Big or Smart Data? Recent trends in Data Science for sales and marketing: An empirical analysis of the consumer-packaged goods industry

Ertel, Julia

ISBN 10: 3346640647 / ISBN 13: 9783346640642

Published by Grin Verlag, 2022

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Seller Inventory # I-9783346640642

Bibliographic Details

• Title: Big or Smart Data? Recent trends in Data Science for sales and marketing: An empirical analysis of the consumer-packaged goods industry
• Publisher: Grin Verlag
• Publication Date: 2022
• Language: English
• ISBN 10: 3346640647
• ISBN 13: 9783346640642
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Bachelor Thesis from the year 2021 in the subject Business economics - Miscellaneous, grade: 1,0, Pforzheim University, language: English, abstract: Big data is getting larger, the pressure in the market to use the existing data is getting stronger and therefore also the number of companies that address the topic of data science increases. This dissertation focuses on identifying big or smart data science trends in marketing and sales within the consumer-packaged goods industry. The objective of this research is to address actual opportunities around data science for the selected focus area. The following research project analyzes those opportunities and identifies nine data science trends. Via in-depth interviews, the expert's experiences and difficulties with data science are questioned, emotions that arise through the interaction with this science are recognized, and potentials for improvements are discussed. Subsequently, central meaningful quotations are analyzed with Mayring's qualitative content analysis, reformulated into condensed codes, and summarized through eighteen overarching categories. The general findings of this analysis include the necessity of smart data insights within this low margin industry, the dependence on consultancy support due to knowledge gaps, expandable engagement in the B2B environment, the promotion of data-thinking and acting, the merge of sales and marketing for data science knowledge generations, and the extension of data science knowledge to maintain competitive advantage within the market for the long run. The improvement proposals consist mainly of automated data cleaning, intelligent algorithms, data handling knowledge development, data democracy, and knowledge combinations in form of project dependent focus teams to broaden data science applications within the industry.