Case Study
Multilingual Customer Intelligence system for FMCG D2C Expansion
Problem statement
The client faced a critical problem common to legacy FMCG businesses pivoting to D2C: fragmented, unstructured customer data across disconnected systems—in multiple languages.
- 5 million customers spread across legacy retail records, invoices, and scanned documents
- Customer data stored in English, Gujarati, and Hindi reflecting their multi-regional customer base
- Manual customer retrieval processes took 2-3 days, creating a severe bottleneck for D2C personalization
- No unified view of customer purchase history, preferences, or lifetime value across language barriers
- Legacy data in image/PDF format with mixed language content prevented integration with modern D2C platforms (Magento, Shopify, custom ecommerce)
- Language fragmentation created operational chaos: customer records split across language silos, making it impossible to build a single customer view
- Missing opportunity: couldn’t leverage customer data for AI-driven personalization, targeted promotions, or predictive analytics
Key Areas Addressed
- Digitized 5 million legacy customer records
- Unified customer view
- Quick Customer Lookup
- Improving Data Accuracy
- Language-aware customer segmentation
- Direct API integration
- Regional Specific Personalization
The Business Impact:
- Without fast customer access and language unification, they couldn’t provide seamless D2C experiences across regional marketE.
- Language silos prevented unified customer intelligence for targeted marketing (critical for D2C margins)
- Inability to identify high-value customers across language boundaries or predict churn at scale
- Regulatory & compliance risks with unorganized, multilingual customer data
- Missed opportunity for region-specific personalization and marketing
Why Standard Solutions Failed
Key Features of the system we designed
We engineered a scalable, intelligent platform that transformed 5 million legacy customer records—spanning English, Gujarati, and Hindi—into a unified, searchable, and actionable customer intelligence hub designed for D2C ecommerce integration.
Multilingual Intelligent Data Ingestion & Processing
- Advanced multilingual OCR (pytesseract, pdfplumber, pdfminer.six) handling varied legacy formats in English, Gujarati, and Hindi
- Language detection: at record level, automatically identifying language of each customer document
- Script-aware text extraction supporting Devanagari script (for Hindi/Gujarati) and Latin script (for English)
- Multilingual data validation with language-specific parsing rules ensuring accuracy across all three languages
- Automated customer record deduplication across 5M+ records using multilingual fuzzy matching and ML-powered entity resolution (handling name variations across languages)
- Smart data validation with pydantic ensuring 99.5% accuracy in extracted customer information across all languages
- Batch processing optimized for massive volume with parallel multilingual pipelines
Unified Customer Identity Graph (Language-Agnostic)
- Single source of truth for each customer: consolidated contact info, address, purchase history, preferences unified regardless of language stored
- Multilingual name matching and deduplication recognizes that “राज शर्मा” (Hindi), “રાજ શર્મા” (Gujarati), and “Raj Sharma” (English) are the same customer
- Real-time customer lookup via FastAPI endpoints (<100ms response) with automatic language translation/normalization
- Ready-to-sync with D2C platforms with language preferences preserved for personalized communication
D2C-Ready Multilingual Intelligence Layer
- Customer segmentation based on historical behavior and purchase patterns across language groups
- RFM (Recency, Frequency, Monetary) analysis for high-value customer identification
- Predictive churn scoring and lifetime value calculation
- Regional personalization engine: deliver marketing messages in customer’s preferred language (English, Gujarati, or Hindi)
- API-first architecture integrating with marketing automation, recommendation engines, and multilingual personalization layers
Technology Used
Pandas
NumPy
TextBlob
spaCy
pdfminer
Results That Drive D2C Growth
Spanning three languages (English, Gujarati, Hindi) in the first month—95% usable data quality
Spanning three languages (English, Gujarati, Hindi) in the first month—95% usable data quality
that previously fragmented customer intelligence and regional operations
across all language variants—enabling real-time personalization and multilingual customer service
through intelligent multilingual deduplication and validation
enabling region-specific and language-specific targeted marketing campaigns
infrastructure supports churn prediction, upsell recommendations, and lifetime value optimization across all language communities
marketing messages automatically delivered in customer’s preferred language, improving engagement and conversion
Business Impact: From Fragmented Multilingual Chaos to Unified Regional Powerhouse
Each of 5M customers receives contextually relevant offers in their preferred language based on actual purchase history
Identify trends and preferences across English-speaking, Gujarati-speaking, and Hindi-speaking customer segments
Organized, auditable customer data with language preservation reduces regulatory risk across regions
