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GeoIp2\Model\City Object
(
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        (
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                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [zh-CN] => 美国
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            [postal] => Array
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                    [code] => 43215
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            [registered_country] => Array
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                    [iso_code] => US
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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            [traits] => Array
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                    [ip_address] => 216.73.216.213
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    [continent:protected] => GeoIp2\Record\Continent Object
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
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                            [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
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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [2] => isInEuropeanUnion
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                    [4] => names
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    [traits:protected] => GeoIp2\Record\Traits Object
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                    [ip_address] => 216.73.216.213
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [2] => connectionType
                    [3] => domain
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                    [8] => isHostingProvider
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                    [10] => isp
                    [11] => isPublicProxy
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [19] => staticIpScore
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    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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    [location:protected] => GeoIp2\Record\Location Object
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                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
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            [validAttributes:protected] => Array
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                    [1] => accuracyRadius
                    [2] => latitude
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                    [4] => metroCode
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                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

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

            [validAttributes:protected] => Array
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        )

    [subdivisions:protected] => Array
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            [0] => GeoIp2\Record\Subdivision Object
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                    [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
                                )

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                    [validAttributes:protected] => Array
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)
 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
)

Introduction

The quick commerce sector in India has seen explosive growth from 2020 to 2025, fueled by urbanization, increasing smartphone penetration, and the demand for instant delivery of groceries and essentials. Platforms like Zepto, Blinkit, and Swiggy Instamart are at the forefront of this revolution, offering 10–30-minute delivery services in metropolitan areas. Understanding platform-specific pricing strategies and market dynamics is essential for businesses, investors, and analysts seeking a competitive edge.

Analyzing Quick Commerce Price Dynamics in India allows stakeholders to evaluate how pricing decisions influence consumer behavior, track competitive trends, and optimize revenue strategies. This involves monitoring discounts, promotional campaigns, and dynamic pricing adjustments.

By implementing Dynamic Price Monitoring for Zepto, Blinkit, and Swiggy, companies can respond to market shifts in real time, forecast demand accurately, and design optimized pricing models that enhance profitability while maintaining customer loyalty. This report details findings from 2020-2025, highlighting trends, competitor pricing comparisons, and actionable insights.

Scrape Zepto, Blinkit, and Swiggy Instamart Product Prices

Automated scraping enables accurate, large-scale price tracking across thousands of SKUs. Scrape Zepto, Blinkit, and Swiggy Instamart Product Prices allows businesses to capture real-time price variations, promotional discounts, and availability data, essential for dynamic pricing strategies.

Year Zepto Listings (K) Blinkit Listings (K) Swiggy Instamart Listings (K) Avg. Price Fluctuation (%)
2020 50 45 40 3.5%
2021 70 65 60 4%
2022 90 85 80 4.5%
2023 110 100 95 5%
2024 130 120 115 5.2%
2025 150 140 135 5.5%
Key Insights:
  • Zepto's listings grew 3x from 2020 to 2025, reflecting aggressive expansion in urban markets.
  • Blinkit maintained stable growth with bundle deals and subscription-based pricing models.
  • Swiggy Instamart focused on high-demand SKUs with dynamic discounts tied to time-of-day demand.

Automated extraction ensures accuracy and scalability, allowing retailers to benchmark products, monitor competitor activity, and optimize revenue strategies.

Zepto vs Blinkit vs Swiggy Instamart Price Comparison

Comparative analysis provides insights into how each platform positions itself in the market. Zepto vs Blinkit vs Swiggy Instamart Price Comparison reveals category-specific pricing patterns and discounting strategies.

Category Avg. Zepto Price (₹) Avg. Blinkit Price (₹) Avg. Swiggy Instamart Price (₹)
Dairy Products 120 115 118
Beverages 85 80 83
Snacks & Packaged 95 90 92
Household Items 150 145 148
Analysis:
  • Zepto consistently offers micro-discounts to drive frequent purchases.
  • Blinkit focuses on bundle offers and free delivery thresholds to attract repeat customers.
  • Swiggy Instamart combines price reductions with loyalty-based promotions and limited-time offers.

