Brand
The customer-facing identity of a business—the name and presence under which it engages with customers. A brand represents a distinct commercial identity and may operate across multiple physical locations and websites. A single legal entity may do business under one or more brands (for example, a franchisor operating under multiple trade names), and a brand may be backed by one or more legal entities.
GraphQL type: Brand
Starbucks is a brand. It has thousands of operating locations, is owned by Starbucks Corporation (a legal entity), and is classified in the food service industry.
Other Attributes
Additional attributes available on this Brand that are not part of the standard attribute groups.
Brand Activity
Description
Identifies businesses that engage in activities with a high compliance risk.
Child attributes
- activity_type response values refer to high-risk categories of business activities.
- compliance_risk_level is
highfor all flagged businesses in Enigma KYB, which will expand in future iterations to varying levels of risk.
Coverage
- Brands: We have classified ~130K brands as having high-risk activities. This includes online-only businesses (those without any identifiable physical address).
Data sources
- The "High-risk activities" attribute is derived from the list of names, websites, and public web descriptions associated with a business via a set of heuristics. Names and websites are derived from all of Enigma's data sources, from card transactions to legal entity registrations.
Methodology
- Enigma looks for keywords through industry descriptions, names, and website URLs associated with businesses. Enigma does not currently look at the content of a website.
- For example, to classify a business as having a high-risk activity of "cannabis", Enigma looks for key terms within industry descriptions, names, and website URLs: cannabis, marijuana, dispensary, CBD, THC, Ganja.
Why use Enigma KYB's high-risk classification?
- Enigma's high-risk classification improves automated customer onboarding by identifying businesses that engage in activities with a high compliance risk, allowing those businesses to be reviewed manually or follow additional risk assessment processes before onboarding. This increases confidence in your organization's automated onboarding workflow and ensures you're only bringing on businesses that meet your desired risk standards.
High-risk categories
- Cannabis: Brick & mortar or online retail stores that primarily sell cannabis/marijuana and related products (THC, CBD, etc.), cannabis/marijuana growers or distributors, and software providers for the cannabis/marijuana industry.
- Tobacco and Vaping: Brick & mortar or online retail stores that primarily sell tobacco and vaping products (cigarettes, cigars, e-cigarettes).
- Firearms, Weapons and Ammunition: Brick & mortar or online retailers that primarily sell guns, firearms, weapons, and ammunition; or shooting ranges or related locations.
- Adult Entertainment and Dating: Dating (online dating sites and applications), Adult entertainment clubs (clubs that are primarily strip clubs, gentlemen's clubs, sex clubs) but not businesses that are primarily just night clubs, adult entertainment retail stores (e.g., sex shops, but not other types of stores like lingerie stores), online adult entertainment sites (pornography sites, pay per view chat sites/apps)
- Gambling and Sports Betting: Casinos, online gambling sites, sports betting websites and B&M retail locations, fantasy sports leagues (but not other sports-related businesses), bingo halls
- Payments and Money Transfer: Payment processors, POS providers, crowdfunding sites, factoring, lending services
- Multi-level marketing: Multi-level marketing, pyramid schemes
- Pawn Shops, Check Cashing and Payday Loans
- Cryptocurrencies and Digital Assets: Cryptocurrencies, blockchain, digital assets, digital wallets, crypto/blockchain related infrastructure
- Investments and Financing: Investment brokers, lending instruments
- Legal Finance: Collections agencies, bail bonds
- Gift Cards: Gift card retailers, retail stores that buy unused gift cards, websites whose primary purpose is selling gift cards
- Health and Lifestyle: Diet centers, supplements/nutraceuticals and other products not regulated by the FDA, hair extensions
- Prescription Drugs: Pharmacies likely to sell prescription drugs
Pricing tier: Plus
| Field | Name | Type | Description |
|---|---|---|---|
| Activity Type | activity_type | string | The type of high-risk activity associated with the business. |
| ID | id | long | |
| First Observed Date | first_observed_date | string | |
| Last Observed Date | last_observed_date | string |
Is Marketable (Brand)
Description
Contains a boolean value indicating whether the brand is marketable.
