From UGC to Brand Product Improvement: Mining Consumer Innovation Insights Across Social Media Platforms

In the era of e-commerce, consumers’ innovative insights are crucial for brands to improve their products and meet consumers’ potential needs. Although user-generated content (UGC) platforms have accumulated a vast amount of consumer voices, brands often face the dilemma of “rich data but scarce insights”: valuable consumer innovation insights are not only scarce but also expressed implicitly. Traditional methods have difficulty reliably identifying such insights in this task. Therefore, this study, for the first time, introduces a large language model (LLM)-augmented Siamese framework, aiming to improve the identification and generalization of consumer innovation insights in multi-platform UGC. Based on four Chinese social media platforms and a dataset of 133,538 comments, we conducted three experiments and performed a systematic comparison under five model configurations. The results show that our approach significantly outperforms traditional benchmarks and strong baselines in most experimental settings, while maintaining stable competitiveness in cross-platform tests. Finally, we showed how the identified innovation insights can be transformed into structured outputs for product planning, thereby facilitating the integration of consumer insights into the brand’s innovation process.