Can Large Language Models Extract Customer Needs as well as Professional Analysts?
This addresses the time-consuming and complex task of customer need identification for product management and marketing, offering a potential reduction in manual effort and improved insights.
The study tackled the problem of automatically extracting customer needs from textual data by comparing fine-tuned large language models (SFT LLMs) to professional analysts, finding that the SFT LLM performs as well as or better than analysts in terms of accuracy, specificity, and coverage.
Identifying customer needs (CNs) is important for product management, product development, and marketing. Applications rely on professional analysts interpreting textual data (e.g., interview transcripts, online reviews) to understand the nuances of customer experience and concisely formulate "jobs to be done." The task is cognitively complex and time-consuming. Current practice facilitates the process with keyword search and machine learning but relies on human judgment to formulate CNs. We examine whether Large Language Models (LLMs) can automatically extract CNs. Because evaluating CNs requires professional judgment, we partnered with a marketing consulting firm to conduct a blind study of CNs extracted by: (1) a foundational LLM with prompt engineering only (Base LLM), (2) an LLM fine-tuned with professionally identified CNs (SFT LLM), and (3) professional analysts. The SFT LLM performs as well as or better than professional analysts when extracting CNs. The extracted CNs are well-formulated, sufficiently specific to identify opportunities, and justified by source content (no hallucinations). The SFT LLM is efficient and provides more complete coverage of CNs. The Base LLM was not sufficiently accurate or specific. Organizations can rely on SFT LLMs to reduce manual effort, enhance the precision of CN articulation, and provide improved insight for innovation and marketing strategy.