Data science in the business environment: customer analytics case studies in SMEs

Jing Lü

29 citations0/0 files storedJournal of Modelling in Management

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Big Data and Business Intelligence Data Mining and Machine Learning Applications Competitive and Knowledge Intelligence

Abstract

Purpose A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising. Design/methodology/approach Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions. Findings Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams. Research limitations/implications The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper. Originality/value Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

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References

2013Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 2019Prescriptive analytics: Literature review and research challenges 2017Data Science 2019Deep learning in business analytics and operations research: Models, applications and managerial implications 2017Business Process Variability Modeling 2020Customer experience management in the age of big data analytics: A strategic framework 2015Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance 2016How Can SMEs Benefit from Big Data? Challenges and a Path Forward

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2022Investigating the Determinants of Big Data Analytics Adoption in Decision Making: An Empirical Study in New Zealand, China, and Vietnam 2021Towards a Conceptual Framework for Customer Intelligence in the Era of Big Data

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