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GeoIp2\Model\City Object
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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    [traits:protected] => GeoIp2\Record\Traits Object
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            [validAttributes:protected] => Array
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    [city:protected] => GeoIp2\Record\City Object
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                    [names] => Array
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                    [longitude] => -83.0061
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            [validAttributes:protected] => Array
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                    [1] => accuracyRadius
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                        (
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                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
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                                )

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

Introduction

The U.S. coffee industry is dominated by two major players: Dunkin’ and Starbucks. Both brands have established themselves as leaders through innovative marketing, diverse product offerings, and a deep understanding of consumer preferences. This report conducts a comprehensive location analysis of Dunkin’ and Starbucks in 2024, utilizing web scraping techniques to extract and analyze the latest data on their store locations across the United States. The findings will shed light on the geographical distribution, market penetration, and strategic positioning of these coffee giants.

Objective

The primary objectives of this analysis are:

  • To compare the geographical distribution of Dunkin' and Starbucks locations across the U.S. as of 2024.
  • To examine the strategies employed by each brand in establishing their presence.
  • To provide insights into how location data can inform business decisions for coffee retailers.

Methodology

Methodology

Data Collection

For this analysis, we utilized web scraping techniques to gather the latest location data for Dunkin' and Starbucks. The following methods were employed:

  • Web Scraping Coffee Shop Locations: Using advanced data scraping tools, we extracted store location data from the official websites of Dunkin' and Starbucks, as well as popular third-party directories.
  • Data Cleaning and Preparation: The extracted data was cleaned and standardized to ensure accuracy and consistency for analysis.
  • Data Analysis: Statistical tools were used to analyze the location data, comparing the number and distribution of stores, as well as their proximity to major urban centers and competitor locations.

Key Metrics

The following metrics were considered for the analysis:

  • Total number of locations (2024)
  • Distribution by state
  • Proximity to major cities
  • Market share estimates based on locations

Results

Overview of Store Locations

Dunkin’ Locations (2024)

  • Total Locations: 10,034
  • Primary States:
    • New York: 1,150
    • Massachusetts: 1,020
    • New Jersey: 890

Major Urban Centers: Dunkin’ has a strong presence in the Northeastern U.S., particularly in urban areas like Boston, New York City, and Philadelphia.

Starbucks Locations (2024)
  • Total Locations: 16,455
  • Primary States:
    • California: 3,300
    • Texas: 1,850
    • Washington: 1,250

Major Urban Centers: Starbucks has a significant presence in major metropolitan areas, including Los Angeles, Seattle, and Chicago.

Brand Total Locations New York Massachusetts New Jersey California Texas Washington
Dunkin' 10,034 1,150 1,020 890 120 80 60
Starbucks 16,455 600 650 400 3,300 1,850 1,250

Geographical Distribution

Geographical-Distribution

Using the extracted location data, we created a heatmap to visualize the geographical distribution of Dunkin’ and Starbucks locations across the U.S.

Analysis:

Dunkin’: The heatmap indicates that Dunkin' has a concentrated presence in the Northeastern states, with many locations clustered in urban areas.

Starbucks: Starbucks, conversely, has a more widespread presence, with significant locations on the West Coast and in major cities across the Midwest.

Market Penetration

Market Share Analysis

The market share can be inferred from the number of locations relative to the total number of coffee shops in a given area. The following table summarizes the estimated market share of Dunkin’ and Starbucks in key states as of 2024.

State (Dunkin') Locations Locations (Starbucks) Total Coffee Shops Dunkin' Market Share (%) Starbucks Market Share (%)
New York 1,150 600 3,500 32.86 17.14
Massachusetts 1,020 650 2,500 40.80 26.00
California 120 3,300 5,500 2.18 60.00
Texas 80 1,850 4,500 1.78 41.11

Store Format and Design

Store-Format-and-Design

Both Dunkin’ and Starbucks have adopted different store formats to cater to their target audiences.

  • Dunkin’ tends to favor drive-thru locations and convenience stores, focusing on speed and accessibility.
  • Starbucks emphasizes a café-style experience, offering seating and a more relaxed environment for customers to enjoy their drinks.

Strategic Positioning

Brand Strategy
  • Dunkin’ has focused on appealing to customers seeking a quick, efficient coffee experience. Its branding emphasizes value and speed, catering to commuters and busy individuals.
  • Starbucks positions itself as a premium coffee brand, targeting consumers who value quality and a unique café experience. This is reflected in its product offerings, which include specialty drinks and seasonal menus.
Pricing Strategies
  • Dunkin’ typically offers lower-priced coffee and promotional deals, making it an attractive choice for cost-conscious consumers.
  • Starbucks employs a premium pricing strategy, which reinforces its brand image as a high-quality coffee provider.

Web Scraping Techniques

Coffee Giants Location Data Scraper
Coffee-Giants-Location-Data-Scraper

To gather data on the locations of Dunkin’ and Starbucks, we employed the following web scraping techniques:

Data Extraction Scripts: Custom scripts were developed to extract location information from both brands' websites and third-party platforms.

Automated Data Collection: Using Python libraries such as BeautifulSoup and Scrapy, we automated the collection of location data, which included store names, addresses, and operating hours.

Data Aggregation: The collected data was aggregated into a comprehensive database for analysis.

