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GeoIp2\Model\City Object
(
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            [city] => Array
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                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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            [traits] => Array
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    [continent:protected] => GeoIp2\Record\Continent Object
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                    [geoname_id] => 6255149
                    [names] => Array
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                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                    [iso_code] => US
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
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            [validAttributes:protected] => Array
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
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                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
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                            [zh-CN] => 美国
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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    [traits:protected] => GeoIp2\Record\Traits Object
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                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
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                    [8] => isHostingProvider
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [20] => userCount
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        )

    [city:protected] => GeoIp2\Record\City Object
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                    [geoname_id] => 4509177
                    [names] => Array
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                            [pt-BR] => Columbus
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                            [zh-CN] => 哥伦布
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    [location:protected] => GeoIp2\Record\Location Object
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                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
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            [validAttributes:protected] => Array
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                    [1] => accuracyRadius
                    [2] => latitude
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                    [4] => metroCode
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                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
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        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => 43215
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            [validAttributes:protected] => Array
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                    [0] => code
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    [subdivisions:protected] => Array
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            [0] => GeoIp2\Record\Subdivision Object
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                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
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                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
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                    [validAttributes:protected] => Array
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)
 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
)
Navratri Mega Sale Price Tracking

1. Introduction

The Quick Service Restaurant (QSR) industry in North America is one of the most franchise-heavy markets in the world. Major chains across the USA and Canada rely on thousands of individually owned franchise units. To support expansion strategies, competitive mapping, and outreach programs, one client approached Actowiz Solutions with a clear question:

“Does your dataset include the name and email address of each franchise owner for QSR brands across the USA and Canada?”

This case study outlines how Actowiz Solutions built a compliant and structured QSR Franchise Intelligence Dataset that includes store-level details, franchise owner information (where publicly available), and operational metadata.

Client Background

Navratri Mega Sale Price Tracking

The client works in:

They needed a single dataset that brings together:

  • QSR franchise locations
  • Business owner information
  • Contact details (public sources only)
  • Brand-wise expansion mapping
  • Geographic coverage for USA & Canada

This dataset would support:

  • Lead generation
  • Territory assessment
  • Market penetration strategies
  • Competitor identification

Challenges

Navratri Mega Sale Price Tracking
3.1 QSR Franchise Ownership Data Is Not Uniform

Some QSR chains reveal:

  • Franchisee owner names
  • Multi-unit ownership details
  • Official contact emails

Other chains show only:

  • Store address
  • Phone number
  • Operational hours

Actowiz had to unify irregular data sources.

3.2 Email Addresses Must Be Publicly Available

For compliance, Actowiz only collects:

  • Emails openly published on official listings
  • Emails available on franchisee websites
  • Public business contact emails
  • Emails available through permitted data sources

No private, login-protected, or confidential emails can be included.

3.3 Identifying Franchise vs Corporate-owned Stores

Not all store-level pages mention ownership status. Actowiz had to detect:

  • Franchise-owned
  • Company-owned
  • Multi-franchise groups
  • Area developers
3.4 Large Geographic & Brand Coverage

The client wanted:

  • USA (All states)
  • Canada (All provinces)
  • Multiple QSR brands, including:
    • McDonald's
    • Wendy’s
    • Burger King
    • Popeyes
    • Tim Hortons
    • KFC
    • Domino’s
    • Pizza Hut
    • Subway
    • Starbucks
    • Chick-fil-A
    • Five Guys
    • Dunkin’

Coverage needed to be complete and standardized.

Actowiz Solutions Approach

Actowiz designed a QSR Franchise Ownership & Contact Intelligence Dataset that aggregates:

  • Location information
  • Franchisee ownership metadata
  • Publicly listed contact details
  • Brand-level categorization
  • Store-level operational information

This dataset is updated regularly (weekly/monthly) depending on client needs.

Data Provided in the QSR Franchise Dataset

5.1 Store-Level Data (USA & Canada)

For every QSR unit:

  • Store name
  • Brand
  • Address (full, parsed)
  • City, State/Province, ZIP/Postal Code
  • Phone number
  • Website/Ordering page
  • Latitude, Longitude
  • Opening hours
  • Drive-thru availability
  • Delivery support (UberEats, DoorDash, etc.)
5.2 Franchisee Ownership Data (Where Publicly Available)

Actowiz extracts franchise owner information ONLY if publicly listed via:

  • Company franchise directories
  • Government business registries
  • Franchisee business websites
  • Public LinkedIn business pages
  • Press releases / local business news
  • Chamber of commerce listings

Possible fields include:

  • Owner Name
  • Franchise Group Name
  • Number of Units Owned
  • Public Business Email
  • Public Business Phone
  • Public Contact Form URL
  • Corporate Office Details (if a multi-unit group)

Actowiz does not provide:

  • Private emails
  • Personal email IDs
  • Restricted data

Only publicly accessible business contact information is delivered.

5.3 Additional Business Metadata
  • Franchise type (single-unit / multi-unit owner / corporate-owned)
  • Franchise group profile
  • Year opened
  • Estimated sales volume (if available through public sources)
  • Workforce size indicators (optional)

Sample Data Output (Illustrative Only)

Store-Level Example
Brand Store Name Address Owner Name Email Phone City State
Subway Subway #4821 3921 7th Ave SW FreshEats Franchise LLC info@fresheatsgroup.com (403) 555-2291 Calgary AB
McDonald's McDonald's Store 21245 1800 Richmond Hwy Smith Family Restaurants Inc. contact@smithfranchising.com (703) 555-7810 Arlington VA
Tim Hortons TH #998 5200 Yonge St NorthBrew Holdings admin@northbrew.ca (647) 555-9082 Toronto ON

(Sample values – Not live data)

Business Impact for the Client

7.1 Immediate Franchisee Outreach Readiness

Client could now:

  • Identify franchise owners
  • Contact business groups
  • Start market expansion conversations
7.2 Stronger Territory Planning

Dataset enabled:

  • Heatmaps of QSR density
  • Brand penetration analysis
  • Regional competitive mapping
7.3 Complete Visibility of Multi-Unit Owners

Actowiz identified:

  • Owners with 5+ locations
  • Owners managing multiple brands
  • Regional operators
  • Largest franchise groups in USA + Canada

This helped the client target high-value prospects.

7.4 Sales, BD, and Marketing Optimization

Teams used the dataset for:

  • Outreach campaigns
  • Regional sales planning
  • Brand distribution modelling
  • Market entry analysis

Why Actowiz for QSR Franchise Data?

Actowiz provides:

  • USA + Canada-wide franchise location data
  • Accurate owner/group identification
  • Public email collection from compliant sources
  • High-quality structured datasets
  • Scalable updates (weekly/monthly)
  • API + CSV + Excel delivery
  • Custom filtering by brand, region, or ownership type

We support all major QSR brands across North America.

Conclusion

Actowiz Solutions delivered a complete QSR Franchise Ownership Intelligence Dataset that includes:

  • Store-level location details
  • Public franchise owner names
  • Public business email addresses
  • Franchise group metadata
  • Operational details

This dataset empowers teams in:

  • Market expansion
  • Franchise development
  • Competitive intelligence
  • B2B outreach
  • Regional analysis

Across the entire USA and Canada.

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.
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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
Product Image
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

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