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                            [iso_code] => OH
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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

Coffee is deeply embedded in American culture, with Starbucks standing out as a leading figure in the coffee shop market.

This report examines Starbucks store distribution data across the United States, providing insights into the company's store location strategy, presence in key markets, and trends in urban retail expansion.

Starbucks Store Distribution Overview

As of March 10, 2025, Starbucks operates 17,015 stores across the United States.

This extensive retail footprint mapping reflects the brand's strong market penetration and accessibility, catering to millions of coffee lovers daily.

Starbucks Store Distribution by State

State	Number of Stores	% of Total Stores
California	3,170	18.6%
Texas	1,785	10.5%
Florida	1,450	8.5%
New York	1,320	7.8%
Illinois	860	5.1%
Other States	8,430	49.5%

This Starbucks store distribution data highlights California as the leading market, with nearly one in five Starbucks locations situated in the state.

Drive-Thru Location Insights

A key trend in coffee shop market analysis is the rise of drive-thru location insights, catering to convenience-driven customers.

In 2025, over 65% of Starbucks locations now include drive-thru services, particularly in suburban and high-traffic highway areas.

This shift reflects Starbucks’ focus on retail footprint mapping, ensuring accessibility for busy commuters and on-the-go consumers.

Starbucks Drive-Thru Growth Over the Years

Year	% of Starbucks Stores with Drive-Thru	Total Drive-Thru Locations
2020	55%	9,500
2023	60%	10,800
2025	65%	11,900

This increase in drive-thru locations aligns with Starbucks' store location strategy, adapting to consumer preferences for faster and more convenient service.

Drive-Thru vs. Non-Drive-Thru Performance

Store Type	Average Daily Customers	Average Revenue Per Store
Drive-Thru	800	$7,200
Non-Drive-Thru	550	$5,300

With drive-thru stores generating 35% more revenue, Starbucks is strategically expanding these locations in suburban areas, reflecting urban retail expansion trends.

By leveraging Starbucks store distribution data and Point-of-Interest (POI) data analytics, businesses can analyze market shifts, optimize their retail footprint mapping, and stay competitive in the evolving coffee industry.

Starbucks Location by State

An analysis of Starbucks store distribution data across various states reveals a strategic store location strategy tailored to population density, economic activity, and consumer demand.

Top 10 States by Starbucks Store Count (2025)

State	Number of Stores	% of Total Starbucks Locations	Population per Store
California	3,170	18.6%	12,500
Texas	1,785	10.5%	16,200
Florida	1,450	8.5%	15,000
New York	1,320	7.8%	14,800
Illinois	860	5.1%	18,400
Washington	810	4.7%	10,200
Pennsylvania	780	4.6%	17,100
Ohio	750	4.4%	16,900
Georgia	725	4.3%	15,800
North Carolina	700	4.1%	17,500

Key Insights from Coffee Shop Market Analysis

• California Leads the Market: With 3,170 stores, California accounts for 18.6% of all Starbucks locations in the U.S., benefiting from high population density and a strong coffee culture.
• Texas and New York as Major Markets: Both states have over 1,300 Starbucks locations, reinforcing their importance in tech city retail presence and economic activity.
• Washington’s Unique Presence: As Starbucks' birthplace, Washington has the highest concentration per capita, with one store for every 10,200 people.

Starbucks Expansion and Retail Data Scraping Services

Starbucks' urban retail expansion trends focus on growing markets with high foot traffic.

Leveraging retail data scraping services helps businesses track store location strategy, consumer preferences, and competitive positioning.

With Starbucks' continued investment in tech city retail presence, their retail footprint mapping remains a key factor in market leadership.

Presence in Top Coffee Cities

Examining Starbucks' retail location intelligence in major coffee-centric cities highlights its strategic positioning in urban hubs with high consumer demand.

