QSR and fast casual is one of the most misunderstood verticals in B2B data.
Most providers will sell you a list of restaurant locations. That list looks impressive. It has tens of thousands of rows. It maps to a real industry code. It has addresses and phone numbers and brand names on the storefront.
It is also the wrong list.
Restaurants are not bought one location at a time. They are bought one operator at a time. A single multi-unit franchisee can run fifty Subway locations through fifteen LLCs. A 50-location operator and a 1-location operator are not the same account, even when the brand on the door is identical. If your target list treats them the same, your pipeline is going to reflect that.
This is the problem QSR data has to solve. Most stacks do not.
The restaurant vertical is not a location problem. It is an operator-resolution problem. The data that wins QSR is the data that rolls every storefront up to the human who actually signs the check.
Chapter 01Quick answer
The best data sources for QSR and fast casual targeting are Secretary of State filings to identify the franchisee LLC behind each location, Franchise Disclosure Documents to verify franchisor-franchisee relationships, alternative name and DBA records to link the legal entity to the brand storefront, credit card transaction panels to estimate per-location revenue, state and county health department permits to confirm active operating status, and officer-level contact data to reach the multi-unit operator who actually buys for the business.
Used together, these resolve the QSR market at the operator level. That is the unit of analysis that matters.
Chapter 02Why QSR data is harder than typical SMB data
The US has roughly 200,000 limited-service restaurant locations under NAICS 722513, and over 60 percent of them are franchised. That creates three structural problems that most B2B data stacks were not designed to handle.
Problem one. Locations and operators are different units of analysis. A multi-unit franchisee might own 50 Subway locations across 15 LLCs. The location count is 50. The buyer count is one. A target list that shows 50 records mistakes 50 storefronts for 50 accounts. The rep then runs the same outbound 50 times to the same person, who tunes out after the second voicemail.
Problem two. Brand names mask legal entity names. The McDonald's on the corner is a brand owned by McDonald's Corporation. The operating entity is an independent LLC with a different legal name. There are roughly 13,000 McDonald's locations in the US. There is not one McDonald's account. There are thousands. Targeting "McDonald's" as a single account is a sales motion that does not exist in the real world.
Problem three. The buyer is the operator, not corporate. Corporate sets brand standards, approves the supplier list, and decides what the menu looks like. Everything else, equipment, POS, payroll, insurance, supplies, services, marketing tech, is decided by the franchisee operator. Pointing outbound at corporate brand HQ is not just inefficient. It is targeting a buyer who does not exist for most B2B sellers in this category.
A QSR data stack that does not solve those three problems will produce a target list that conflates locations with accounts, misses 60 percent of the universe, and points outbound at the wrong person.
Chapter 03The seven data sources that actually matter
There is no single source that resolves QSR. The vertical requires seven, layered.
Secretary of State filings
The legal entity layer. Every franchisee LLC files with the SoS in the state where it operates. Provides legal name, registered address, officer of record, incorporation date, and current status.
Franchise Disclosure Documents
The franchisor's official annual filing listing every franchisee. Authoritative for brand-to-franchisee verification. Universal coverage across registered franchisors, annual refresh.
Alternative names and DBA records
The trade name layer. "Smith Holdings LLC" operating as "Subway #38291" is the link that ties a legal entity to a brand. Without this layer, brand resolution is guesswork.
Credit card transaction panels
The revenue layer. Per-location transaction volume estimates from card-issuer or aggregator panels. Aggregated per-operator, this becomes account-level revenue sizing.
Health department permits
The operating-status layer. Active permit confirms the location is actually serving food today. Catches closed locations that still appear active in SoS data.
Officer-level contact data
The contactability layer. Direct phone and direct email on the registered officer, who for franchisees is the operator. What makes the list usable for outbound, not just analysis.
POS and payments technographics
Optional. Toast, Square, Clover, Lightspeed signals at the location level. Useful as a secondary filter for vendors selling adjacent to the POS, not reliable as a primary universe.
The point is not having access to one of these. The point is layering all of them into a single operator-resolved view.
Chapter 04The fields that move the needle
A QSR data dictionary can run to hundreds of fields. A small number do the actual work.