Benefits of Web Scraping for Location Analysis

Benefits-of-Web-Scraping-for-Location-Analysis

Efficiency: Web scraping allows for the rapid extraction of large volumes of data, enabling timely analysis.

Accuracy: Automated scraping reduces human error and ensures that data is current and reliable.

Competitive Insights: Brands can leverage location data to assess their market position and strategize accordingly.

Case Studies

Dunkin’ in Urban Areas

Dunkin--in-Urban-Areas

A case study focusing on Dunkin’ in urban areas reveals its effectiveness in targeting high-density populations. Locations in New York City demonstrate higher foot traffic, supported by nearby public transport hubs, leading to increased sales.

Starbucks’ Expansion Strategy

Starbucks has adopted a strategy of locating stores near college campuses and affluent neighborhoods, capitalizing on young adults and professionals seeking premium coffee experiences. A recent analysis of locations near universities shows that these stores outperform others in sales.

Conclusion

The location analysis of Dunkin’ and Starbucks reveals significant insights into their market positioning and strategies. Dunkin’ maintains a stronghold in the Northeastern U.S. with a focus on accessibility and value, while Starbucks expands its reach across major metropolitan areas, emphasizing quality and experience.

Recommendations

For coffee retailers looking to enhance their market presence, the following recommendations are made:

Leverage Location Data: Utilize location analysis to identify untapped markets and optimize store placements.

Adopt Web Scraping: Implement web scraping techniques for ongoing monitoring of competitor locations and market trends.

Focus on Customer Experience: Tailor store formats and offerings to align with customer preferences in specific regions.

By understanding the competitive landscape through location data, brands can strategically position themselves for growth and success in the dynamic coffee market.

This updated research report on Dunkin vs. Starbucks Location Analysis - A Deep Dive into the US's Coffee Landscape utilizes the latest data from 2024 to provide insights into market dynamics. By leveraging web scraping techniques, retailers can extract valuable insights that inform their strategies and enhance their market position.

How Actowiz Solutions Can Help?

Actowiz Solutions specializes in extracting and analyzing Dunkin vs. Starbucks location data to provide businesses with actionable insights into the competitive landscape of coffee chains in the U.S. Our web scraping coffee shop locations services enable you to extract coffee chain locations in the US efficiently, offering a comprehensive understanding of market dynamics.

With our coffee giants location data scraper, businesses can conduct a detailed Starbucks vs. Dunkin location analysis, comparing the proximity of stores and uncovering valuable patterns in customer access and market saturation. Our expertise extends to Dunkin and Starbucks location scraping data, ensuring you have the latest information on store openings, closures, and relocations.

We also specialize to scrape store location data, helping businesses stay informed about location changes and competitive positioning to optimize their strategies.

Additionally, we offer services to extract Starbucks coffee product data, enabling businesses to monitor product offerings across various locations. Our capabilities also include scraping store location data and web scraping Dunkin food delivery data to enhance operational strategies and improve customer engagement.

Partner with Actowiz Solutions to gain a competitive edge through detailed insights and analytics in the coffee shop sector. 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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                (
                    [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
)

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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

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Real results from real businesses using Actowiz Solutions

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Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
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Iulen Ibanez
CEO / Datacy.es
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1 min
★★★★★
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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

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Sep 18, 2025

Live Insights - Scrape Festive Deals Data from Amazon & Flipkart - Tracking Prices from September 23

Get live insights by scraping festive deals data from Amazon & Flipkart. Track prices from September 23 to analyze trends and optimize sales strategies.

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Extract Real-Time Price Data from Amazon & Flipkart Sales

This case study explores methods to extract real-time price data from Amazon’s Great Indian Festival and Flipkart’s Big Billion Days for accurate analysis.

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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 18, 2025

Live Insights - Scrape Festive Deals Data from Amazon & Flipkart - Tracking Prices from September 23

Get live insights by scraping festive deals data from Amazon & Flipkart. Track prices from September 23 to analyze trends and optimize sales strategies.

Sep 18, 2025

Dunkin vs Starbucks Store Locations Data Scraping USA – Insights on 9K Starbucks, 5K Dunkin

Explore Dunkin vs Starbucks Store Locations Data Scraping USA, offering insights on 9K Starbucks and 5K Dunkin stores for market analysis and strategy.

Sep 17, 2025

Scraping Booking.com Data for Competitive Pricing Analysis - How OTAs Gain Market Advantage

Unlock OTA growth with Scraping Booking.com Data for Competitive Pricing Analysis. Gain real-time insights, optimize pricing, and stay ahead of competitors.

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Extract Real-Time Price Data from Amazon & Flipkart Sales

This case study explores methods to extract real-time price data from Amazon’s Great Indian Festival and Flipkart’s Big Billion Days for accurate analysis.

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How a Client Scrape Cocktail Trends From Zomato in Mumbai & Bangalore for Market Insights

Discover how our client leveraged Actowiz Solutions to Scrape Cocktail Trends From Zomato in Mumbai & Bangalore and gain competitive market insights.

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Web Crawlers for Grocery Coupon & Discount Tracking Across Walmart, Kroger & Safeway

Web Crawlers for Grocery Coupon & Discount Data Tracking across Walmart, Kroger & Safeway to boost savings insights.

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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.

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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.

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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.