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Research paper

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

Michael Dupin and Sophia Tannir
Applied Marketing Analytics: The Peer-Reviewed Journal, 10 (2), 142-157 (2024)
https://doi.org/10.69554/JDYP3666

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

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Author's Biography

Michael Dupin serves as an adjunct professor at Merrimack College, bringing over two decades of expertise in data and statistical analytics to his role. An ardent enthusiast of data science, Dr Dupin has cultivated a diverse career that spans academia, finance and market research. He founded and led the Data Science department at C Space, focusing on pioneering artificial intelligence applications. His tenure in the banking sector involved leading significant projects in macroeconomic stress testing, risk management, financial modelling and statistical model validation. Prior to his corporate engagements, Dr Dupin was a research fellow at Harvard University, where he developed models to study blood flow in tumours. His academic credentials include degrees in nuclear physics and instrumentation, culminating in a PhD in computational fluid dynamics. Outside of his professional pursuits, he is an avid sailor and holds a captain's licence in the Merchant Marine.

Sophia Tannir is a data scientist based in Kentucky who has worked primarily in the consulting space. She holds a master of science in data science from Vanderbilt University and a Bachelor of Arts from Smith College in economics and statistical and data sciences. Her main interests lie in data ethics, sports analytics, statistical modelling and predictions, as well as large language models.

Citation

Dupin, Michael and Tannir, Sophia (2024, September 1). Overcoming analysis paralysis in bulk: Efficient methods for extensive key driver analyses. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 10, Issue 2. https://doi.org/10.69554/JDYP3666.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 10 / Issue 2
© Henry Stewart
Publications LLP

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