Starbucks Store Count in Major Coffee Cities (2025)

City	Number of Stores	Stores per 100,000 People	% with Drive-Thru
Seattle, WA	90	11.5	40%
New York, NY	240	2.8	15%
Los Angeles, CA	185	4.5	35%
Chicago, IL	150	5.6	38%
San Francisco, CA	105	6.2	30%
Portland, OR	72	8.9	50%
Miami, FL	88	7.1	45%
Houston, TX	125	5.2	55%
Boston, MA	98	7.4	33%
Washington, D.C.	85	8.0	28%

Key Insights from Coffee Shop Market Analysis

• Seattle, WA – Starbucks’ Birthplace: With 90 stores, Seattle has one of the highest Starbucks locations per capita, showcasing strong brand loyalty and a well-established Starbucks store distribution data footprint.
• Miami, FL – A Growing Market: With 88 stores and 45% drive-thru availability, Miami is a significant Starbucks hub in the southeastern U.S., catering to both tourists and locals.
• Portland, OR – A Coffee-Centric Culture: Hosting 72 stores, Portland emphasizes Starbucks' focus on markets with strong local coffee traditions. 50% of stores here feature drive-thru location insights, catering to convenience-focused customers.
• New York, NY – The Largest Urban Market: With 240 stores, New York has the highest total count, but due to its dense urban environment, only 15% of locations have drive-thrus.

This coffee shop market analysis highlights Starbucks' approach to balancing urban retail expansion trends with drive-thru location insights, ensuring optimal coverage in both high-density cities and suburban markets.

Starbucks in Tech-Centric Cities

Starbucks has strategically expanded in technology-driven urban centers, where high-income professionals and fast-paced lifestyles fuel coffee demand.

By leveraging retail footprint mapping and Point-of-Interest (POI) data analytics, Starbucks optimizes its store location strategy in major tech hubs.

Starbucks Presence in Top Tech Cities (2025)

City	Number of Stores	Tech Industry Workforce	% Change in Store Count (2023-2025)	% with Drive-Thru
San Francisco, CA	110	430,000+	+5%	30%
San Jose, CA	50	280,000+	-10%	40%
Seattle, WA	90	350,000+	+3%	40%
Austin, TX	75	200,000+	+8%	55%
Boston, MA	98	180,000+	+6%	33%

Key Insights from Starbucks’ Store Location Strategy

• San Francisco, CA – A Core Tech Hub: With 110 Starbucks stores, the city reflects Starbucks’ alignment with high-density professional areas. The store count has grown by 5% since 2023, despite real estate challenges.
• San Jose, CA – Strategic Adjustments: San Jose, home to 50 locations, has seen a 10% reduction in stores, showcasing urban retail expansion trends adapting to shifting demand and operational efficiency.
• Austin, TX – A Rising Tech Scene: With 75 locations and 55% of stores featuring drive-thrus, Austin’s Starbucks footprint aligns with high commuter activity and suburban expansion.
• Seattle, WA – Home Advantage: As the birthplace of Starbucks, Seattle’s 90 locations cater to a dense corporate workforce, leveraging POI data analytics to maximize high-traffic areas.

This focus on tech city retail presence demonstrates Starbucks' ability to track economic growth, adapt store strategies, and leverage retail data scraping services to refine its urban retail expansion trends for sustained market dominance.

Final Thoughts on Starbucks' Location Strategy

Starbucks' expansive and strategically placed network of stores across the U.S. showcases its sophisticated approach to retail location intelligence.

By leveraging coffee shop market analysis and Starbucks store distribution data, the company continuously optimizes its locations to meet consumer demand and enhance accessibility.

One of the key factors driving Starbucks' retail footprint mapping is its reliance on Point-of-Interest (POI) data analytics.

By analyzing foot traffic, commuter patterns, and local demographics, Starbucks ensures store placement in high-traffic commercial areas, suburban hubs, and emerging urban markets.

Additionally, the rise of drive-thru location insights has influenced Starbucks’ expansion strategy.

