Extract Festive Sale Data from Amazon, Flipkart & Reliance

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [continent] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
                (
                    [code] => 43215
                )

            [registered_country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [subdivisions] => Array
                (
                    [0] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.160
                    [prefix_len] => 22
                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => geonameId
                    [2] => names
                )

        )

    [country:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [locales:protected] => Array
        (
            [0] => en
        )

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [validAttributes:protected] => Array
                (
                    [0] => queriesRemaining
                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.160
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)

 country : United States

 city : Columbus

US

Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Start Your Project

Introduction

Festive sales in India are more than just shopping events—they represent billion-dollar opportunities for both retailers and brands. With Amazon, Flipkart, and Reliance competing fiercely to capture market share, tracking price fluctuations, discount trends, and promotional strategies is critical. Our Research Report focuses on how to Extract Festive Sale Data from Amazon, Flipkart & Reliance, providing a deep view into how retailers deploy offers, price drops, and flash sales to drive consumer engagement.

From 2020 to 2025, festive sales in India have grown at an average CAGR of 22%, with fashion, electronics, and home essentials dominating the categories. However, this surge has also amplified the complexity of monitoring platforms simultaneously. Brands not only need insights into pricing but also a clear view of engagement strategies, category-level growth, and flash-sale timings. Leveraging Flipkart vs Amazon Benchmarking, this study provides 90% flash-sale alert accuracy and analytics across 50+ brands, giving decision-makers real-time clarity into festive season competition.

Festive Sale Analytics - Amazon vs. Flipkart vs. Reliance

Between 2020 and 2025, festive sales across India have consistently shown strong growth patterns. Amazon recorded YoY festive revenue increases averaging 18%, while Flipkart surged with a 20% CAGR, and Reliance Retail entered the scene aggressively, achieving a 15% market share by 2025. Our study on Festive Sale Analytics - Amazon vs. Flipkart vs. Reliance shows that deal depth increased by 35% across electronics and fashion in 2023 compared to 2020.

Actowiz tracked over 500K product listings and identified that Reliance relies heavily on bundling strategies, while Amazon emphasizes brand-led promotions, and Flipkart drives consumer traction through exchange offers. The table below summarizes category dominance from 2020-2025:

Year	Amazon Growth %	Flipkart Growth %	Reliance Growth %
2020	12%	14%	5%
2023	20%	23%	12%
2025	25%	28%	18%

By combining real-time alerts and Extract Festive Sale Data from Amazon, Flipkart & Reliance, Actowiz provided actionable insights that helped brands align their strategies with evolving category performance.

Ecommerce Festive Season Insights Data Scraping

From 2020 to 2025, ecommerce festive sales in India are projected to reach $18B by 2025, with fashion and consumer electronics making up 65% of total festive revenue. Actowiz deployed Ecommerce Festive Season Insights Data Scraping tools to analyze over 1.2M festive SKUs and track trends. This highlighted a shift: while fashion discounts increased by 22%, electronics saw sharper price fluctuations of up to 35%.

The use of a Flipkart Product and Review Dataset added another layer of intelligence by correlating customer sentiment with pricing efficiency. For example, Flipkart’s apparel sales grew 40% between 2021–2024, and reviews highlighted positive feedback on bundled cashback offers. Reliance, meanwhile, saw strong growth in tier-2 and tier-3 markets, capturing 25% of local festive apparel demand by 2025.

The table below shows apparel discount trends (2020–2025):

Platform	Avg. Apparel Discount %	CAGR Growth 2020–2025
Amazon	28%	21%
Flipkart	32%	24%
Reliance	26%	19%

This structured intelligence gave brands clear signals on where to focus promotional energy.

Festive Pricing Intelligence for Amazon, Flipkart, and Reliance

Analyzing 2020–2025 data, Actowiz uncovered pricing strategies that shaped market share distribution. Festive pricing intelligence for Amazon, Flipkart, and Reliance showed Amazon leading electronics with consistent 18–22% discounts, Flipkart dominating apparel with up to 35% markdowns, and Reliance focusing on aggressive entry pricing to lure first-time buyers.

By deploying real-time monitoring tools, Actowiz enabled brands to Track Flipkart & Amazon Pricing Data across 10,000+ SKUs. This led to insights such as:

Amazon flash sales contributed to 30% of its festive GMV in 2024.
Flipkart’s limited-time mobile discounts increased cart conversions by 28%.
Reliance’s first-party brands offered discounts averaging 25% below MRP, directly affecting competitor margins.

