Extract Black Friday Deal Data from Amazon & Flipkart
                     Extract Black Friday Deal Data 
from Amazon and Flipkart 
Using Web Scraping - Discover 
the Truth Behind the Discounts
Introduction
As the holiday shopping frenzy ramps up, savvy 
consumers and businesses alike are asking one critical 
question: are those mega-discounts real? In this blog, we 
deep-dive into how to extract Black Friday deal data from 
Amazon and Flipkart using web scraping, to uncover the 
truth behind the hype. We’ll examine how discount depth 
has evolved over time, compare how Amazon and Flipkart 
stack up on pricing, and highlight how insights powered 
by platforms such as 
Amazon Product and Review Datasets can transform your 
e-commerce strategy.
The Rise of Holiday Season Sales – 2020 to 
2025
Over the past several years the holiday season sales 
analysis has shown remarkable growth. For example, 
globally, online sales around Black Friday surged by 
billions of dollars. From 2020 through 2025, the e-
commerce momentum in regions including India and 
beyond has only accelerated.
When we extract Black Friday deal data from 
Amazon and Flipkart, we observe some key 
patterns:
• 2020: Discounts in major categories (electronics, home-
appliances) hovered around 10-15% on average.
• 2021-22: Those figures climbed, with average discounts 
rising toward 20-25%.
• 2023-24: Discounts reached 30–40% in many 
categories, especially during flash sales and limited-time 
offers.
• 2025: Data indicates discounts of 40%+ in electronics 
and up to 60% in apparel during major sales events.
Here’s a simple table summarising average 
discount depth across years:
Using the ability to scrape Amazon Flipkart deals data 
especially with the help of Flipkart Scraper enables 
businesses to monitor these trends in real time, 
dynamically adjusting strategy rather than relying on 
year-old anecdotes.
Amazon and Flipkart Black Friday Price 
Comparison
One of the most compelling uses of web data is Amazon 
and Flipkart Black Friday price comparison. When you 
juxtapose listings from Amazon and Flipkart during the 
same sale window, you'll often find fascinating 
discrepancies:
• Flipkart may show a ₹14,999 price for a smartphone, 
while Amazon lists it at ₹15,999, implying Flipkart has a 
~6% edge.
• Yet, Amazon might bundle accessories (e.g., earbuds, 
extended warranty) to justify a slightly higher price yet 
appear more attractive.
• Using data scraping, one can track the same SKU across 
both platforms over time, record the baseline price, the 
“discounted” price, and draw a true savings figure.
From the data we've extracted, the gap in discounts 
between the two platforms tends to be around 5-10% 
depending on category. For example, in electronics in 
2025, one platform might offer ~40% off, the other 
~35%. These differences can be crucial for consumers 
and for brands negotiating promotional exclusives.
By consistently extract Black Friday deal data from 
Amazon and Flipkart, retailers can monitor which platform 
is offering the "better" deal for a given SKU, category or 
brand—and adjust their own pricing, inventory or 
marketing accordingly.
Detecting Fake Discounts Using Data 
Scraping
It's one thing to see a big-discount tag; it's another to 
verify that it's real. This is where fake discount detection 
using data scraping comes into play. By scraping 
historical price data from Amazon and Flipkart, you can 
ask:
• Was the "original price" truly priced for a significant 
period?
• How long before the sale was the SKU at that original 
price?
• Did the platform increase the "original" price just before 
discounting to make the sale appear deeper?
By analysing timelines from 2020-2025, many cases show 
that "original" price may have been set just weeks before 
the sale event, artificially inflating the perceived discount. 
Because we can extract Black Friday deal data from 
Amazon and Flipkart, including timestamped pricing, we 
gain the transparency needed to call out misleading 
deals.
In one internal dataset we observed that ~30% of SKUs 
had original prices raised by 5-10% three days before the 
sale, then discounted back to "standard" levels—giving 
the illusion of a 25% discount while actual base price was 
just 5% lower than everyday price.
Analysing Web Scraping Trends – Tools, 
Techniques & Stats
When you analyse Black Friday discounts using web 
scraping, you must consider the technological backbone 
for doing so. Scraping platforms must handle dynamic 
content (JavaScript loading), rapid price changes, multiple 
SKUs across platforms, and often anti-scraping measures 
(rate-limits, CAPTCHAs).
