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Revolutionising Market Surveys through Unprecedented Generative AI for Efficient Data Synthesis

Abstract(s): Rahul Pandey, Nishchay Mahor, Shubham Kansal


This study addresses challenges inherent in traditional market survey methodologies, necessitating a transformative approach to overcome delayed insights and suboptimal responses. Extended survey cycles pose a threat to decision-making efficiency, risking missed opportunities crucial for businesses. Our avant-garde strategy pioneers the integration of cutting- edge Generative Pre-trained Transformers (GPTs) and ensemble Language Models (LLMs).

The primary objective is a comprehensive overhaul of conventional survey techniques, emphasising synthetic data generation efficiency while maintaining stringent privacy and ethical considerations. Capitalising on advanced generative AI techniques, our methodology utilises GPTs and ensemble LLMs to ensure transparency and clarity in dataset synthesis. Tangible impact is demonstrated through real-world scenarios and meticulously analysed case studies, spotlighting specific metrics showcasing remarkable efficiency gains.

A candid acknowledgement of limitations fortifies the depth of our proposed solution. Elevating ethical considerations to a paramount position, our approach places a robust emphasis on user privacy and data security. Compliant with stringent data protection regulations, our GPT and ensemble LLM-led approach not only accelerates survey processes but also forges personalised engagement, constituting a distinctive value proposition in addressing prevalent business challenges. In conclusion, this pioneering methodology signifies a paradigm shift, reshaping market survey practices and setting an industry benchmark for ethical and efficient data synthesis.

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