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Weekly E-commerce Price Comparison in Amazon India - Trends & Insights-01

Introduction

In 2025, McDonald’s continues to dominate the fast-food industry, with over 15,000 outlets across the United States. Businesses and analysts are increasingly leveraging McDonald’s Restaurant Analytics to understand market trends, consumer behavior, and expansion strategies. From historical growth patterns to projected new locations, this data is crucial for investors, franchisees, and competitors. Using McDonald's data scraping services, companies can extract accurate, real-time information about store openings, closures, and performance metrics across regions.

Analyzing McDonald’s U.S. Locations 2025 allows businesses to benchmark their operations, identify underserved markets, and plan strategic expansion. The growing demand for data-driven insights makes Restaurant Data Scraping essential for capturing McDonald’s location data, sales figures, and operational metrics efficiently. Whether it’s understanding regional performance differences or monitoring competitor strategies, comprehensive analytics provides a clear picture of the fast-food landscape.

This blog dives deep into McDonald’s Restaurant Analytics, covering historical growth, state-wise distribution, location analysis, and future expansion trends, helping stakeholders make informed decisions in 2025 and beyond.

McDonald’s Store Count in 2025 – Historical and Projected Trends

The growth of McDonald’s in the United States has been remarkable, and tracking the McDonald’s store count in 2025 provides critical insights for businesses, franchisees, and market analysts. From 2020 to 2025, McDonald’s has consistently expanded, reflecting both strategic planning and adaptive market strategies. In 2020, there were approximately 14,200 U.S. locations. By 2025, this number exceeds 15,200 outlets, representing a total growth of around 7% over five years. This expansion has been driven by urban densification, suburban growth, and targeted market penetration.

Year U.S. McDonald’s Locations Annual Growth Rate
2020 14,200 -
2021 14,400 1.41%
2022 14,650 1.74%
2023 14,850 1.36%
2024 15,000 1.01%
2025 15,200+ 1.33%

The steady growth indicates that McDonald’s is balancing expansion with market saturation concerns. While urban areas offer high foot traffic, these locations are highly competitive, requiring a strategic approach to site selection. Suburban areas, on the other hand, provide untapped potential, particularly in regions experiencing population growth or rising disposable incomes.

The historical and projected McDonald’s U.S. locations data also reveal the effectiveness of franchise models. Approximately 60% of McDonald’s outlets are franchise-owned, while the remaining 40% are corporate-owned. This distribution allows rapid growth while maintaining quality control through standardized operational guidelines. The franchise system also enables local adaptation, giving franchisees the flexibility to implement region-specific menu items or promotional strategies.

Using McDonald's data scraping services, analysts can track openings and closures in near real-time, providing valuable insights into market dynamics. For example, data from 2023 indicated a spike in new openings in Texas and Florida, correlating with demographic growth in suburban regions. These insights allow businesses to anticipate trends and make informed decisions about investments or competitive positioning.

The forecast for 2025 shows continued expansion into high-potential regions without cannibalizing existing stores. By integrating McDonald’s Restaurant Analytics with demographic, economic, and geographic data, stakeholders can not only monitor growth trends but also evaluate which areas offer the best ROI. This proactive approach ensures that McDonald’s continues to expand efficiently while maintaining profitability across its vast U.S. network.

McDonald’s Restaurant Dataset 2025 – Key Metrics and Insights

The McDonald’s Restaurant Dataset 2025 is a crucial resource for understanding operational efficiency, sales performance, and market positioning across the U.S. With over 15,000 locations, the dataset captures metrics such as average monthly sales, employee numbers, franchise vs. corporate ownership, and regional distribution. Companies can use Restaurant Data Scraping to collect this information efficiently, providing near real-time access to store-level insights that would be challenging to obtain manually.

From 2020 to 2025, average sales per McDonald’s outlet increased steadily, reflecting improvements in menu offerings, digital ordering platforms, and delivery partnerships. For instance, the average monthly sales per outlet rose from $55,000 in 2020 to $65,000 in 2025, a growth rate of 18%. Franchise-owned outlets slightly outperform corporate-owned locations in revenue generation due to localized marketing and community engagement strategies.