For identifying the operator
| Field | Why it matters |
|---|---|
| company_number + jurisdiction_code | The composite primary key for the legal entity. Joins everything else. |
| name | LLC name, usually a holdings company, not the brand on the storefront. |
| incorporation_date | When the franchisee LLC was formed. New franchisees are a buying signal. |
| industry_code_uids | NAICS 722513, 722515, and where relevant 722511. |
| current_status, inactive | Filters out dissolved entities, of which there are many. |
| number_of_employees | Proxy for operation size when location count is unknown. |
| registered_address.region | Territory routing. |
For resolving the brand
The alternative_names file is the join. Pattern-match the trade name against a brand reference list. After matching, every entity falls into one of three buckets: brand-franchised, brand-corporate (a branch of the franchisor), or independent.
For reaching the operator
| Field | Why it matters |
|---|---|
| officer_phone | Direct dial on the operator. Without this, the list is read-only. |
| officer_email_hem | Direct email on the operator. Same point. |
| position | Filter for Managing Member, Member, President. Reject Registered Agent. |
| address.in_full | Operator contact address, the join key for consumer overlay. |
For sizing the operator
person_uid on the officer file links the same individual across multiple franchisee LLCs they own. Transaction panel match on registered_address.in_full provides per-location revenue. Sum across LLCs owned by the same person_uid gives operator-level revenue.
DefinitionOperator resolution is the process of collapsing multiple franchisee LLCs and multiple storefront locations into a single account record representing the human being who owns and runs them.
Chapter 05How to build an operator-level QSR target list
A six-step build that gets you from the raw SoS file to a working outbound list.
Step 1. Define the universe by industry code. Filter the companies file for NAICS 722513, 722515, and where the use case calls for it, 722511. Apply current_status = active and inactive = false. The result is roughly 280,000 to 320,000 legal entities, higher than the 200,000 location count because multi-unit operators own multiple LLCs each filed separately.
Step 2. Resolve the brand. Join the alternative names file. Pattern-match trade names against a brand reference list. Subway #38291 matches Subway. Smith Family Pizza matches no major brand and gets tagged as independent. After this step, every entity carries a brand attribution.
Step 3. Roll up to the operator. Link multiple LLCs to the same human using person_uid on the officer file when available, or by matching officer name and contact address across LLCs when it is not. Where control statements exist in the Relationships file, use them. The output is an operator-level account list with 1 to N locations attached to each operator.
Step 4. Size with transaction data. Match credit card transaction panel estimates to each location's registered address. Sum per-location estimates by operator. Rank operators by total estimated revenue and by location count. A 50-location Subway operator and a 50-location Jersey Mike's operator might be similarly sized. A 50-location independent grouping is structurally a different conversation.
Step 5. Add operator contact data. Join officer_phone and officer_email_hem on the principal officer, typically the Managing Member whose start_date matches the LLC's incorporation_date. For multi-unit operators, the same person carries across all their LLCs, so the contact data joins once at the operator level.
Step 6. Verify operating status. Match locations against state and county health department permit files. An active permit confirms the location is serving food today. An expired permit catches closures that still appear active in SoS data.
A QSR target list ranked by operator location count and operator estimated revenue is a different artifact than a list of restaurant addresses. One drives pipeline. The other generates dials.
Chapter 06Consumer attributes on the operator
The QSR operator is the buyer. The operator is also a consumer. Consumer attributes overlaid on the operator's contact address produce signals that pure firmographic data cannot.
- Vehicle ownership. Operators with multiple high-MSRP vehicles (
Auto_MSRP_Max > $50,000,Auto_Class_Luxury) tend to be higher-revenue multi-unit operators. Single mainstream-class vehicles tend to be single-unit owner-operators. - Home value and real estate. Operator home value correlates with operator business revenue in the QSR vertical. Not perfectly, but strongly enough to refine prioritization within revenue bands.
- Household composition. Family-owned operations buy differently than single-operator businesses. The household composition fields capture this.
These signals do not replace transaction data for revenue sizing. They refine prioritization once revenue bands are already set.
DefinitionConsumer overlay on the operator is the process of joining personal consumer attributes to the operator's contact address, treating the operator as the buyer they are.
Chapter 07How each GTM persona should use this
Demand generation
Build audiences of multi-unit operators by brand, region, and revenue band. Hash-match the operator contact address into Meta, LinkedIn, and the audience layer for paid acquisition. Run brand-specific campaigns targeting "all Subway operators in Texas with 5+ locations" rather than generic restaurant targeting.
