arXiv:2607.05761v1 Announce Type: new Abstract: Modern data-driven marketing relies on large amounts of consumer data, yet collecting such data can be costly, time-consuming, and difficult to scale. This research examines whether large language models (LLMs) can be used to generate synthetic consumer data for projective techniques, a set of methods designed to elicit consumer associations, emotions, wants, and needs. We test LLM-generated responses across multiple projective tasks, LLMs, prompting strategies, and temperature settings, and compare them with human responses from a primary resear
The rapid advancement and accessibility of large language models are enabling their application to complex tasks like consumer insight generation, as traditional data collection methods face increasing scrutiny and cost pressures.
This research suggests a fundamental shift in market research, allowing for faster, cheaper, and potentially more scalable generation of consumer insights, impacting marketing strategies and product development across industries.
Market research methodologies will increasingly incorporate synthetic data generation by LLMs, reducing reliance on conventional primary research and potentially democratizing access to nuanced consumer understanding.
- · AI companies
- · LLM developers
- · Marketing tech
- · Data-driven businesses
- · Traditional market research firms
- · Primary data collection services
Companies gain the ability to rapidly test product concepts and marketing messages against synthetically generated consumer profiles without extensive fieldwork.
This could lead to a proliferation of highly targeted products and services, potentially increasing market fragmentation and competition as barriers to entry for user understanding are lowered.
Ethical considerations around the realism and potential biases of synthetic consumer data will become a major regulatory and industry focus, potentially leading to new standards for AI-generated insights.
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Read at arXiv cs.AI