The A-share listed company consumer complaint text database, developed by CnOpenData, systematically integrates consumer complaint records associated with China's A-share listed companies, comprising a total of 3.32 million entries. Through sophisticated entity matching algorithms, this database accurately links massive volumes of publicly available complaint information to corresponding listed company entities, establishing a research bridge connecting micro-level market behaviors with macro-level corporate subjects. This repository achieves large-scale, structured integration of consumer feedback across diverse operating entities (including subsidiaries, brands, and online channels) under listed companies, providing unique longitudinal panel data for observing corporate reputation risks, product/service quality, and consumer relationship dynamics.
Key Features:
- Enterprise-level Penetration: Through systematic name matching and association rules, complaint targets are precisely mapped to listed companies and their affiliated entities (e.g., subsidiaries, controlled brands, official online channels), revealing consumer feedback faced by conglomerates across different business units and market interfaces.
- Multidimensional Granular Coverage: Records are date-specific, supporting long-term dynamic analysis and event studies; comprehensive coverage includes all listed companies and tens of thousands of associated operating entities (e.g., subsidiaries, brands, online stores); original complaint texts are fully preserved, providing rich material for in-depth text mining and sentiment analysis.
- Complementary Field Verification: Employs quadruple verification through "affiliated company name" fuzzy matching, "trademark name" exact matching, "social media account" matching, and "AI large model-enhanced matching" techniques. These fields mutually complement and validate each other, while the core logic of "fuzzy-matched name + year" ensures high accuracy in entity linking and data robustness.
Potential Applications:
- Academic Research: Analyze patterns of product defects, root causes of service failures, and effectiveness of corporate crisis communication strategies based on large-scale authentic complaint texts. Investigate the generation and diffusion mechanisms of negative word-of-mouth and their long-term impacts on brand equity.
- Commercial Services: Provide quantitative investment funds and securities research institutes with unique alternative data factors for constructing corporate reputation risk models and ESG (especially strengthening the Social-"S" dimension) scoring systems, supporting investment decisions and risk control.
- Policy Optimization: Promote the establishment of a big data-driven collaborative governance mechanism integrating "corporate self-regulation-consumer supervision-government oversight" to enhance efficiency and transparency in consumer dispute resolution, facilitating continuous business environment optimization.
In summary, this database transforms massive, dispersed market consumer voices into deeply mineable high-value resources, enabling long-term systematic tracking of micro-level market feedback at the listed company level. It provides robust data infrastructure for theoretical innovation in academia, precision decision-making in industry, and scientific regulation in government sectors, demonstrating significant theoretical value and broad application prospects.
Time Period
2018-2024
Data Scale
Field Display
Sample Data
Related Literature
- Cai, W., Pu, Y., & Li, H. (2024). Customer First: Predictive Power of Consumer Online Complaints on Corporate Fundamentals. Management World, 5.
Data Update Frequency
Annual update