Reliance Retail vs D-Mart Price & SKU Comparison A Data Scraping Case Study by Food Data Scrape

Lack of Transparent Price Comparison

Brands and analysts often ask:

• Which retailer offers lower prices for the same SKU?
• Where are discounts deeper and more frequent?
• How consistent are prices across cities?

Without automated data extraction, answering these questions accurately was not possible.

SKU Overlap and Assortment Complexity

Although Reliance Retail and D-Mart sell many overlapping FMCG products, each retailer also carries exclusive SKUs, private labels, and region-specific assortments. Manual comparison could not capture this complexity at scale.

City-Level Variations

Prices and availability differ by city due to logistics, competition, and demand. Clients needed city-wise price comparison data, not just national averages.

No Unified Dataset

There was no single dataset that:

• Mapped identical SKUs across both retailers
• Tracked price changes over time
• Captured stock availability consistently

Comparing Reliance Retail and D-Mart data enables businesses to:

• Benchmark pricing competitiveness
• Identify underpriced or overpriced SKUs
• Track promotion and discount strategies
• Analyze private label penetration
• Improve channel strategy and negotiations

For FMCG brands, this data directly impacts pricing decisions, margin planning, and retailer relationships.

Food Data Scrape implemented a dual-source retail data scraping solution that extracts and standardizes product data from both Reliance Retail and D-Mart.

Key Objectives

• Scrape product listings, prices, and SKUs from both retailers
• Normalize identical products across platforms
• Track price, discount, and availability changes
• Enable city-wise and category-wise comparison
• Deliver analytics-ready datasets

Product-Level Data

• Product name
• Brand
• SKU / product ID
• Category and subcategory
• Pack size and unit
• Product image URL

Price-Level Data

• MRP
• Selling price
• Discount amount
• Discount percentage
• Offer type (flat, bundle, seasonal)

Availability Data

• In-stock / out-of-stock status
• City or store availability
• Delivery or pickup eligibility

Food Data Scrape designed a scalable architecture to support multi-retailer comparison.

Core Components

• Distributed scraping infrastructure
• Retailer-specific parsing logic
• SKU matching and normalization engine
• Price and availability validation
• API-based and file-based data delivery

This ensures consistent and comparable data across platforms.

The comparison system enables:

• Daily and intraday price tracking
• Detection of sudden price drops or hikes
• Promotion start and end tracking
• City-wise pricing anomalies

Clients can set alerts when price gaps cross defined thresholds.

Food Data Scrape delivers comparison data in:

• REST API (JSON)
• CSV files
• Excel spreadsheets
• Cloud-based delivery

Sample JSON Output (Simplified)

 
{
  "product": "Fortune Sunflower Oil 1L",
  "city": "Mumbai",
  "reliance_price": 165,
  "dmart_price": 158,
  "price_gap": -7,
  "lower_priced_retailer": "D-Mart",
  "timestamp": "2025-12-18T10:45:00"
}
                         

An FMCG brand used the comparison data to evaluate channel pricing.

Outcome

• Identified where D-Mart consistently undercut prices
• Adjusted wholesale pricing strategy
• Reduced channel conflict

Distributors tracked retailer pricing to understand margin pressure.

Outcome

• Better negotiation with retailers
• Improved inventory allocation
• Reduced low-margin exposure

Consulting firms used the dataset to analyze retail competitiveness.

Outcome

• Clear insights into discount-led vs everyday-low-price strategies
• Data-backed market reports for clients

The data revealed clear patterns:

• D-Mart had higher private label penetration in staples
• Reliance Retail offered broader branded SKU variety
• Price gaps were widest in high-volume essentials

Food Data Scrape enabled long-term tracking of:

• Monthly price trends
• Promotion frequency
• SKU churn (additions and removals)

This allowed clients to forecast pricing behavior during festivals and peak seasons.

Each dataset passes through:

• SKU duplication checks
• Price validation rules
• Category standardization
• Availability verification

This ensures enterprise-grade reliability.

The solution supports:

• Multiple cities and regions
• Thousands of SKUs per category
• High-frequency refresh cycles
• Multi-client integrations

Food Data Scrape follows responsible scraping practices:

• Only publicly visible data
• No consumer or personal data
• Client-aligned compliance standards

Primary keywords included:

• Reliance Retail vs D-Mart comparison
• Retail price comparison data scraping
• FMCG price intelligence API
• SKU price monitoring India
• Retail product data scraping API

The Reliance Retail vs D-Mart comparison delivered:

• Clear price leadership insights
• Better SKU and assortment planning
• Stronger pricing negotiations
• Reduced manual research effort
• Scalable retail intelligence

This case study demonstrates how Food Data Scrape enables deep, data-driven comparison between Reliance Retail and D-Mart using automated product data scraping APIs. In a highly competitive retail environment, access to accurate price and SKU intelligence is no longer optional. By transforming raw retailer listings into structured comparison datasets, Food Data Scrape empowers brands, distributors, and analysts to make faster, smarter, and more profitable retail decisions.