RevOps
Get the data model right. Locations are not accounts. Accounts are operators. Locations are child records under the operator. Routing, scoring, and reporting all have to respect that hierarchy or the pipeline numbers will not match reality. The person_uid field is what makes this work.
Sales and SDR leadership
Equip reps with operator-level account briefs that show every location an operator runs, the brands they operate, the estimated revenue per location, the years in business, and the direct dial. The conversation changes when the rep opens with "I see you run 12 Jersey Mike's locations across DFW" rather than "I see you have a Jersey Mike's in Plano."
CRO
Prioritize the top-decile of multi-unit operators. The top 10 percent of franchisees by location count control roughly 40 percent of franchise locations in the US. Coverage strategy at the operator level is structurally different than coverage strategy at the location level. The dictionary makes that strategy possible.
Want a working sample of the operator-resolved QSR file?
Companies, Officers, Alternative Names, and Relationships, joined on company_number + jurisdiction_code, with operator rollup applied. Delivered to your warehouse or CRM.
Chapter 08FAQ
How many QSR locations are there in the US?
Approximately 200,000 limited-service restaurant locations under NAICS 722513, per Census Bureau County Business Patterns data. Adding fast casual brands classified under broader restaurant codes brings the operating universe to roughly 230,000 to 250,000 locations.
What is the difference between QSR and fast casual?
QSR (quick service restaurant) is limited-service with counter ordering and lower price points: McDonald's, Burger King, Subway, Taco Bell. Fast casual is the hybrid category, higher-quality ingredients and slightly higher price points but still counter service: Chipotle, Panera, Sweetgreen. Same data sources, different brand list.
Who actually buys for a franchise QSR location, corporate or the franchisee?
The franchisee operator buys, within the constraints set by the franchisor. Equipment, POS, payroll, insurance, supplies, and most services are franchisee decisions. Brand standards, core menu, marketing campaigns, and approved-supplier lists are franchisor decisions. For most B2B sellers targeting QSR, the operator is the buyer.
How do I find multi-unit QSR operators?
Link franchisee LLCs by common officer using person_uid, or by matching officer name and contact address across LLCs. Add control statements from the Relationships file where they exist. The output is operator-level accounts with multiple LLCs and multiple locations attached. The top 10 percent of operators by location count control roughly 40 percent of franchise locations.
Can I get revenue data for individual QSR locations?
Exact per-location revenue is generally not public. Credit card transaction panels (Earnest, Yipit, Fable Data, and panels assembled from card-issuer data) produce per-location estimates with reasonable accuracy. Aggregated per-operator revenue is more reliable than per-location estimates.
How do I tell a franchise location apart from a corporate-owned location?
Corporate-owned locations register as branches (branch = 'F') of the franchisor's corporate entity, with home_jurisdiction_company_number pointing back to corporate. Franchisee-owned locations register as independent LLCs with the operator as the officer. The structural signal is whether the operating entity is a branch of the franchisor or an independent entity.
Is restaurant POS data (Toast, Square, Clover) available for targeting?
Yes, but with caveats. Some technographic providers expose POS vendor signals at the location level. Coverage is biased toward the customer bases of those POS vendors and is incomplete for legacy or independent systems. Use POS technographic data as a secondary signal, not as a primary universe.
What does QSR-specific data cost?
A national QSR target list with franchisee LLC resolution, brand mapping, operator contact data, and transaction-based revenue estimates typically prices from $25,000 to $150,000 annually depending on refresh cadence and depth of consumer attribute overlay. Pure SoS data without brand resolution or transaction sizing is significantly cheaper, but produces the structural problems described in Chapter 02.
Can the operator-resolved file be delivered into a CRM or warehouse directly?
Yes. LeadGenius bulk delivery supports CRM-ready output for Salesforce and HubSpot, and warehouse delivery into Snowflake, BigQuery, and Databricks. The dictionary, the records, and the operator rollup logic come together in the delivery.
Stop targeting brands.
Start targeting operators.
Tell us the brands, the regions, and the location-count thresholds. We will build the operator-resolved file, deliver the records, and refresh on the cadence your QSR motion actually needs.
