In today’s highly competitive FMCG landscape, brand consistency across marketplaces is critical. Nestlé products such as Nescafé, Maggi, KitKat, and Cerelac are sold across multiple online platforms in India including Amazon, Flipkart, and BigBasket.
However, inconsistencies in product titles, pack sizes, seller listings, discounts, and availability create major challenges for price monitoring, MAP compliance, and digital shelf analytics.
Iweb Data Scraping implemented an AI-powered automated product matching and marketplace data scraping solution to unify Nestlé SKUs across platforms in real time.
The solution delivered:
This case study explains the business challenge, the technical architecture, the scraping methodology, and the final business outcomes.
Nestlé operates across multiple FMCG categories including:
Each marketplace represents products differently:
For large-scale brands, manually matching SKUs across marketplaces is impossible.
That’s where Iweb Data Scraping stepped in.
When Nestlé products appear on:
They may look like different products even when they are identical.
Example Issue:
| Platform | Product Title |
|---|---|
| Amazon | Nestlé Maggi 2-Minute Noodles Masala 70g Pack of 4 |
| Flipkart | Maggi Masala Instant Noodles 70 gm (4 Pack) |
| BigBasket | MAGGI 2-Minute Masala Noodles – 70 g x 4 |
For analytics engines, these are 3 different SKUs unless matched correctly.
Without automated product matching:
Iweb Data Scraping was assigned to:
Iweb Data Scraping implemented:
The web scraping API collected:
Amazon Sample
{
"platform": "Amazon",
"product_title": "Nestle Maggi 2-Minute Noodles Masala 70g Pack of 4",
"price": 52,
"mrp": 56,
"seller": "RetailNet",
"availability": "In Stock",
"rating": 4.4,
"reviews": 18234
}Flipkart Sample
{
"platform": "Flipkart",
"product_title": "Maggi Masala Instant Noodles 70 gm (Pack of 4)",
"price": 50,
"mrp": 56,
"seller": "SuperMart",
"availability": "In Stock",
"rating": 4.3,
"reviews": 15420
}BigBasket Sample
{
"platform": "BigBasket",
"product_title": "MAGGI 2-Minute Masala Noodles 70 g x 4",
"price": 54,
"mrp": 56,
"seller": "BigBasket Retail",
"availability": "Available",
"rating": 4.5,
"reviews": 8342
}.png "Sample-Raw-Scraped-Data")
Iweb Data Scraping implemented a 4-layer product matching model:
Layer 1: Title Normalization
Layer 2: Attribute Extraction
Extract structured fields:
Layer 3: Similarity Score
Compute similarity using:
Layer 4: Image Matching
After matching:
{
"unified_sku_id": "MAGGI_70G_4PACK_MASALA",
"brand": "Nestle",
"product_name": "Maggi 2-Minute Masala Noodles",
"pack_size": "70g",
"pack_count": 4,
"amazon_price": 52,
"flipkart_price": 50,
"bigbasket_price": 54,
"lowest_price": 50,
"highest_price": 54,
"price_variation_percent": 8,
"availability_status": {
"amazon": "In Stock",
"flipkart": "In Stock",
"bigbasket": "Available"
}
}
1. Inconsistent Naming Conventions
Solved through NLP-based parsing.
2. Missing EAN Codes
Used attribute-level matching.
3. Pack Size Confusion
Converted units and extracted pack multipliers.
4. Combo Products
Separated bundle SKUs.
5. Rapid Price Fluctuations
Implemented hourly scraping API refresh.
This project leveraged and targeted:
1. SKU Matching Accuracy
98.7% accurate matching across platforms.
2. Price Monitoring Efficiency
Reduced manual work by 75%.
3. Duplicate Seller Detection
Identified 11% unauthorized sellers.
4. Price Variation Insights
Detected up to 12% marketplace variance.
5. Real-Time API Delivery
Integrated into Nestlé analytics dashboard.
The client received:
Iweb Data Scraping proposed:
Iweb Data Scraping specializes in:
The team ensures:
Automated product matching across Amazon, Flipkart, and BigBasket is no longer optional for FMCG brands.
Without intelligent SKU mapping and real-time scraping APIs, brands risk inaccurate analytics, pricing conflicts, and revenue leakage.
Iweb Data Scraping delivered a scalable AI-driven marketplace data scraping and SKU matching system that transformed Nestlé’s digital commerce visibility.
From inconsistent listings to unified SKUs, from fragmented price data to real-time intelligence — the transformation was data-driven, automated, and measurable.
We start by signing a Non-Disclosure Agreement (NDA) to protect your ideas.
Our team will analyze your needs to understand what you want.
You'll get a clear and detailed project outline showing how we'll work together.
We'll take care of the project, allowing you to focus on growing your business.