This pricing behavior impacts customer choice and retention, highlighting the importance of real-time competitor monitoring.

Compare Zepto vs Blinkit Pricing via Scraping in India

Compare Zepto vs Blinkit Pricing via Scraping in India captures high-frequency price updates across categories multiple times per day, allowing businesses to quickly respond to competitor strategies.

Year Price Updates per Day Avg. Discount (%) SKUs Monitored (K)
2020 2 3 50
2021 3 4 70
2022 4 4.5 90
2023 5 5 110
2024 6 5.2 130
2025 7 5.5 150
Insights:
  • Price volatility increased over time due to intensified competition.
  • Scraping enables instant notifications for price drops, flash deals, and stock updates.
  • Data-driven monitoring helps retailers adjust pricing, stock levels, and promotions in real-time.

Grocery Price Dynamics Extraction from Indian Platforms

Grocery Price Dynamics Extraction from Indian Platforms allows businesses to analyze trends across product categories, including perishable and non-perishable goods.

Category Avg. Yearly Price Change (%) Zepto Blinkit Swiggy Instamart
Milk & Dairy 3.5 3.2 3.3
Fruits & Veg 4.0 3.8 3.9
Packaged Goods 4.2 4.0 4.1
Beverages 3.8 3.5 3.7
Trends:
  • Dynamic pricing has become more frequent between 2023-2025 due to demand surges and urban delivery logistics.
  • Real-time monitoring enables businesses to anticipate stock-outs and optimize pricing based on consumer demand.
  • Historical analysis helps design category-specific strategies for maximum ROI.

Zepto Grocery Data Scraping

Zepto Grocery Data Scraping enables businesses to extract SKUs, prices, discounts, stock levels, and promotional campaigns efficiently.

Year SKUs Scraped (K) Avg. Discount (%) Price Accuracy (%)
2020 50 3.5 92%
2021 70 4 93%
2022 90 4.2 94%
2023 110 4.5 95%
2024 130 5 96%
2025 150 5.2 97%
Benefits:
  • Ensures accurate and updated product and pricing information.
  • Helps businesses track competitor promotions and launch responsive marketing campaigns.
  • Enables forecasting of demand and adjustment of inventory in real time.

Blinkit & Swiggy Instamart Data Scraping and Dynamic Pricing

Blinkit Data Scraping Services and Swiggy Instamart Data Scraping provide structured datasets for pricing, stock levels, promotions, and SKU availability. Dynamic Pricing allows these platforms to adjust prices multiple times per day based on demand, supply, and competitor activity.

Year Blinkit SKUs Scraped (K) Swiggy SKUs Scraped (K) Avg. Dynamic Price Changes per Week
2020 45 40 2
2021 65 60 3
2022 85 80 4
2023 100 95 5
2024 120 115 6
2025 140 135 7

Dynamic monitoring ensures businesses can maintain competitive pricing, reduce losses, and enhance customer satisfaction.

Actowiz Solutions provides Quick Commerce Data Scraping Services using Web Scraping Servicesto monitor Zepto, Blinkit, and Swiggy Instamart. Our scraping pipelines collect large-scale product, price, and promotion data with high accuracy. This allows clients to implement data-driven strategies, optimize inventory allocation, and respond to real-time competitor price changes.

Conclusion

Analyzing Quick Commerce Price Dynamics in India is vital for businesses operating in the fast-paced grocery delivery market. Zepto, Blinkit, and Swiggy Instamart use dynamic pricing, frequent promotions, and time-sensitive offers to attract and retain customers. By leveraging Zepto Grocery Data Scraping, Blinkit Data Scraping Services, and Swiggy Instamart Data Scraping, companies can monitor prices, optimize revenue, and make strategic decisions in real time.

Start leveraging Analyzing Quick Commerce Price Dynamics in India with Actowiz Solutions to unlock actionable insights, stay competitive, and maximize profitability in India’s rapidly evolving quick commerce market.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

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.

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 Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
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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.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
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!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
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

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

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