Sales management analysis with text mining methods

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dc.contributor.advisor Tümen, Semih
dc.contributor.author Soyer, Tuğba
dc.date.accessioned 2021-01-25T08:25:03Z
dc.date.available 2021-01-25T08:25:03Z
dc.date.issued 2020-02
dc.identifier.uri http://hdl.handle.net/20.500.12485/742
dc.description.abstract The aim of this study is to examine offer contents of a major vehicle company using data mining techniques to categorize these offers based on difficulty level for the purpose of improving sales management process. The language of the offers is mainly in German. Categorizing the offers according to the difficulty level will allow the company to observe the changes in the customer requests over time and to make long term strategic sales management and marketing plans. A new method is developed inspired by the Support Vector Machine and Lexicon sensitivity analysis, and the offers are categorized with respect to their difficulty levels. At this stage, technical dictionaries are prepared with the support of the company’s expert team. The study also constructs a large consolidated data set, which could serve as a basis for future sales management analysis of the offers. The data set used in this study contains 1,082,093 observations used in the vehicle sales process between 2006-2019. As a result of this study, unstructured and distributed offers data are converted into a data set ready for analysis. It is seen that the content of the special requests affects the difficulty level more than the number of requests included in the offer. It is understood how market demands have changed over time for the countries. en_US
dc.language.iso en en_US
dc.publisher Applied Data Science en_US
dc.subject Sales management en_US
dc.subject Text mining en_US
dc.subject Text categorization en_US
dc.subject Lexicon sentiment analysis en_US
dc.subject Machine learning en_US
dc.subject Automotive en_US
dc.subject German en_US
dc.title Sales management analysis with text mining methods en_US
dc.type Thesis en_US

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