Metric 2020 2021 2022 2023 2024 2025
Avg. Monthly Sales ($K) 55 57 59 61 63 65
Corporate vs Franchise % 40/60 40/60 40/60 40/60 40/60 40/60
Avg. Employees per Store 25 26 27 28 28 29

The dataset also reveals operational metrics such as staffing efficiency, drive-thru performance, and peak hour management. For example, restaurants in high-density urban areas report longer wait times but higher sales volumes, whereas suburban locations often maintain shorter service times with moderate revenue levels. These insights allow operators to optimize staffing and operational processes to maximize profitability.

McDonald’s restaurant growth trends in 2025 also indicate targeted expansion in underserved markets. Using data analytics, McDonald’s identifies areas with high population growth, increased disposable income, and limited fast-food competition. By applying predictive analytics, franchisees and corporate decision-makers can project revenue potential for new outlets and minimize risk.

Integrating the McDonald’s Restaurant Dataset 2025 with consumer feedback, location intelligence, and competitive analysis provides a holistic view of performance. Businesses can benchmark against national averages, identify underperforming stores, and implement strategies to enhance customer satisfaction, drive traffic, and improve revenue across all U.S. locations.

Unlock powerful insights with McDonald’s Restaurant Dataset 2025 – optimize locations, boost revenue, and make data-driven growth decisions today!
Contact Us Today!

McDonald’s Location Data for Site Selection

Strategic site selection is fundamental to the success of any McDonald’s outlet. Utilizing McDonald’s Location Data for Site Selection, businesses can evaluate demographic trends, traffic patterns, competitor density, and consumer preferences to identify high-potential locations. This process ensures new outlets are positioned for optimal visibility, accessibility, and profitability.

Between 2020 and 2025, McDonald’s expanded aggressively in suburban regions experiencing population growth. Heatmap analysis shows that states like Texas, Florida, and California have become key focus areas due to urban sprawl and economic development. Approximately 70% of new store openings are in these regions, while 30% target smaller cities and underserved neighborhoods.

State New Openings 2020-2025 Total Locations
California 200 1,225
Texas 180 1,212
Florida 150 882
New York 90 800
Illinois 50 638

The McDonald’s location analysis across the United States shows that outlets near transportation hubs, schools, and commercial centers achieve higher footfall. Suburban locations near highways often perform well in drive-thru sales, while urban downtown outlets see higher dine-in traffic. This geographic intelligence enables McDonald’s to optimize each store’s format and operational focus.

Advanced analytics also support site selection for future expansion by combining historical performance data with projected demographic trends. Predictive models help franchisees identify regions likely to see increased consumer spending, urban migration, and population growth, reducing the risk of underperforming outlets.

Additionally, integrating Ratings & Reviews Analytics with location data provides insights into customer satisfaction for each potential site. Feedback trends can indicate local preferences and expectations, enabling McDonald’s to tailor menu offerings and service strategies for maximum acceptance and revenue potential.

Ratings & Reviews Analytics

Customer sentiment and feedback play a crucial role in evaluating McDonald’s performance and informing strategic decisions. By leveraging Ratings & Reviews Analytics, McDonald’s can gain insights into customer satisfaction, operational efficiency, and menu preferences across its 15K+ U.S. locations in 2025. Collecting, analyzing, and acting on this feedback ensures that each outlet maintains high service standards while identifying areas for improvement.

Between 2020 and 2025, customer ratings across U.S. McDonald’s locations averaged 4.2 stars out of 5, with slight regional variations. Urban locations tend to receive higher ratings for convenience and accessibility, while suburban stores score higher in service friendliness and consistency. Negative feedback often highlights wait times during peak hours, order accuracy issues, or menu availability concerns.

Region Avg. Customer Rating Common Positive Feedback Common Negative Feedback
West 4.3 Quick service, clean outlets Limited menu variety
East 4.1 Friendly staff, quality food Drive-thru congestion
Midwest 4.2 Efficient operations Order errors during rush hours
South 4.0 Value promotions Inconsistent service speed

Integrating McDonald’s Restaurant Analytics with review data enables targeted improvements. For example, outlets with lower drive-thru ratings may implement staff retraining programs or invest in queue management technology. Meanwhile, stores receiving consistent praise for cleanliness or order accuracy can use these metrics to reinforce operational best practices.

Moreover, analyzing ratings trends helps forecast McDonald’s restaurant growth trends in 2025. Areas with higher average ratings tend to have stronger revenue growth and lower churn rates. This data is especially valuable when evaluating potential expansion locations, as new outlets can be designed to address known pain points and replicate proven operational strengths.

