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.
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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3.3.2 Data modelling in sales and marketing
Before applying data mining techniques, one needs to decide on the input data being a cluster or a sample. While a sample is a randomly selected part of a larger population or customer base, a cluster, such as weekend promotion shoppers, combines data entries with similarities in a selected variable (cf. Bradlow et al., 2017, p. 27). Sample findings are mostly identified with statistical techniques and transferred to the entire database, which might not represent the segment of focus (cf. Malthouse and Li, 2017, p. 233). Whereas clusters are mostly defined and analyzed via ML algorithms as described below. For a better orientation, the addressed data mining methods are visually organized in figure 6.
Classic statistical data mining techniques can be subdivided into statistical models and time series analysis, whereas the first includes multivariate statistics, such as simple linear regressions, but also structural equation modelling (SEM) or generalizable linear models. A maximum likelihood estimator is also part of the statistical modelling techniques, specifically a computer-based model which can prognose a function of the observed phenomenon by describing the data in detail, such as an individualized online demand model including the return time of customers, their related volume of sales, as well as substitutional product sales in case of unavailability (cf. Feng and Shanthikumar, 2018, p. 1673).
For example, continuously available online sales data offers businesses the possibility to nowcast market demand and respond to market changes (cf. See-To and Ngai, 2018, p. 428) even faster by applying such model estimations. Chong et al. (2016, p. 374-376), as well as in their subsequent analysis (cf. Chong et al.; 2017, p. 5152), analyzed various influencing factors of demand for electronic goods and identified discount offerings for items with high sales volumes as the most effective one. They also state that the combination of online feedback and discounts has a significant impact on revenue. The study from Feng et al. (2020, p. 10f) is one of the first to focus on mobile commerce data, which allows the discovery of behavioral trends of customers and required business partners.
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.
- Arbeit zitieren
- Julia Ertel (Autor:in), 2021, Big or Smart Data? Recent trends in Data Science for sales and marketing, München, GRIN Verlag, https://www.grin.com/document/1169258
