Overcoming analysis paralysis in bulk: Efficient methods for extensive key driver analyses

Michael Dupin and Sophia Tannir
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Michael Dupin: Adjunct Professor, Merrimack College, USA
Sophia Tannir: Data Scientist, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2024, vol. 10, issue 2, 142-157

Abstract: In the rapidly changing field of market research, identifying the key drivers of customer relationships is essential for enhancing business strategies and customer satisfaction. This paper explores the application of driver analysis, a critical methodology that assists businesses in pinpointing the crucial factors influencing customer behaviour and satisfaction. By effectively distinguishing impactful elements from less relevant ones, this technique enables more precise decision making and strategy development. The core of this paper introduces an innovative method for categorising drivers into primary and secondary groups, simplifying the complex data landscape and focusing on the most influential factors. This new grouping method significantly reduces the analytical complexity typically associated with traditional models, making the insights more accessible and actionable for businesses. A case study utilising the Kiwis Count survey — a comprehensive public service evaluation in New Zealand — serves to illustrate this methodology in a real-world context. By applying the proposed method to this survey, the paper provides a detailed examination of how various demographic groups perceive public services and what drives their satisfaction. The results reveal distinct patterns in how different demographics value aspects of service delivery, from staff competence to trust and transparency. By focusing on the most impactful drivers, organisations can allocate resources more effectively, enhance customer experiences and ultimately achieve greater customer loyalty and success.

Keywords: Key Driver Analysis (KDA); categorisation; data-driven strategy; noise reduction; enhanced data interpretation (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2024
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