10th International Congress on Information and Communication Technology

Authors - Zachary Matthew Alabastro, Stephen Daeniel Mansueto, Joseph Benjamin Ilagan
Abstract - Product innovation is critical in strategizing business decisions in highly-competitive markets. For product enhancements, the entrepreneur must garner data from a target demographic through research. A solution to this involves qualitative customer feedback. The study proposes the viability of artificial intelligence (AI) as a co-pilot model to simulate synthetic customer feedback with agentic systems. Prompting with ChatGPT-4o’s homo silicus attribute can generate feedback on certain business contexts. Results show that large language models (LLM) can generate qualitative insights to utilize in product innovation. Results seem to generate human-like responses through few-shot techniques and Chain-of-Thought (CoT) prompting. Data was validated with a Python script. Cosine similarity tested the similarity of datasets to quantify the juxtaposition of synthetic and actual customer feedback. This model can be essential in reducing the total resources needed for product evaluation through preliminary analysis, which can help in sustainable competitive advantage.