Scraped 1 Million Product Listings — Indian Brand Secrets Exposed

Author : John Bennet | Published On : 22 May 2026

EXCLUSIVE DATA INTELLIGENCE REPORT · 1 MILLION LISTINGS · 30 DAYS · 42 CATEGORIES

We Scraped 1 Million Product Listings — Here's What the Data Reveals About Indian E-Commerce

The most comprehensive Indian e-commerce listing intelligence report compiled from scraped data — revealing price inflation patterns, stock availability discrepancies, rating inconsistencies, and fee structures across Amazon, Flipkart, Myntra, Nykaa, Blinkit, Zepto & JioMart.

IMPORTANT DISCLAIMER: All findings and statistics presented in this report are based solely on publicly available product listing data collected from the platforms mentioned. This report does not allege intentional wrongdoing, fraud, or misconduct by any specific brand, seller, or platform. All patterns and observations are presented as data-derived findings for informational and market intelligence purposes only. Product Data Scrape makes no representations regarding the legal compliance or non-compliance of any brand's pricing practices. Readers should conduct independent verification before drawing any conclusions. This report should not be construed as legal advice.

1M+

Product listings scraped

42

Categories analysed over 30 days

68%

Listings with pre-sale price adjustments

11.4%

Unfulfillable stock listings detected

Product Data Scrape deployed its data scraping infrastructure across Amazon India, Flipkart, Myntra, Nykaa, Blinkit, Zepto, and JioMart for 30 consecutive days. One million product listings. Forty-two categories. Data captured from hundreds of brands — from emerging D2C companies to established FMCG names. This report presents what the scraped data shows, objectively and without attribution to any individual brand.

This is the most comprehensive Indian e-commerce listing intelligence analysis compiled from scraped publicly available data. Every statistic in this report is derived from our direct data collection infrastructure. Each listing capture included title, description, listed price, MRP, effective checkout price, rating, review count, stock availability status, seller identity, fulfilment type, and full listing history — providing a complete, timestamped dataset of marketplace listing behaviour across seven platforms over 30 days.

PLATFORMS COVERED

5 SIGNIFICANT PATTERNS DETECTED IN THE DATA

1. Pattern Detected: Pre-Sale Price Adjustments

Our scrapers tracked 1,20,000+ listings across Amazon and Flipkart for 30 days before and during major sale events. The data shows that 68% of products displaying significant sale discounts during Big Billion Day and Great Indian Festival periods had their listed reference prices adjusted upward by an average of Rs.340 in the 21 days preceding the sale window. This pattern was observed consistently across the electronics and fashion categories. Consumers and brands monitoring price history data may find this pattern useful when evaluating advertised discount depth.

Pattern observed in: Electronics & Fashion · Avg. reference price shift: Rs.340/SKU

2. Pattern Detected: Cross-Platform Price Variations on Identical SKUs

The same product — verified by matching barcode data, brand, model, and packaging specifications — was observed listed at prices varying by up to 34% across Amazon, Flipkart, Blinkit, and Nykaa on the same day. Our cross-platform price scraping identified 2,80,000 instances of identical SKUs carrying different prices across platforms simultaneously. This finding highlights the importance of cross-platform price comparison tools for both consumers and competing brands seeking accurate market pricing benchmarks.

Pattern observed in: FMCG & Personal Care · Max observed price variation: 34%

3. Pattern Detected: Rating Inconsistencies Across Near-Duplicate Listings

The data revealed 14,200 instances of near-duplicate listings — products with highly similar titles, images, and specifications listed under different listing IDs by the same brand — where review distribution varied significantly across the duplicates. In the personal care and electronics categories, primary listings showed average ratings 0.6 to 0.8 stars higher than the aggregate rating when all near-duplicate listing reviews were consolidated. This observation is presented as a data pattern and does not attribute intentional conduct to any brand.

Pattern observed in: Personal Care & Electronics · Rating variance: 0.6–0.8 stars

4. Pattern Detected: Stock Availability Signal Discrepancies

11.4% of all listings scraped across quick commerce and marketplace platforms displayed an in-stock availability status but could not be fulfilled when order placement was tested. These stock signal discrepancies were observed most frequently in quick commerce platforms. The data suggests a gap between displayed availability status and actual fulfilment readiness for a measurable portion of active listings — a signal relevant to brands monitoring competitor availability accuracy and search visibility patterns.

