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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] => 216.73.216.141 [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] => 216.73.216.141 [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 )
McDonald’s store and pricing data drives expansion into new U.S. cities, leveraging 15K+ locations for data-driven growth strategies.
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In 2025, McDonald’s continues to dominate the U.S. fast food market, operating over 15,000 locations nationwide. Leveraging McDonald’s Location Data, the brand seeks to identify new cities for expansion while optimizing pricing strategies for maximum profitability. McDonald’s store and pricing data plays a critical role in this process, providing insights into competitor pricing, regional demand patterns, and menu performance across various markets. By analyzing these datasets, McDonald’s can make informed decisions about where to open new outlets and how to adjust pricing to suit local market conditions. Actowiz Solutions partnered with McDonald’s to provide advanced analytics and data scraping capabilities, enabling the company to streamline its expansion strategy and ensure each new location is strategically positioned for long-term success. This approach combines geographic intelligence with pricing intelligence to maintain McDonald’s competitive edge in a saturated fast-food market.
McDonald’s, one of the world’s largest fast-food chains, has an ambitious U.S. growth plan for 2025, aiming to expand into untapped suburban and urban markets. The company required accurate McDonald’s Food Delivery Data and sales information to make data-driven expansion decisions. Their goal was to leverage McDonald’s store and pricing data to identify cities with high revenue potential while avoiding market saturation. McDonald’s also sought insights into customer preferences, competitor pricing strategies, and regional economic trends. Partnering with Actowiz Solutions allowed them to access structured datasets, including historical sales data, location performance metrics, and competitive pricing intelligence. By integrating this information, McDonald’s could optimize its restaurant location strategy in the U.S., ensuring that every new outlet aligns with broader business objectives and achieves maximum profitability while maintaining brand standards.
One of the main challenges was collecting accurate, real-time data across thousands of U.S. locations. With more than 15,000 stores, analyzing individual store performance, competitor pricing, and local market trends was complex and time-consuming. McDonald’s also needed to evaluate fast food pricing analysis for expansion decisions to ensure profitability in new cities, balancing affordability with operational costs. Another challenge involved integrating multiple data sources, such as delivery trends, customer reviews, and regional demographics, to form a complete picture of potential markets. Ensuring data accuracy and consistency was critical, especially when considering location-specific pricing adjustments. Additionally, McDonald’s sought to minimize risk in new market entries by understanding competitors’ pricing and promotional strategies, which required sophisticated U.S. fast food market analysis and continuous monitoring of real-time changes. The company required a reliable partner to provide both historical insights and actionable forecasts to support its McDonald’s expansion strategy.
Actowiz Solutions implemented a comprehensive data-driven approach using Restaurant Location Data Scraping to capture detailed metrics from thousands of existing McDonald’s locations. This included sales volumes, menu performance, competitor pricing, and customer engagement trends. Leveraging McDonald’s store and pricing data, Actowiz created predictive models to identify high-potential cities for expansion and determine optimal pricing strategies tailored to each local market. Advanced Online Restaurant Data Scraping Services enabled the aggregation of competitor pricing and promotions, providing valuable insights into regional pricing trends. Actowiz also employed location-based pricing intelligence to fine-tune menu pricing based on local demographics and economic conditions. By combining geographic, operational, and pricing data, Actowiz provided actionable data insights that supported McDonald’s market entry strategy and expansion planning. The result was a data-backed roadmap guiding McDonald’s to expand strategically, optimize pricing, and enhance revenue in new U.S. cities.
"Actowiz Solutions transformed our approach to expansion. Their data-driven insights allowed us to strategically select cities and optimize pricing for maximum profitability. The team’s expertise in McDonald’s store and pricing data helped us confidently enter new markets and achieve measurable growth."
— Director of U.S. Expansion, McDonald’s Corporation
Through Actowiz Solutions’ advanced data scraping and analytics capabilities, McDonald’s was able to leverage McDonald’s store and pricing data to drive expansion into new U.S. cities with precision. By combining store and pricing data analysis with location intelligence, McDonald’s gained actionable insights into market potential, competitor pricing, and regional consumer behavior. This comprehensive approach allowed the brand to minimize risk, optimize pricing, and strategically grow its presence across the U.S. The partnership with Actowiz Solutions not only streamlined data collection and analysis but also enabled McDonald’s to maintain a competitive edge in the fast-food market. Businesses looking to expand efficiently and make informed pricing decisions can emulate this data-driven strategy for sustainable growth.
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
Real Estate
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×
Organic Grocery / FMCG
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
Quick Commerce
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
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
Beverage / D2C
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
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
Real results from real businesses using Actowiz Solutions
In Stock₹524
Price Drop + 12 minin 6 hrs across Lel.6
Price Drop −12 thr
Improved inventoryvisibility & planning
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
"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"
✔ Scraped Data, SKU availability, delivery time
With hourly price monitoring, we aligned promotions with competitors, drove 17%
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