Ratings & Reviews Analytics also helps McDonald’s respond proactively to market shifts. During seasonal promotions or new menu rollouts, sentiment monitoring can identify customer reactions in real-time, allowing rapid adjustments to staffing, inventory, or menu offerings. By connecting this analysis to McDonald’s Location Data, the brand ensures localized, actionable insights for each outlet, enhancing both customer experience and operational efficiency.

McDonald’s Expansion Across the U.S. in 2025

The McDonald’s Expansion Across the U.S. in 2025 reflects strategic planning informed by demographic trends, competitive analysis, and consumer demand. Using Retailer Intelligence Services, McDonald’s identifies areas for new openings, evaluates potential competition, and determines optimal store formats to maximize revenue.

From 2020 to 2025, McDonald’s has opened approximately 2,200 new locations, with net growth of about 300 stores per year after accounting for closures. Urban centers remain critical due to high foot traffic, but suburban and small-town expansions are increasingly prioritized.

Year New Openings Closures Net Growth
2020 350 100 250
2021 360 110 250
2022 380 120 260
2023 400 130 270
2024 420 120 300
2025 430 130 300

By leveraging McDonald’s Location Analysis Across the United States, the company identifies high-potential markets while avoiding oversaturation. Texas, California, and Florida continue to be expansion hubs due to population density and economic growth. Meanwhile, Midwest and Southern states provide opportunities for growth in underserved markets.

Expansion strategies also consider consumer behavior and trends, such as increased demand for drive-thru efficiency, mobile ordering, and menu personalization. Integrating McDonald’s Restaurant Analytics with predictive modeling enables McDonald’s to forecast revenue potential and operational performance for new outlets before committing to real estate investments.

The combination of Retailer Intelligence Services and historical performance data allows McDonald’s to optimize its rollout schedule, ensuring consistent brand standards and profitability. Each new location is evaluated for accessibility, demographic alignment, and proximity to competitors, which minimizes risk and maximizes ROI.

Drive success with McDonald’s Expansion Across the U.S. in 2025 – identify growth markets, optimize site selection, and maximize revenue efficiently!
Contact Us Today!

State-Wise Distribution of McDonald’s Restaurants in 2025

Understanding the State-Wise Distribution of McDonald’s Restaurants in 2025 provides valuable insights for franchisees, competitors, and analysts. McDonald’s strategically distributes its 15,200+ U.S. locations to maximize market coverage while maintaining operational efficiency. States like California, Texas, and Florida lead in total outlets due to high population density and economic activity.

State Number of Outlets Growth Rate 2020-2025
California 1,225 6%
Texas 1,212 5%
Florida 882 4%
New York 800 3%
Illinois 638 2%

Regional variations reveal opportunities for new store openings. The Northeast has a high density of outlets but slower growth due to saturation, whereas the South and Midwest are prime targets for expansion. Utilizing McDonald’s Location Data, decision-makers can pinpoint underserved cities, evaluate competitive presence, and anticipate revenue potential.

Historical growth analysis shows that between 2020 and 2025, California added 200+ locations, while Texas added 180. These states continue to demonstrate robust consumer demand and support McDonald’s expansion strategy. Suburban markets near major cities have become a primary focus, combining moderate competition with strong local demand.

Integrating Number of McDonald’s Outlets Across the United States with demographic data helps identify regions with rising populations, higher disposable incomes, and potential for long-term revenue growth. This ensures that new locations are not only profitable but also sustainable in the long term.

State-wise analytics also supports marketing and operational decisions. High-density states may benefit from targeted digital campaigns, loyalty programs, or menu adaptations, while lower-density regions require strategies that maximize brand awareness and customer acquisition efficiently.

Overall, analyzing McDonald’s Expansion Across the U.S. in 2025 alongside state-wise distribution enables informed decision-making for franchisees, corporate planners, and investors. This ensures that McDonald’s continues to grow strategically while maintaining strong market performance nationwide.

How Actowiz Solutions Can Help?

At Actowiz Solutions, we empower businesses with advanced McDonald’s Restaurant Analytics and actionable insights. Using tools like Restaurant Data Scraping, Ratings & Reviews Analytics, and Retailer Intelligence Services, we help clients:

  • Access accurate McDonald’s Restaurant Dataset 2025 for strategic decisions.
  • Evaluate McDonald’s U.S. Locations 2025 for expansion and investment planning.
  • Perform state-wise and regional location analysis for site optimization.
  • Monitor customer sentiment to improve service and engagement.

