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[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] => 216.73.216.157 [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 )
Starbucks has become a household name in the United States, known for its premium coffee and welcoming ambiance. With thousands of locations across the country, understanding the geographical distribution of Starbucks stores provides valuable insights into its market strategy, consumer behavior, and regional preferences.
The very first Starbucks opened its doors in 1971 at Pike Place Heritage Market in Seattle, Washington. Over the course of fifty years, the company has experienced remarkable growth, now boasting 16,472 stores across the U.S. and its territories. California stands out with 3,105 locations, representing 18.7% of the total.
This impressive data is the result of an extensive analysis of Starbucks locations throughout the U.S., concentrating on review counts and ratings to extract valuable insights.
In this detailed overview of Starbucks statistics, we will uncover public opinions about the brand, highlighting both the best and worst Starbucks stores across the country. Moreover, we’ll share intriguing tidbits about Starbucks, such as the total number of its locations in the U.S. and globally, the demographics of its visitors, the average number of customers it serves, and its annual revenue.
By utilizing web scraping Starbucks store data, we can efficiently perform Starbucks store location scraping USA, scrape Starbucks store distribution by state, and extract geographical data of Starbucks in the U.S.A. Our efforts in Starbucks US store count extraction allow us to gather essential insights into consumer behavior and preferences across different regions.
Businesses interested in leveraging this data can utilize Starbucks outlet data scrapers to extract Starbucks coffee product data, scrape Starbucks coffee menu data, and collect data on customer reviews for deeper insights into customer ratings and preferences. Starbucks coffee data scraping services enable efficient access to this valuable information, empowering organizations to make informed decisions based on comprehensive analysis.
As of the latest data, there are 16,472 Starbucks locations spread across the United States and its territories.
The West region leads in the number of Starbucks stores, boasting 6,146 locations, which accounts for 37.2% of the total. Notably, this concentration is primarily in highly urbanized states, particularly California.
In contrast, the Northeast Region has the fewest Starbucks outlets, representing only 12.3% of the overall count with 2,043 stores. Within this region, most stores are situated in densely populated states, such as New York and Pennsylvania, which together comprise 55.7% of all Starbucks locations in the Northeast.
California boasts the highest number of Starbucks locations, totaling 3,105 stores. This impressive figure represents 18.7% of all Starbucks outlets in the United States.
The sheer quantity of Starbucks in California surpasses the 1,289 high schools in the state, resulting in a ratio of 2.3 Starbucks for every high school. When considering both middle and high schools combined, totaling 2,5562.
The top five cities in California with the most Starbucks are as follows:
These five cities together have 1.2 times the number of Starbucks compared to states with the fewest stores. In descending order, these states are: Montana and Mississippi, each with 49 stores; followed by West Virginia with 46; New Hampshire and Delaware, both at 42; South Dakota with 41; Rhode Island with 32; Wyoming with 28; Maine with 26; North Dakota with 21; and finally, Vermont, which has the fewest stores at 11.Following California, Texas and Florida rank second and third, with 1,393 (8.3%) and 883 (5.3%) Starbucks locations, respectively.
To extract geographical data of Starbucks in the U.S.A, businesses can use a variety of scraping tools and techniques. By collecting this data, companies can analyze trends such as:
Store density per capita:Identifying regions with a high number of stores relative to population size can help Starbucks target areas with significant growth potential.
Market penetration:Understanding how many stores exist in specific areas can guide strategic decisions for opening new locations.
Coffee consumption in the United States continues to grow, with Starbucks being a significant player in this market. According to the National Coffee Association, around 61% of Americans drink coffee daily. Here are some key statistics related to Starbucks coffee consumption:
Starbucks serves approximately 100 million customers each week globally, with a significant portion of those customers being in the U.S.
The average American consumes about 3.1 cups of coffee daily, with Starbucks customers often exceeding this average due to the chain's extensive menu.
Starbucks’ most popular beverage is the Caramel Macchiato, contributing to the chain's substantial sales volume.
Specialty Coffee:Specialty coffee sales have surged, with customers increasingly seeking high-quality, unique flavors. Starbucks has capitalized on this trend by offering a diverse menu.
Sustainable Practices:Consumers are becoming more conscious of sustainability. Starbucks has responded by offering ethically sourced coffee and promoting environmentally friendly practices.
Web scraping plays a crucial role in gathering and analyzing data related to Starbucks’ geographical distribution and coffee consumption. By employing effective web scraping techniques, businesses can:
Extract Starbucks Store Data:By scraping data from various online sources, companies can gather information about store locations, hours of operation, and services offered.
Collect Data on Customer Reviews:Analyzing customer reviews provides insights into consumer sentiment and preferences, enabling Starbucks to adapt its offerings.
Scrape Customer Ratings:Understanding how customers rate their experiences helps Starbucks maintain quality control and enhance customer satisfaction.
Extract Starbucks Coffee Product Data:Gathering data on menu items allows for analysis of popular products and trends, guiding inventory and marketing strategies.
Scrape Starbucks Coffee Menu Data:By scraping menu data, businesses can assess pricing strategies and product offerings in different regions.
To effectively scrape Starbucks store distribution data, businesses can use various tools and programming languages, such as Python with libraries like Beautiful Soup or Scrapy. The process typically involves:
Identifying Data Sources:Finding reliable websites that list Starbucks locations and relevant information.
Setting Up Scraping Tools:Configuring tools to extract necessary data fields, such as store addresses, contact information, and operational hours.
Data Cleaning and Structuring:Once the data is collected, it must be cleaned and structured for analysis.
Analyzing the Data:Conducting analyses to identify trends and insights that can inform strategic decisions.
Understanding the geographical distribution of Starbucks stores in the USA provides valuable insights into the brand’s market strategy and consumer behavior. By leveraging web scraping techniques, businesses can collect data on customer reviews and scrape customer ratings on Starbucks, allowing for comprehensive analysis of essential data that informs decision-making processes. From understanding store density to evaluating customer feedback, Collect Data Customer Reviews on Starbuck, and Scrape Customer Rating on Starbuck, web scraping offers a thorough approach to gathering data that drives success in the competitive coffee market.
Actowiz Solutions provides advanced web scraping services tailored to your business needs. We help you extract, analyze, and leverage data from Shopee to maximize your potential. Contact Actowiz Solutions today to empower your e-commerce strategy! You can also reach us for all your mobile app scraping, instant data scraper and web scraping service requirements.
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Marketing Director, Sleepyowl Coffee
Boosted marketing responsiveness
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