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.
Inhaltsverzeichnis (Table of Contents)
- 1 Introduction
- 2 Fundamentals of big and smart data
- 2.1 Characteristics of big data
- 2.2 Development of smart data
- 3 Data Science
- 3.1 Evolution of the data economy
- 3.2 A data science definition
- 3.3 Data science techniques in sales and marketing
- 3.3.1 Introduction of the data mining process
- 3.3.2 Data modelling in sales and marketing
- 3.3.3 Model evaluation and deployment
- 4 Research Design
- 4.1 Conceptual framework
- 4.2 The selected industry
- 5 Case study results
- 5.1 Summary and validation of expert information
- 5.2 Data science framework
- 6 Overview of future approaches in data science
- 6.1 Challenges and opportunities in practice
- 6.2 Limitations
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This bachelor thesis aims to identify current trends in data science applications within the consumer-packaged goods (CPG) industry, specifically focusing on sales and marketing. It addresses the opportunities and challenges of utilizing big and smart data in this sector, drawing on expert interviews to fill a gap in existing literature.
- The evolution and application of big and smart data in sales and marketing.
- Data science methodologies and techniques used in the CPG industry.
- Challenges and opportunities presented by data science implementation in CPG companies.
- The role of data science teams and their organizational structures.
- The development of a data-driven mindset within CPG organizations.
Zusammenfassung der Kapitel (Chapter Summaries)
1 Introduction: This chapter introduces the thesis topic, motivated by the increasing importance of data science in various industries, particularly the CPG sector. It establishes the research objective—identifying the importance and trends of big or smart data in CPG sales and marketing—and outlines the structure of the thesis, which comprises seven chapters.
2 Fundamentals of big and smart data: This chapter explores the characteristics of big data, including volume, velocity, variety, value, veracity, and variability. It traces the historical development of big data, highlighting its growth and impact on businesses. Furthermore, it defines smart data as the transformation of big data into actionable insights through various analytical techniques, and illustrates the sequence from big to smart data analytics.
3 Data Science: This chapter details the evolution of the data economy, showing how data modeling progressed from early statistical methods to today's data-driven approaches. It provides a definition of data science, emphasizing its interdisciplinary nature, encompassing statistical methods, business knowledge, and computer science. The chapter then thoroughly explains data mining, data science techniques (statistical and machine learning), and model evaluation.
4 Research Design: This chapter outlines the research methodology employed in the thesis. It describes the use of qualitative in-depth semi-structured interviews, the Zaltman Metaphor Elicitation Technique (ZMET), and Mayring's qualitative content analysis. The chapter also justifies the selection of the CPG industry as the focus area, highlighting its unique characteristics and untapped potential for data science applications. The chapter concludes by giving a detailed explanation on the inductive approach that will be applied to the collected material.
5 Case study results: This chapter presents the findings from the qualitative content analysis of expert interviews. It details the coding process, including the development and validation of coding categories using Krippendorff's alpha. The chapter culminates in the identification of nine key trends in data science for sales and marketing within the CPG industry, forming the basis for a proposed data science framework.
6 Overview of future approaches in data science: This chapter discusses future challenges and opportunities for data science in the CPG industry. Based on the analysis of the interview data, it identifies areas for improvement and potential solutions. It addresses areas like automated data preparation, more intelligent algorithms, improved data accessibility, stronger B2B data science capabilities, and the development of a data-driven mindset within organizations. Furthermore it emphasizes on challenges and opportunities from an individual perspective, which were often neglected in other chapters.
Schlüsselwörter (Keywords)
Big data, smart data, data science, data mining, consumer-packaged goods (CPG), sales, marketing, machine learning (ML), artificial intelligence (AI), qualitative content analysis, expert interviews, data science framework, data-driven decision making, competitive advantage, challenges, opportunities.
Frequently Asked Questions: A Comprehensive Language Preview of Data Science in the CPG Industry
What is the main topic of this document?
This document is a comprehensive preview of a bachelor thesis that explores the current trends in data science applications within the consumer-packaged goods (CPG) industry, specifically focusing on sales and marketing. It investigates the opportunities and challenges of using big and smart data in this sector, using expert interviews to address gaps in existing literature.
What are the key themes explored in the thesis?
The thesis explores several key themes, including: the evolution and application of big and smart data in sales and marketing; data science methodologies and techniques used in the CPG industry; challenges and opportunities presented by data science implementation in CPG companies; the role of data science teams and their organizational structures; and the development of a data-driven mindset within CPG organizations.
What is the structure of the thesis?
The thesis is structured into six chapters: an introduction; a chapter on the fundamentals of big and smart data; a chapter on data science, including data mining and modeling; a chapter on research design; a chapter presenting case study results from expert interviews; and a concluding chapter providing an overview of future approaches in data science for the CPG industry, along with challenges and opportunities.
What research methodology was used?
The research employed a qualitative approach, utilizing in-depth semi-structured interviews, the Zaltman Metaphor Elicitation Technique (ZMET), and Mayring's qualitative content analysis. The CPG industry was chosen as the focus area due to its unique characteristics and untapped potential for data science applications. An inductive approach was used to analyze the collected data.
What are the key findings of the thesis?
The case study results chapter presents findings from the qualitative content analysis of expert interviews, including the identification of nine key trends in data science for sales and marketing within the CPG industry. These findings form the basis for a proposed data science framework.
What are the future challenges and opportunities in data science for the CPG industry?
The concluding chapter discusses future challenges and opportunities, including the need for automated data preparation, more intelligent algorithms, improved data accessibility, stronger B2B data science capabilities, and the development of a data-driven mindset. It also highlights challenges and opportunities from an individual perspective.
What are the key terms and concepts discussed in this thesis?
Key terms include: big data, smart data, data science, data mining, consumer-packaged goods (CPG), sales, marketing, machine learning (ML), artificial intelligence (AI), qualitative content analysis, expert interviews, data science framework, data-driven decision-making, competitive advantage, challenges, and opportunities.
Where can I find more details about each chapter?
The document provides chapter summaries which detail the content of each chapter. These summaries describe the topics covered and the methodology used within each section of the thesis.
- Quote paper
- Julia Ertel (Author), 2021, Big or Smart Data? Recent trends in Data Science for sales and marketing, Munich, GRIN Verlag, https://www.grin.com/document/1169258
