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Data Intelligence System for Linking Customer Complaints with Sales Performance

We built a data intelligence and cross-referencing system that connects customer complaints with sales data to identify which product quality issues have the greatest business impact.
 By integrating complaint analytics from Re:amaze and sales metrics from BigQuery, the system helps teams prioritize fixes, improve product quality, and enhance customer satisfaction based on actual revenue data.

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Summary :

We have developed a business intelligence solution that merges customer support data with sales performance to uncover revenue-impacting product issues. The system automatically cross-references complaints with SKU-level sales metrics, ranks problems by severity, and generates actionable insights for quality and supplier management. It enables e-commerce teams to make informed, data-backed decisions about quality improvements that directly affect profitability.

Problem

Problem

Objectives

  • Combine Quality and Sales Data

    Correlate customer complaints with actual product sales.

  • Prioritize High-Impact Issues

    Identify which defects affect best-selling SKUs.

  • Enable Supplier Accountability

    Use data-backed insights for vendor performance management.

  • Improve Product Quality Strategically

    Focus fixes where they create maximum business value.

  • Automate Reporting

    Generate clear, structured summaries for cross-functional teams.

Challenges

  • Integrating two large-scale datasets (complaints and sales) with different schemas.
  • Parsing unstructured complaint data for relevant tags and product mentions.
  • Automating correlation and visualization at SKU-level granularity.
  • Maintaining data consistency and report generation efficiency.

Solution

  • We built a cross-referencing data intelligence system that connects complaint data from Re:amaze with sales data from BigQuery.
  • The AI engine parses complaint content, identifies patterns, and links issues with corresponding SKUs to calculate complaint-to-sales ratios.
  •  It automatically generates insights showing which quality problems affect top-performing products, along with actionable summaries for suppliers and operations teams.
  •  This creates a continuous feedback loop between customer experience, sales performance, and product quality management.

Technologies

Architecture

Data Extraction
Pull complaint and sales data from respective APIs.
Parsing & Tagging
NLP model extracts product mentions and issue types.
Cross-Referencing
Link complaints with SKU-level sales metrics.
Insight Generation
AI calculates impact ratios and revenue-weighted severity.
Reporting
Generate structured summaries for business and supplier review.

Results & Impact

Identified high-revenue products with recurring quality issues

Automated complaint-to-sales correlation and ranking

Enabled data-driven supplier accountability

Improved prioritization of quality investments

Enhanced collaboration between QA, CX, and finance teams

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