Big or Smart Data? Recent trends in Data Science for sales and marketing

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

7 Conclusion

Research Objectives & Key Topics

This dissertation investigates the identification and application of big and smart data science trends within the marketing and sales departments of the consumer-packaged goods (CPG) industry, with the primary objective of addressing actual opportunities for these businesses in the field of data science.

• Identification of nine overarching data science trends via expert interviews.
• Analysis of expert experiences, challenges, and emotional responses regarding data science interaction.
• Application of Mayring’s qualitative content analysis to derive a data science framework.
• Development of improvement proposals including automated data cleaning, intelligent algorithms, and focus teams.

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Summary of Chapters

1 Introduction: Introduces the topic of big and smart data, the motivation for the research, and the focus on the CPG industry.

2 Fundamentals of big and smart data: Explains the characteristics of big data and the emergence of smart data for business value.

3 Data Science: Details the evolution of the data economy, defines data science, and explores techniques used in sales and marketing.

4 Research Design: Describes the conceptual framework and the selection of the CPG industry for the case study.

5 Case study results: Summarizes and validates the expert information and develops the data science framework.

6 Overview of future approaches in data science: Discusses practical challenges, opportunities, and limitations of the conducted research.

7 Conclusion: Synthesizes the main findings and provides a concluding overview of the research.

Keywords

Big Data, Smart Data, Data Science, CPG Industry, Sales and Marketing, Predictive Analytics, Machine Learning, CRISP-DM, Qualitative Content Analysis, Data Mining, Forecasting, Consumer-Packaged Goods, Customer Insights, Digitalization, Data Economy.

Frequently Asked Questions

What is the core focus of this research?

The thesis focuses on identifying trends in big and smart data science specifically within the sales and marketing departments of the consumer-packaged goods (CPG) industry.

What are the primary themes covered in the work?

Key themes include the transition from big data to smart data, the role of data science in business strategy, data mining processes, and the practical application of machine learning in market forecasting.

What is the central objective of the study?

The objective is to address untapped opportunities and identify real-world trends in data science that can help CPG companies maintain a competitive advantage.

Which scientific methodology is employed?

The research utilizes qualitative in-depth semi-structured interviews with experts, analyzed through Mayring’s qualitative content analysis to build a robust data science framework.

What topics are discussed in the main body?

The main body covers the theoretical fundamentals of big data, the evolution of the data economy, various data mining techniques (like neural networks and regressions), and the detailed case study results from international CPG experts.

What are the characterizing keywords of the work?

The work is characterized by terms such as Big Data, Smart Data, CPG Industry, Predictive Analytics, Machine Learning, and Qualitative Content Analysis.

How does the CPG industry specifically utilize big data?

The industry uses big data for demand forecasting, product placement strategies, customer churn prediction, and analyzing the impact of marketing campaigns on sales turnover.

Why is the "Data-Thinking" mindset important according to the interviews?

Data-thinking facilitates the long-term integration of data science by shifting decision-making processes from emotional or experience-based inputs to evidence-based insights derived from data.