal.committeeMemberName
Berberidis, Dr C.
en
heal.academicPublisher
School of Science &Technology, Master of Science (MSc) in Information and Communication Systems
en
heal.academicPublisherID
ihu
heal.numberOfPages
130
heal.fullTextAvailability
true


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