Pattern observed in: Quick Commerce platforms · Discrepancy rate: 11.4% of all listings

5. Pattern Detected: Effective Checkout Price Divergence from Listed Price

Platform fees, handling charges, convenience fees, and minimum order thresholds varied by up to Rs.89 per order across platforms for comparable basket values. These additional charges appear at checkout and are not reflected in listing-level price displays. Our end-to-end price scraping — which captures the effective checkout price rather than just the listed price — found that the lowest listed price corresponded to the lowest effective delivered price in only 61% of cases across the dataset.

Pattern observed in: All categories · Listed price = lowest delivered price: only 61% of cases<< /p>

DATA SUMMARY — KEY PATTERNS AT A GLANCE

WHAT THESE PATTERNS MEAN FOR YOUR BRAND

The patterns identified in this dataset have practical implications for any brand operating across Indian e-commerce platforms. When reference price adjustments ahead of sale events are widespread across a category, consumer price anchoring shifts — affecting how your own pricing and discount depth is perceived, regardless of your brand's individual practices. When cross-platform price variation is common in your category, consumers who multi-platform shop may develop different price expectations that affect conversion rates on any single platform.

Similarly, when stock availability signal discrepancies are common in a category, platform search algorithms that factor in availability signals may rank listings inconsistently — creating search visibility outcomes that do not purely reflect genuine stock depth. Product Data Scrape's listing intelligence platform tracks all five of these data patterns in real time across every major Indian marketplace — enabling brands to monitor category-level trends, benchmark their own listing performance, and respond to market shifts backed by objective scraped data.

Understanding these marketplace-wide data patterns is not about attributing specific conduct to any competitor. It is about giving your brand the objective, data-verified intelligence needed to make pricing, inventory, and listing strategy decisions that are grounded in what the market actually shows — not what competitors choose to report.

INTELLIGENCE CAPABILITIES — WHAT PRODUCT DATA SCRAPE TRACKS

PRE-SALE PRICE MONITORING

Track reference price changes 21 days early

Product Data Scrape's historical price tracking database captures daily listing price changes — giving brands visibility into reference price movement patterns in their category up to 21 days before sale events. Use this data to benchmark your own pricing decisions against observed market patterns.

CROSS-PLATFORM PRICE INTELLIGENCE

Monitor your SKUs across all platforms simultaneously

Our cross-platform SKU matching engine tracks every listing of your brand's products and competitor products across Amazon, Flipkart, Nykaa, Blinkit, Zepto, and JioMart simultaneously — identifying price variation patterns and MAP discrepancy instances in real time with structured data output.

STOCK AVAILABILITY MONITORING

Detect fulfilment discrepancy patterns

Product Data Scrape's availability monitoring goes beyond scraping the in-stock label. We track fulfilment signal consistency for monitored SKUs — flagging listings where availability status and actual order fulfilment diverge, providing brands with accurate competitor stock intelligence.

LISTING & RATING INTELLIGENCE

Track near-duplicate listings and review distribution

Our listing analysis engine identifies near-duplicate product listings across platforms — tracking review distribution patterns and rating consistency to give your brand an accurate competitive rating benchmark based on comprehensive listing data rather than primary listing data alone.

DATA COLLECTION METHODOLOGY

Over 30 days, Product Data Scrape's scraping infrastructure extracted publicly available listing data from Amazon India, Flipkart, Myntra, Nykaa, Blinkit, Zepto, and JioMart across 42 product categories. Each listing capture included title, description, price, MRP, effective checkout price, rating, review count, stock status, seller identity, fulfilment type, and listing history. Data was captured at 4-hour intervals during standard hours and at 1-hour intervals during identified sale event windows.

Our cross-platform SKU matching engine reconciled identical products across different platforms using a combination of barcode data, product title normalisation, image hash comparison, and brand-model string matching — achieving a 94.7% accurate cross-platform match rate across the full dataset. All data collected is publicly available listing data accessible to any user of these platforms. No proprietary, private, or confidential data was accessed in the preparation of this report.

All findings are presented as observed data patterns. This report does not attribute specific conduct to any named brand, seller, or platform. Product Data Scrape's listing intelligence datasets are available via structured API output for brands, market research firms, and analysts conducting marketplace benchmarking and competitive intelligence research.

Source : https://www.productdatascrape.com/scraped-1-million-product-listings-indian-brand-secrets.php

Originally published at https://www.productdatascrape.com

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