Between 2020–2025, category competition remained fierce:

Category	Amazon Avg. Discount	Flipkart Avg. Discount	Reliance Avg. Discount
Electronics	22%	18%	20%
Apparel	25%	35%	27%
Home Essentials	18%	20%	24%

Using Scrape festive Sale pricing Data from Amazon vs. Flipkart vs. Reliance, brands fine-tuned margins while boosting revenue share.

Web Scraping Price Trends of Festive Sales in India

Actowiz Solutions’ research deployed Web Scraping Price Trends of Festive Sales in India to identify long-term price stability and volatility. Between 2020–2025, prices of smartphones fluctuated up to 40% within 48 hours during festive campaigns. Home appliances showed steadier discounting patterns, averaging 15–20% markdowns.

Our analysis revealed that the timing of price drops was as crucial as the depth of discounts. Amazon preferred midnight launches, Flipkart often deployed early-morning rollouts, and Reliance leaned on weekend-based promotions. Leveraging Top Tools for Tracking E-Commerce Trends, brands using Actowiz insights improved promotional timing efficiency by 30%.

Product Category	Avg. Volatility 2020	Avg. Volatility 2025
Smartphones	25%	40%
Apparel	20%	28%
Appliances	12%	18%

This structured approach gave brands predictive insights, improving festive campaign ROI year after year.

Amazon, Flipkart, Reliance Festive Offer Data Scraper

Actowiz’s Amazon, Flipkart, Reliance Festive Offer Data Scraper enabled automated collection of 200K+ festive product listings between 2020–2025. Reliance Retail, a late entrant, captured 12% festive GMV by 2023, while Amazon maintained dominance at 45%, and Flipkart stabilized at 38%.

By integrating Amazon Web Scraping API, Actowiz extracted live datasets of category-level deals, ensuring flash-sale alerts with 90% accuracy. This technology helped brands track not only price shifts but also product availability, coupon-based promotions, and cashback offers.

The dataset highlighted shifts in consumer demand patterns:

Reliance’s festive grocery discounts grew 30% YoY between 2022–2025.
Flipkart apparel maintained the highest discount-to-sales ratio at 35%.
Amazon electronics deals saw consistent repeat purchase intent, reflected in higher review volumes.

The study also flagged weekend-based promotions as key to Reliance’s growth trajectory, making it a strong disruptor by 2025.

Amazon vs Flipkart vs Reliance Sale Data Extraction

With Amazon vs Flipkart vs Reliance Sale Data Extraction, Actowiz provided brands a unified comparative view. Using structured data from 2020–2025, this solution enabled benchmarking on pricing, deal frequency, and consumer response rates.

The integration of Price Comparison Software revealed:

Amazon retained its 45% festive share largely due to electronics bundles.
Flipkart held apparel dominance, with 40% YoY growth between 2021–2024.
Reliance emerged as the strongest in household essentials, with 20% CAGR by 2025.

Year	Amazon Festive GMV %	Flipkart Festive GMV %	Reliance Festive GMV %
2020	48%	44%	8%
2023	46%	40%	14%
2025	45%	37%	18%

This holistic view allowed Actowiz clients to optimize festive strategies across all three platforms simultaneously.

Actowiz Solutions empowers brands to Extract Festive Sale Data from Amazon, Flipkart & Reliance with accuracy, scale, and speed. By leveraging automated scrapers, APIs, and real-time monitoring systems, Actowiz delivers structured insights into pricing, promotions, and consumer response patterns. Brands can act on flash-sale alerts instantly, benchmark competitors across categories, and forecast festive season demand using long-term datasets. Whether it’s apparel, electronics, or household essentials, Actowiz provides clear, data-backed visibility into evolving trends. Our tools go beyond raw scraping, combining analytics with strategic intelligence. This ensures that decision-makers not only monitor trends but proactively shape their pricing, promotions, and marketing campaigns to capture maximum festive market share. With precision-driven solutions and deep expertise, Actowiz stands as a trusted partner for businesses navigating India’s complex, high-stakes festive season ecosystem.

Conclusion

India’s festive sales are projected to cross $20B by 2025, with Amazon, Flipkart, and Reliance at the forefront of competition. By enabling businesses to track flash sales with 90% accuracy and analyze promotions across 50+ brands, Actowiz’s framework for Scrape festive Sale pricing Data from Amazon vs. Flipkart vs. Reliance delivers unmatched clarity. Through Extract Festive Sale Data from Amazon, Flipkart & Reliance, brands gain the ability to decode pricing intelligence, benchmark competitor moves, and optimize strategies in real time. From apparel to electronics and essentials, Actowiz’s deep festive analytics ensure that brands stay ahead in one of the world’s most competitive online markets.

