Data-Driven Insights Built on Rigorous Methodology
Our research methodology has been refined over a decade of serving industry leaders. We transform raw e-commerce and social data into actionable intelligence through systematic collection, advanced AI processing, and expert validation.
How much history does Moojing's data have?
China e-commerce data goes back to 2016 at SKU level. History for other markets varies by country and platform.
Who relies on Moojing's methodology?
500+ enterprise clients across FMCG, healthcare, electronics and retail, from multinational corporations to emerging brands.
140+
Team Members
6
Offices · KL + China
2016
Data since
500+
Enterprises Served
Serving leading consumer brands with SKU-level market intelligence.
Enterprises We've Worked With
Helping leading APAC brands across FMCG, retail, and e-commerce
Where does Moojing's data come from?
Four sources: e-commerce sales data from major domestic and international platforms, social media conversation, consumer reviews and ratings, and custom data collection for specific projects.
E-Commerce Sales Data
Transaction-level data from major domestic and international e-commerce platforms, covering multiple categories and SKUs.
- Monthly sales updates
- Price analysis
- Historical data since 2016
Social Media Intelligence
Comprehensive social listening across major platforms capturing consumer sentiment and trending topics.
- Sentiment analysis
- Trend detection
- Influencer analysis
Review & Rating Analysis
Structured extraction and analysis of consumer reviews to understand product perception and pain points.
- Opinion mining
- Aspect classification
- Competitive benchmarking
Custom Data Integration
Flexible data collection services tailored to your specific market intelligence requirements.
- Platform-specific extraction
- API integrations
- Historical backfills
How is the data analysed?
Machine-learning models handle trend prediction, sentiment analysis, anomaly detection and cross-platform normalisation, and industry analysts review the findings for business relevance.
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Machine Learning Models Proprietary algorithms for trend prediction, sentiment analysis, and anomaly detection refined on China market data collected since 2016.
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Data Normalization Cross-platform standardization ensuring consistent, comparable metrics across different data sources and time periods.
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Trend Detection Early identification of emerging market opportunities using our high-growth concept screening algorithms.
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Expert Validation Industry analysts review and contextualize automated findings to ensure business relevance and accuracy.
What do Moojing's AI models do?
Three jobs: detect high-growth concepts and emerging brands, unify brands and categories across platforms, and map market performance to listed companies.
Growth Detection
Proprietary algorithms identify high-growth concepts, market segments, and emerging brands before they become mainstream.
- High-growth concept screening
- Emerging brand discovery
- Market segment identification
Cross-Platform Intelligence
Unified view across fragmented e-commerce platforms through automated brand aggregation and category normalization.
- Brand identity aggregation
- Category normalization
- SPU-level product matching
Financial Mapping
Connect market performance data to publicly listed companies for investment analysis and corporate intelligence.
- Listed company mapping
- Brand portfolio analysis
- Market share attribution
How does Moojing check data quality?
Three layers: fraud detection filters out fake orders, bot activity and abnormal prices; validation checks completeness and consistency across platforms; and each data type is refreshed on its own schedule, monthly for e-commerce and daily for some social platforms.
Fraud Detection
Proprietary algorithms identify and filter fake orders, bot activity, and manipulated content from our datasets.
- Sybil account detection
- Fake order filtering
- Abnormal price filtering
- Advertisement filtering
Data Validation
Automated and manual checks verify data completeness, consistency, and accuracy through cross-platform normalization.
- Anomaly detection
- Cross-platform brand aggregation
- Category normalization
- SPU product matching
Systematic Updates
Differentiated update frequencies optimized for each data type ensure comprehensive market coverage.
- Monthly e-commerce updates
- Daily social analysis
- Data history back to 2016
Who maintains the methodology?
Moojing's data engineers and data scientists, working with its industry analysts.
Data Engineers
Specialists in data pipeline architecture, quality assurance, and multi-platform integration ensuring clean, reliable data flows.
- Pipeline optimization
- Quality assurance automation
- API development
Data Scientists
Machine learning experts developing proprietary algorithms for trend detection, sentiment analysis, and growth opportunity identification.
- ML model development
- NLP and sentiment analysis
- Predictive analytics
Where has Moojing's research been presented?
Our research methodology and market insights are regularly featured at industry conferences and recognised by brands, distribution channels, and academic institutions across APAC.
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Industry Conference Speaker Invited to present at leading conferences covering cosmetics, food, healthcare, home appliances, and FMCG sectors.
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Cross-Industry Partner Recognized partner for brand owners, consulting firms, financial institutions, and government clients.
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Academic Collaboration Contributing to industry digital transformation through research partnerships and data-driven insights.
Frequently Asked Questions
Publicly available e-commerce and social media data, analysed with Moojing's own AI and machine-learning models: SKU-level sales and pricing, consumer reviews, and social conversation.
E-commerce data is refreshed monthly, and data from some social platforms daily.
China e-commerce data goes back to 2016 at SKU level. History for other markets varies by country and platform.
Proprietary algorithms filter out fake orders, bot and sybil accounts, abnormal prices and advertising before the data reaches your analysis.
See Our Methodology in Action
Request a sample dataset to experience the depth and quality of our research methodology firsthand. Our team will walk you through how we can support your specific intelligence needs.