This is like having a very smart assistant read through millions of customer reviews on an app store or marketplace and then automatically build the same satisfaction metrics your research team would create—things like “service quality”, “ease of use”, or “value for money”—without hand-coding survey questions or rules.
Traditional customer satisfaction measurement in platform-based services (e.g., marketplaces, apps, sharing-economy platforms) relies on costly surveys or simplistic star ratings that miss nuance and scale poorly. This work uses large language models to turn unstructured review text into structured satisfaction constructs, giving richer, scalable insight into what actually drives satisfaction and dissatisfaction.
Proprietary labeled datasets linking text reviews to validated satisfaction constructs, plus domain-specific prompt designs and validation methodology, can be a strong moat for a platform operator applying this research in production.
Hybrid
Vector Search
High (Custom Models/Infra)
Label quality and construct validity across domains and languages; LLM inference cost at large review volumes.
Early Adopters
Moves beyond simple sentiment or star-rating prediction to modeling richer, validated satisfaction constructs from review text, which aligns with academic service-quality models and is more actionable for platform design and management.