Our solutions provide real-time, data-driven intelligence, enabling investors, franchisees, and market analysts to make informed decisions and capitalize on McDonald’s ongoing growth trends.

Conclusion

McDonald’s Restaurant Analytics 2025 reveals over 15,000 U.S. locations, steady growth trends, and strategic expansion opportunities. From historical and projected McDonald’s U.S. locations to state-wise distribution and ratings & reviews insights, data-driven decisions are critical for success in the competitive fast-food sector.

By leveraging McDonald’s Location Data for Site Selection and intelligent analytics, stakeholders can identify high-potential regions, optimize operations, and maximize ROI. Actowiz Solutions equips businesses with cutting-edge McDonald’s data scraping services and analytics tools to stay ahead of trends and make smarter expansion choices.

Ready to harness the power of McDonald’s Restaurant Analytics 2025? Partner with Actowiz Solutions today and transform insights into actionable growth! You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!

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            [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] => 216.73.216.188
                    [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

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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.
Product Image
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
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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

Actowiz Insights Hub

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

All
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Case Studies
Infographics
Report
Sep 2, 2025

Ecommerce Growth 45% Faster with Price Intelligence vs Price Monitoring Strategies – Let’s See How?

Discover how ecommerce brands grow 45% faster using price intelligence vs price monitoring, boosting profits, competitiveness & smart pricing.

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How Instacart & Amazon Fresh Data Helped LA Retailers Boost Revenue by 25%

Discover how retailers in Los Angeles & San Francisco leveraged Instacart and Amazon Fresh data scraping for pricing, inventory, and customer insights to boost revenue by 25%.

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Competitive Intelligence 2025 - QSR Brands Use McDonald’s Competitive Intelligence Data Across 40K+ Locations

Explore how QSR brands leverage McDonald’s competitive intelligence data across 40K+ locations in 2025 to optimize menus, pricing, and boost revenue.

Sep 2, 2025

Ecommerce Growth 45% Faster with Price Intelligence vs Price Monitoring Strategies – Let’s See How?

Discover how ecommerce brands grow 45% faster using price intelligence vs price monitoring, boosting profits, competitiveness & smart pricing.

Sep 1, 2025

Scrape Maggiano’s Little Italy Location Data to Optimize Restaurant Marketing Strategies

Learn how to Scrape Maggiano’s Little Italy location data to gain insights, optimize restaurant marketing strategies, and improve local business performance.

Aug 31, 2025

McDonald’s Restaurant Analytics 2025 - 15K+ U.S. Locations, Growth & Expansion Insights

Explore McDonald’s Restaurant Analytics 2025 with 15K+ U.S. locations. Get detailed insights on growth, expansion, and industry trends for fast food.

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How Instacart & Amazon Fresh Data Helped LA Retailers Boost Revenue by 25%

Discover how retailers in Los Angeles & San Francisco leveraged Instacart and Amazon Fresh data scraping for pricing, inventory, and customer insights to boost revenue by 25%.

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Quick Commerce in Texas – Competitive Grocery & E-Commerce Intelligence in Dallas & Houston

Discover how Dallas & Houston retailers used real-time grocery data from Walmart, Instacart, and Uber Eats with Actowiz Solutions to grow revenue by 22%.

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NYC Quick Commerce Growth with Real-Time Grocery Data from Walmart & Uber Eats

Learn how New York City retailers used real-time data scraping from Walmart and Uber Eats to optimize pricing, stock, and promotions, fueling quick commerce growth.

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Competitive Intelligence 2025 - QSR Brands Use McDonald’s Competitive Intelligence Data Across 40K+ Locations

Explore how QSR brands leverage McDonald’s competitive intelligence data across 40K+ locations in 2025 to optimize menus, pricing, and boost revenue.

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Regional Cruise Demand Analysis with CruiseOnly Data - Comparing U.S., Europe, and Asia Trends

Explore regional cruise demand with CruiseOnly data—compare U.S., Europe, and Asia trends, passenger growth, and seasonal booking patterns for 2025.

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Product Matching with Web Scraping – Achieving 92% Accuracy Across 50+ Global Retail Platforms

Discover how Product Matching with Web Scraping achieved 92% accuracy across 50+ global retail platforms, enabling precise SKU alignment and pricing insights.