PersonaBOT: Bringing Customer Personas to Life with LLMs and RAG : Generating and Utilizing Customer Personas with LLMs and Retrieval-Augmented Generation

The introduction of Large Language Models (LLMs) has significantly transformed Natural LanguageProcessing (NLP) applications by enabling more advanced analysis of customer personas. At VolvoConstruction Equipment (VCE), customer personas are traditionally developed through qualitativemethods, which are time-consuming and lack scalability. The main objective of this research is to generate synthetic customer personas and integrate them into a Retrieval-Augmented Generation (RAG)chatbot to support decision-making in business processes. The study utilizes the Design Science Research Methodology (DSRM) in three iterative cycles. The first iteration focuses on developing apersona-based RAG chatbot integrated with verified personas. In the second iteration, synthetic personas were generated using Few-Shot and Chain-of-Thought (CoT) prompting techniques and evaluated based on completeness, relevance, and consistency using McNemar’s test. In the final iteration,the chatbot knowledge base is augmented with synthetic personas and additional segment informationto assess improvements in response accuracy and practical utility. Key findings indicate that Few-Shotprompting outperformed CoT in generating more complete personas, while CoT demonstrated greaterefficiency in terms of response time and token usage. After augmenting the knowledge base, the average accuracy rating of the chatbot increased from 5.88 to 6.42 on a 10-point scale, and 81.82% ofparticipants found the updated system useful in business contexts.