Professional service firms face complex challenges in acquiring new clients, owing to the high degree of trust, customization and client involvement these services require. This study aims to explore how large language models (LLMs), such as ChatGPT, can analyze early-stage text-based communications to identify linguistic markers that predict purchase readiness. By focusing on the professional services domain, the study contributes to service marketing theory by introducing a novel method to assess service interest through client-generated text.
For two years, the authors collected responses submitted through an inquiry form for a home improvement firm. The artificial intelligence (AI) platform ChatGPT was trained to assess the level of specificity in the language used by prospective customers and generated a specificity score for each response. These scores were then used to predict the likelihood of customer purchase over time using an accelerated hazard model.
The results indicate that the specificity scores generated by ChatGPT effectively predict a customer’s position within the sales funnel and likelihood of purchase over time. Customers who provided more detailed information, as measured by AI, exhibited a higher probability of conversion.
This study provides actionable insights for managers aiming to optimize customer acquisition efforts and minimize resource waste. It demonstrates how AI – specifically LLMs – can be leveraged to analyze unstructured text from prospective customers and identify linguistic signals (e.g. specificity) that are predictive of purchase likelihood.
Consumers reveal valuable insights through the language they use. While it has traditionally been difficult to empirically analyze this verbiage, the emergence of LLMs enables the transformation of qualitative text into measurable indicators. These tools allow firms to make data-driven predictions about customer behavior. This study introduces a novel methodological approach to the analysis of customer acquisition.