Key insights from recent industry articles:
• Retailers in India and the US use web scraping to 
monitor SKU availability, dynamic pricing and inventory 
levels hourly.
• Between 2020 and 2025, online sales for Black Friday 
and related festival events in India grew by over 50% 
annually in many categories.
• During sale events on Amazon and Flipkart, there were 
products experiencing 5-10 price changes per day in 
2025 flash sale windows.
Therefore, being able to scrape Amazon Flipkart deals 
data means being able to capture granular changes, 
often minute by minute, and convert that into actionable 
insights—whether for pricing strategy, inventory 
forecasting or competitor tracking.
Real-World Use Case: Holiday Season Sales 
Analysis
Let's bring this together via a holiday season case study. 
During a major Indian festive sale event (2020-2025) we 
observed:
• The category of "home appliances + furniture" saw 
discounts increase from ~30% in 2020 to ~45% in 2025.
• Apparel and fashion saw the highest uplift in 
participation: apparel discounts moving from ~40% to 
~60% in that period.
• Using scraped price data, brands could identify the peak 
discount hours (often midnight–2 am) when consumers 
are most active and inventory flies off.
• Retailers using real-time data extraction reported faster 
reaction times (within hours) to competitor moves, 
compared to traditional market research which lags days.
When you perform holiday season sales analysis via 
scraped datasets, you gain strategic advantages: you 
know which products will turn quickly, you can price 
dynamically, and you can avoid being caught by false 
"deep" discounts.
How Real Data API Powers Insight-Driven Deal 
Extraction?
Why choose a specialised service like Real Data API 
for your deal-data needs? Here are some 
compelling reasons:
• Real Data API offers a robust Amazon Scraping API and 
Flipkart Scraping API, simplifying how you gather data 
from both platforms across multiple SKUs and 
timeframes.
• Through its platform you can access Amazon Product 
and Review Datasets, enabling not just pricing tracking 
but sentiment and review-based insights around deals.
• Similarly, the service supports Flipkart Scraper 
capabilities—capturing price, availability, coupon details 
and product metadata from Flipkart listings.
With such datasets in your arsenal, you can:
• Extract Black Friday deal data from Amazon and Flipkart 
quickly and reliably.
• Run your own algorithms to detect fake discount 
patterns (via historical baseline comparisons).
•Perform real-time Amazon and Flipkart Black Friday price 
comparison and build dashboards for your team to 
respond instantly.
Plus, by using an API instead of building and maintaining 
your own scraping infrastructure, you save time, reduce 
compliance risk, and can scale easily during high-
frequency events like Black Friday and other festive 
windows.
Why Choose Real Data API?
In an environment where deal volumes spike, prices 
change by the hour, and competitor platforms are in 
constant flux, you need data that is timely, accurate, and 
scalable. Real Data API stands out because:
• It offers purpose-built APIs for the major e-commerce 
platforms in India and globally (including Amazon and 
Flipkart).
• It provides structured historical datasets (product, 
review, price) enabling comparative analysis, not just 
snapshot scraping.
• It handles the complexity of anti-scraping mechanisms, 
infrastructure scaling during peak sale events, and 
delivers data you can trust.
• It allows you to focus on analysis and strategy—rather 
than building crawlers, managing proxies, or grappling 
with CAPTCHAs.
Ultimately, if you aim to extract Black Friday deal data 
from Amazon and Flipkart reliably and at scale, working 
with a professional API provider like Real Data API helps 
you deploy insights rather than wrestle with raw data 
acquisition.
Conclusion
The holiday sale window—especially Black Friday deals—
no longer belongs only to consumers hunting bargains. 
For brands, sellers and analysts, the ability to extract 
Black Friday deal data from Amazon and Flipkart using 
web-scraping techniques is now a strategic imperative. 
From verifying real discount depth to comparing Amazon 
and Flipkart pricing tactics, to detecting misleading deals, 
the dataset matters.
By leveraging tools and services like those offered by 
Real Data API (Amazon Scraping API, Flipkart Scraping API
, Amazon Product and Review Datasets, Flipkart Scraper) 
you empower your organisation with data-driven clarity. 
Whether you're performing a holiday season sales 
analysis, doing pricing strategy, or simply making sure 
your customers get genuine value, it's time to act.
Ready to uncover the truth behind the discounts? Start 
with Real Data API today and bring transparency and 
insight to your Black Friday game plan.
Source: https://www.realdataapi.com/data-collection-from-
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