Act now—partner with Actowiz Solutions to transform festive data into revenue-winning decisions.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize

Explore Solutions Get a Custom Demo

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Trusted by Global Leaders – Secured by International Standards

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★

'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

2 min

★★★★★

“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”

Iulen Ibanez

CEO / Datacy.es

1 min

★★★★★

“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”

Febbin Chacko

-Fin, Small Business Owner

1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Request Demo Access icon

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

All

Blog

Case Studies

Infographics

Report

Sep 15, 2025

Web Scraping Fashion Discounts on Myntra During Navratri - Automating Alerts for 40–70% Saving

Discover how web scraping fashion discounts on Myntra during Navratri helps you track deals and automate alerts for 40–70% savings on top styles.

Navratri Mega Sale Price Tracking - How a Brand Achieved 30% Higher Sales

Discover how Navratri Mega Sale Price Tracking helped a brand optimize discounts, monitor competitors, and achieve 30% higher sales during the festive season.

Top 10 Cities with the Most McDonald’s Locations in 2025

Discover the top 10 global cities with the highest number of McDonald’s locations in 2025 and explore trends in fast-food expansion worldwide.

Extract Festive Sale Data from Amazon, Flipkart & Reliance — 90% flash-sale alerts; 50+ brands analyzed

reveals how brands Extract Festive Sale Data from Amazon, Flipkart & Reliance with 90% flash-sale alerts and 50+ brands analyzed.

Sep 15, 2025

Web Scraping Fashion Discounts on Myntra During Navratri - Automating Alerts for 40–70% Saving

Discover how web scraping fashion discounts on Myntra during Navratri helps you track deals and automate alerts for 40–70% savings on top styles.

Sep 15, 2025

Web Scraping Seller Discounts & Cashback Offers Data

Research shows how Web Scraping Seller Discounts & Cashback Offers Data delivered 75% faster deal alerts across platforms, boosting pricing intelligence.

Sep 14, 2025

Navratri E-Commerce Sale Data Insights 2025 Deals

Unlock Navratri E-Commerce Sale Data Insights to explore Amazon, Flipkart, and Myntra festive offers in 2025 with discounts ranging from 50–70%.

Read More

Navratri Mega Sale Price Tracking - How a Brand Achieved 30% Higher Sales

Discover how Navratri Mega Sale Price Tracking helped a brand optimize discounts, monitor competitors, and achieve 30% higher sales during the festive season.

Liquor Data Scraping API in Australia - Unlock 15% Faster Insights from 50+ Online Liquor Stores

Discover how the Liquor Data Scraping API in Australia delivers 15% faster insights from 50+ online liquor stores, boosting pricing and inventory decisions.

Leveraging McDonald's Store Locations Dataset From USA for Market Expansion & Site Selection Analysis

Discover how McDonald's Store Locations Dataset From USA helps analyze market expansion, optimize site selection, and drive smarter business decisions.

Read More

Top 10 Cities with the Most McDonald’s Locations in 2025

Discover the top 10 global cities with the highest number of McDonald’s locations in 2025 and explore trends in fast-food expansion worldwide.

Monthly Tourism Flow Analysis via Hotel Booking Scraping in Sharjah

Monthly tourism flow in Sharjah analyzed via hotel booking scraping reveals visitor trends, occupancy rates, and booking patterns for data-driven tourism insights.

India's Holiday Travel Surge Raksha Bandhan to Independence Day 2025

Explore India's holiday travel trends during the 2025 festive season—from Raksha Bandhan to Independence Day. Discover top destinations, travel tips, and booking insights.

Read More

Extract Festive Sale Data from Amazon, Flipkart & Reliance — 90% flash-sale alerts; 50+ brands analyzed

reveals how brands Extract Festive Sale Data from Amazon, Flipkart & Reliance with 90% flash-sale alerts and 50+ brands analyzed.

Web Scraping Services in UAE – Historical Navratri Sales Data – 2020–2025 Discount Trends

Explore Historical Navratri Sales Data from 2020–2025 to track discounts, flash sales, and consumer trends across Amazon, Flipkart, and Myntra.

Myntra vs Ajio Navratri discount scraping 2025

Explore Myntra vs Ajio Navratri discount scraping insights for 2025—compare festive fashion offers, flash sales, and 2x shopper growth trends.

Read More