As of 2025, 65% of Starbucks stores feature drive-thrus, particularly in suburban and highway locations, catering to on-the-go customers.

This shift reflects changing consumer behaviors, prioritizing convenience and speed.

By combining data-driven insights with flexible store placement strategies, Starbucks continues to dominate the U.S. coffee market.

The brand’s adaptive retail strategy enables it to thrive in both tech-centric cities and suburban growth areas, solidifying its presence as a leader in the coffee shop market analysis landscape.

Empower Your Business with Retail Data Insights

Gaining insights from Starbucks store distribution data highlights the importance of urban retail expansion trends and strategic store location strategy.

Businesses looking to enhance their retail footprint mapping can leverage retail data scraping services to access real-time location intelligence and market trends.

By utilizing retail location intelligence and Point-of-Interest (POI) data analytics, companies can identify high-traffic areas, competitor positioning, and consumer behavior patterns.

This data-driven approach helps optimize site selection, improve market penetration, and enhance sales potential.

At Actowiz Solutions, we provide cutting-edge retail data scraping services to equip businesses with actionable insights.

Whether you’re tracking drive-thru location insights, studying coffee shop market analysis, or planning your next expansion, our solutions empower you with precise, up-to-date data.

Stay ahead of the competition—partner with Actowiz Solutions for smarter retail decisions!

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                    [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

+1 ▼

Select Service Web Scraping Real-Time API Access Data Dashboards Datasets Review & Sentiment Analysis Price Monitoring Custom Solution

Select Industry E-Commerce Quick Commerce Food Delivery Travel & OTA Grocery & Retail Real Estate Others

Target Country Web Scraping Services Mobile App Scraping Web and App Scraping API Datasets Dashboard

A$лвR$C$CHF¥Kčkr€£HK$FtRp₪₹kr¥₩Mex$RMkrNZ$₱złleikrS$฿₺$R Budget Range $500 - $1,000 - Starter Budget (POC/Single Site) $1,000 - $2,500 - Basic Plan (Small Business Needs) $2,500 - $5,000 - Growth Phase (Multi-site/Data Volume Projects) $5,000 - $10,000 - Business-Scale (Automation/API Integrations) $10,000 - $25,000 - Enterprise Projects (Real-Time Systems, Global Data) $25,000 - $50,000 - Enterprise+ (Custom Dashboards, Ongoing Support) $50,000+ - Strategic Partnership (End-to-End Data Ops)

Project Timeline * Immediate 1–3 Months Planning Phase

Additional Trust Elements

✨ "1000+ Projects Delivered Globally"

⭐ "Rated 4.9/5 on Google & G2"

🔒 "Your data is secure with us. NDA available."

💬 "Average Response Time: Under 12 hours"

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize

Explore Solutions Get a Custom Demo

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

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

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

Ecommerce

Q-Commerce

Travel & OTA

Food Delivery

Real Estate

Social Media

Trusted by Global Leaders – Secured by International Standards

Industry:

Fintech / Digital Payments

Result

Accurate daily voucher &

cashback visibility across platforms

★★★★★

“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”

Product Manager, Fintech Platform (India)

✓ Daily voucher & cashback tracking via Push & Pull APIs

View Case Studies

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

View Case Studies

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×

View Case Studies

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

View Case Studies

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

View Case Studies

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

View Case Studies

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

View Case Studies

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

View Case Studies

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★

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

Thomas Galido

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

2 min

★★★★★

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

Iulen Ibanez

CEO / Datacy.es

1 min

★★★★★

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

Febbin Chacko

-Fin, Small Business Owner

1 min

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

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

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

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

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

✔ Scraped Data: Price Insights Top-selling SKUs

Request Demo Access

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

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

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

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

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

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

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

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

All

Blog

Case Studies

Infographics

Report

Feb 19, 2026

Scraping Aqualite UK with Python - BeautifulSoup vs Selenium - Choosing the Right Approach for Data Extraction

Scraping Aqualite UK with Python - BeautifulSoup vs Selenium compares speed, accuracy, and scalability to choose the best scraping method.

