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Enhancing market research with a GPT-based API wrapper: A leap towards advanced data analysis
Applied Marketing Analytics: The Peer-Reviewed Journal,
10
(4),
348-373
(2025)
Abstract
This paper introduces an innovative application programming interface (API) wrapper built around OpenAI’s GPT, designed to significantly enhance the capabilities of large language models (LLMs) for market research. Traditional applications of GPT in survey crafting, response analysis and open-ended query interpretation, although effective, are limited by the standard interfaces provided. These interfaces (such as ChatGPT) often fall short when handling complex analytical tasks and ensuring data security, particularly in data-sensitive sectors like healthcare and finance. Our proposed API wrapper addresses these shortcomings by extending GPT’s functionalities beyond basic text generation and interaction. By integrating a sophisticated user interface (UI) and query management system, the wrapper simplifies interactions between users and the LLM. This allows researchers and analysts, regardless of their programming expertise, to leverage advanced data analysis tools and gain deeper insights from large datasets. Moreover, the wrapper ensures high standards of data privacy and security. Key features of the API wrapper include flexible UI options, ranging from open-source platforms to commercial software-as-a-service solutions that cater to diverse organisational needs and technical capabilities. The wrapper also incorporates advanced query handling and error management techniques to enhance user experience and efficiency. Through realworld applications and case studies, we demonstrate the wrapper’s ability to facilitate complex data analysis tasks such as thematic analysis of narrative data, comparative studies and multilingual data processing. The development and implementation of this wrapper marks a significant step towards democratising access to powerful AI tools in market research. It opens new possibilities for extracting nuanced insights and conducting sophisticated analyses without the need for deep technical knowledge or significant manual labour, thereby broadening the scope and impact of research methodologies in various industries.
Keywords: large language models (LLMs); GPT; text analysis; API wrapper; automated insights
The full article is available to subscribers to the journal.
Author's Biography
Michael Dupin is the Director of Artificial Intelligence for Environmental, Social, and Governance and Corporate Finance at Wolters Kluwer. He is also an adjunct professor at Merrimack College, bringing over two decades of expertise in data and statistical analytics to his role. 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.
M. Furkan Oruç M. Furkan Oruc is a data scientist whose expertise lies at the intersection of machine learning, maritime safety and artificial intelligence (AI) driven solutions. He holds a bachelor’s degree from Bogazici University, in addition to dual master’s degrees in statistics and computer science and civil engineering. His professional journey includes contributions at Google’s marketing department and Skylight at the Allen Institute for AI, where he focused on mitigating illegal fishing activities via deep-learning based weather prediction models. At C Space, Furkan worked on AI engineering applications, utilising large language models to transform unstructured survey data into actionable insights, helping non-technical consultants navigate complex datasets with ease. His research on maritime collision prediction via machine learning has been published in Expert Systems with Applications.
Citation
Dupin, Michael and Oruç, M. Furkan (2025, March 1). Enhancing market research with a GPT-based API wrapper: A leap towards advanced data analysis. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 10, Issue 4. https://doi.org/10.69554/AWCT1053.
© Henry Stewart
Publications LLP
Publications LLP