scrape supplier and product data from IndiaMART

1. Unlock Pricing and Supplier Patterns Across IndiaMART — scrape supplier and product data from IndiaMART on 2 Million+ Listings

2. Introduction IndiaMART, one of India's largest B2B marketplaces, connects millions of suppliers and businesses across a variety of product categories. For organizations looking to optimize procurement, pricing strategies, and supplier selection, it's crucial to scrape supplier and product data from IndiaMART to uncover actionable insights. Using IndiaMART data scraping for B2B market insights, extract real-time product pricing, and automate large-scale data collection. In this blog, we explore how scrape supplier and product data from IndiaMART enables deep marketplace intelligence, track trends from 2020–2025, and help decision-makers gain a competitive edge. IndiaMART Scraper, businesses can conduct

3. IndiaMART Marketplace Growth (2020–2025) IndiaMART has witnessed remarkable growth over the past five years, with both supplier and product listings increasing exponentially. By product data extraction, we analyzed trends and summarized key metrics below: leveraging IndiaMART This surge demonstrates why scrape supplier and product data from IndiaMART is essential for businesses seeking real insights rather than relying on high-level numbers from the marketplace dashboard. Key observation: Electronics, industrial machinery, and consumer goods dominate listings, accounting for over 45% of total products. This is a critical insight for buyers and analysts looking to focus their sourcing and price benchmarking.

4. Extracting Real-Time Product Pricing from IndiaMART Understanding price fluctuations in a B2B marketplace is critical for competitive sourcing. With extracting real-time product pricing from IndiaMART, businesses can: • Track product price changes across categories and regions. • Monitor supplier discounts and promotional strategies. • Identify products with the most competitive pricing. From 2020 to 2025, the electronics category saw an average annual price increase of 4.2–5.5%, whereas industrial tools increased steadily at around 4% per year. By leveraging scrape supplier and product data from IndiaMART, companies can access live pricing data, allowing procurement teams to make timely and informed decisions.

5. For instance, a mid-sized manufacturer sourcing raw materials can monitor price trends across multiple suppliers and negotiate better deals by identifying which suppliers consistently offer lower rates. Automating B2B Data Collection from IndiaMART Manual monitoring of millions of listings is inefficient and prone to errors. Automating B2B data collection from IndiaMART enables: • Continuous, real-time data collection from multiple categories. • Trend analysis for pricing, product demand, and supplier activity. • Centralized dashboards dynamics. Using IndiaMart Scraping API, businesses can schedule recurring scrapes to track seasonal shifts in demand, monitor stock availability, and identify suppliers entering or leaving the marketplace. to visualize supply-side

6. Example: A distributor analyzing industrial tools observed that new suppliers increased by 18% between 2023 and 2024, signaling emerging competition in this category. Automation ensures businesses stay ahead of such changes without manually checking listings daily. Web Scraping IndiaMART Data for Pricing and Trend Analysis Web scraping IndiaMART data for pricing and trend analysis provides actionable insights for pricing strategy and market positioning. Key benefits include: • Competitive Benchmarking: Compare prices across suppliers and categories to negotiate better deals. • Market Trend Detection: Identify emerging products or categories gaining traction. • Inventory Planning: Track stock trends to anticipate shortages or demand spikes.

7. For example, in the E-Commerce Dataset of electronics between 2020–2025, products such as LED lighting, consumer electronics, and IoT devices saw consistent growth in supplier listings, increasing from 250,000 to 450,000 suppliers. By monitoring such trends, buyers can anticipate product shortages and plan procurement accordingly. Insights from IndiaMART Scraper API The IndiaMART scraper API provides structured access to millions of listings with granular detail, including: • Supplier profiles and ratings. • Product descriptions, categories, and pricing. • Stock levels and regional distribution.

8. With IndiaMART Scraper, businesses can generate E- Commerce Datasets that reveal supplier trends, pricing fluctuations, and product popularity. Using these datasets, companies can feed information into AI models or predictive tools for demand forecasting and market analysis. Example: A B2B marketplace analyst found that home appliances listings grew 55% from 2020 to 2025, with supplier numbers increasing 45%. Such insights are invaluable for strategic sourcing and sales planning. E-Commerce Dataset Analysis (2020–2025) Analyzing the E-Commerce Commerce Data Scraping API reveals key supplier and product trends: Dataset through E- Insights: • Electronics and consumer goods show high growth in listings and suppliers, indicating intense competition. • Industrial tools have steady pricing, making supplier selection critical. • Home appliances demonstrate strong regional demand patterns, suggesting localized sourcing strategies. By using scrape supplier and product data from IndiaMART, businesses gain actionable insights into pricing, product availability, and competitive landscapes across multiple categories.

9. Why Choose Real Data API? Real Data API is the ultimate solution for businesses seeking to scrape supplier and product data from IndiaMART at scale. Key benefits: •Reliable and Real-Time Data: Access millions of listings without delays or errors. •Automation & Scalability: Schedule recurring scrapes for continuous market intelligence. •Structured Data Outputs: JSON, CSV, or database-ready formats for immediate analysis. •Historical Insights: Track trends from 2020–2025 for strategic decision-making. •Seamless Integration: Integrate with internal BI tools, dashboards, or AI models. With Real Data API, companies can leverage IndiaMART data scraping for B2B market insights, identify emerging suppliers, track competitive procurement strategies. pricing, and enhance

10. Conclusion IndiaMART's massive marketplace offers a wealth of insights for businesses willing to dig deeper. By leveraging Real Data API to scrape supplier and product data from IndiaMART, organizations can extract real-time pricing, monitor supplier activity, and uncover trends across categories and regions. Ready to unlock actionable B2B marketplace insights? Start using Real Data API today to automate IndiaMART product data extraction, monitor supplier and product trends, and make data-driven sourcing and pricing decisions. Source: https://www.realdataapi.com/scrape-supplier- product-data-from-indiamart.php

15. 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%.

16. • 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 discrepancies: often find fascinating

17. • 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.

18. 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?

19. 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

20. 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 forecasting or competitor tracking. strategy, inventory

21. 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 participation: apparel discounts moving from ~40% to ~60% in that period. and fashion saw the highest uplift in • 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.

22. 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 timeframes. multiple SKUs and • 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, capabilities—capturing price, availability, coupon details and product metadata from Flipkart listings. the service supports Flipkart Scraper 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).

23. •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.

24. 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- issa-show-north-america.php