Structuring Panel Data for Real-Time Consumer Insights Delivery

Client Background 

The client is a global consumer insights agency running recurring B2C sentiment studies across 12 international markets. Using mobile and web-based panels, the agency provides brands with timely perspectives on consumer attitudes and behaviors, making consistent data quality critical to its delivery mod 

Client’s Challenge 

The agency was receiving raw panel response data in varying formats that posed challenges for timely reporting. Issues included inconsistent field entries, missing values, free-text answers instead of standardized dropdowns, and multilingual submissions that lacked normalization. With deliverables tied to strict timelines, internal teams were spending excessive time on cleaning and restructuring data, leaving limited capacity for actual analysis. The client turned to Mordor Intelligence to streamline this process and ensure consistent data quality across cycles. 

How Mordor Intelligence Helped 

Implemented Scalable Cleaning Logic 

• Developed rule-based protocols to correct field misalignment, resolve outliers, and standardize response types across input formats.
• Built automated workflows to minimize manual intervention for recurring issues. 

Mapped Open-Ended Responses to Structured Outputs 

• Applied NLP filters to classify free-text inputs and map them to relevant dropdown categories or predefined options.
• Enabled category consolidation across fragmented entries. 

Normalized Multilingual Submissions 

• Deployed language detection and automated translation mapping to ensure formatting consistency across markets.
• Preserved semantic accuracy in localized sentiment data. 

Aligned Outputs to Reporting Cadence 

• Delivered weekly, cleaned datasets pre-aligned with the agency’s dashboard schema and reporting timeline.
• Ensured smooth integration with visualization platforms and internal tracking tools. 

Key Findings 

• Over 20 percent of responses initially failed data quality checks due to format or entry inconsistencies.
• Language-based ambiguities were a recurring cause of misclassification, especially in open-ended fields.
• Cleaning automation reduced turnaround time for raw-to-final dataset processing by over 40 percent. 

Our Industry Coverage 

Mordor Intelligence supports consumer insights agencies, retail analytics providers, and B2C brands in converting unstructured or inconsistent research data into dependable, decision-ready formats. We work across survey-based research, product feedback loops, and behavior tracking datasets. 
Key offerings for the consumer insights and research industry include: 

• Customer Behavior Analysis: Detect and segment patterns across structured and open-ended responses.
• Feedback and Satisfaction Surveys: Standardize and clean large-scale panel data across regions and languages.
• Product Launch and Marketing Claims Analysis: Validate consumer sentiment and expectations pre- and post-launch. 
• Surveys, FGDs, and Primary Research Fieldwork: Integrate qualitative and quantitative inputs for unified reporting.
• Value Chain and Regulatory Assessment: Contextualize feedback within broader operational or policy frameworks.