Costco Walmart Grocery Pricing Scraping – Geo-Based Real-Time Houston

Geo-based real-time grocery pricing scraping for Costco and Walmart in Houston. Boost retail intelligence with Actowiz Solutions data APIs.

Manual Data Collection vs Automated Scraping — Which Saves More Time & Cost?

Discover the key differences between manual data collection and automated web scraping. Learn which method saves more time, reduces costs, and improves efficiency for your business in 2026.

Amazon Sports And Fitness Market Analysis - Key Insights From Our Analysis Of 1896 Products

Amazon Sports And Fitness Market Analysis covering pricing trends, competition insights, and key findings from 1896 products.

Feb 19, 2026

Scraping Aqualite UK with Python - BeautifulSoup vs Selenium - Choosing the Right Approach for Data Extraction

Scraping Aqualite UK with Python - BeautifulSoup vs Selenium compares speed, accuracy, and scalability to choose the best scraping method.

Feb 18, 2026

How Pedidosya Food Data Extraction Covers All Cities and Categories for Smarter Market Insights

Boost insights with Pedidosya Food Data Extraction. Access all cities and categories to track trends, menus, and customer preferences efficiently.

Feb 17, 2026

How to Solve Stock and Pricing Challenges with Zara Price & Inventory Monitoring with AI-Based Web Scraping

Track Zara product prices and stock efficiently with Zara Price & Inventory Monitoring with AI-Based Web Scraping, ensuring accurate, real-time retail insights.

Read More

Costco Walmart Grocery Pricing Scraping – Geo-Based Real-Time Houston

Geo-based real-time grocery pricing scraping for Costco and Walmart in Houston. Boost retail intelligence with Actowiz Solutions data APIs.

Apartments.com Inventory Scraping Houston 2026 – Lead Generation for Real Estate

Houston apartment inventory scraping from Apartments.com for 2026. Generate verified rental leads with structured data and real-time insights by Actowiz Solutions.

How We Solved Real-Time Competitive Monitoring Challenges with AI-Based Zara Fashion Product Scraping for a Retail Client

AI-powered Zara product scraping enabled real-time competitive insights, automated tracking, and improved pricing decisions for our retail client.

Comparing Medicine Prices in Mumbai vs Delhi Using Real-Time 1mg Data

Comparing medicine prices in Mumbai vs Delhi using real-time 1mg data to analyze price differences, availability, and savings across cities.

Read More

Manual Data Collection vs Automated Scraping — Which Saves More Time & Cost?

Discover the key differences between manual data collection and automated web scraping. Learn which method saves more time, reduces costs, and improves efficiency for your business in 2026.

10 Ways Data Scraping Drives Business Growth & Market Intelligence

Discover 10 powerful ways data scraping boosts business growth, from competitive price intelligence and demand forecasting to inventory tracking and market monitoring.

Grocery Price Movement Tracker – Walmart, Instacart & Target

Real-time grocery price changes across Walmart, Instacart and Target. Track top SKU drops, increases and hourly volatility with Actowiz Solutions.

Read More

Amazon Sports And Fitness Market Analysis - Key Insights From Our Analysis Of 1896 Products

Amazon Sports And Fitness Market Analysis covering pricing trends, competition insights, and key findings from 1896 products.

Gap vs American Eagle Data Scraping – The Financial Giant vs. The Physical Leader Divide

Gap vs American Eagle Data Scraping reveals e-commerce trends, sales performance, and market gaps between the financial giant and physical leader.

Sephora vs Ulta Beauty Data Scraping Comparison - Extracting Prices, Ratings & Trends at Scale

In-depth research report comparing Sephora and Ulta Beauty data scraping, analyzing prices, ratings, product trends, and competitive insights at scale.

Read More

Our perks are irreplaceable

Time Zone Flexibility

Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.

Clear Communication

Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.

Uncompromising Quality

Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations

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