Data Sources
A brand is considered marketable if it meets certain criteria, like whether it has open locations, revenue in the last 12 months, or reviews in the last 12 months.
Pricing tier: Core
| Field | Name | Type | Description |
|---|---|---|---|
| Is Marketable | is_marketable | boolean | This field contains a boolean value indicating whether the brand is marketable. |
| ID | id | long | |
| First Observed Date | first_observed_date | string | |
| Last Observed Date | last_observed_date | string |
Brand Location Description
Description
A human-readable description of where a brand operates geographically, based on the states of its operating locations.
For brands with multiple locations, this shows the top states where the brand has a significant presence (either more than 10% of locations or more than 5 locations). Up to 5 states are listed alphabetically (e.g., "CA, FL, NY, TX, WA" or "CA, FL, NY, TX, WA and others" if there are more than 5 significant states).
For brands with a single location, this shows the specific city and state of that location (e.g., "San Francisco, CA").
This attribute provides a quick summary of a brand's geographic footprint without needing to examine all individual locations.
Time Structure
This attribute does not include time series data and reflects the most current state of the brand's locations.
Data Sources
This attribute is derived from:
- Brand to operating location relationships
- Operating location to address relationships
- Address state and city information
Pricing tier: Core
| Field | Name | Type | Description |
|---|---|---|---|
| Location Description | location_description | string | A text description of where a brand operates, showing either the top states for multi-location brands or the specific city and state for single-location brands. |
| ID | id | long | |
| First Observed Date | first_observed_date | string | |
| Last Observed Date | last_observed_date | string |
Name
Description
The customer-facing version of the name that best represents the business.
Data Sources
The brand name is derived from:
- Publicly available business data and listings
- Privately verified business information
Methodology
- Enigma uses high quality data sources to get the best representation of a business name. Within those data sources, Enigma ranks by dataset quality and frequency to determine the most likely name the business is referred to by.
- Persons: Agents/providers/business owners available in our data asset that are branded as the business will use their person name as the brand name. The company that employs the agents/providers will also have their own brand name and an affiliated relationship with the agents/providers.
- Sub-brands/Franchises: Businesses that operate multiple other businesses, or franchisers that are operated by multiple franchise locations available in our data asset will be referred to by different brand names and have affiliated brand relationships.
Pricing tier: Core
| Field | Name | Type | Description |
|---|---|---|---|
| Name | standardized_name | The customer-facing version of the name that best represents the business. | |
| ID | id | long | |
| First Observed Date | first_observed_date | string | |
| Last Observed Date | last_observed_date | string |
Brand Revenue Quality
Description
Warnings and issues related to the revenue of this brand.
Pricing tier: Plus
| Field | Name | Type | Description |
|---|---|---|---|
| Issue Reason | issue_reason | string | The reason for the revenue quality issue. The reasons signify the following: - REVENUE_DECREASE_TO_0_PCT_LOCATION_OPEN (HIGH severity): Brand revenue drops to zero and at least 1 operating location is currently open. - REVENUE_DECREASE_TO_20_PCT_LOCATION_OPEN (HIGH severity): Brand revenue drops to 20% of the median revenue over the past 12 months and at least 1 operating location is currently open. - REVENUE_INCREASE_TO_250_PCT_IN_LAST_18M (HIGH severity): Brand revenue increases to 250% of the median revenue for 3 months in the past 18 months. - REVENUE_INCREASE_TO_250_PCT_ALL_TIME (HIGH severity): Brand revenue increases to 250% of the median revenue for 3 months at any point in its revenue history. - REVENUE_DECREASE_TO_0_PCT_LOCATION_UNKNOWN (MEDIUM severity): Brand revenue drops to zero and the latest operating location operating status is stale. - REVENUE_DECREASE_TO_20_PCT_LOCATION_UNKNOWN (MEDIUM severity): Brand revenue drops to 20% of the median revenue over the past 12 months and the latest operating location operating status is stale. |
| Issue Severity | issue_severity | string | The severity of the revenue quality issue. |
| Issue Description | issue_description | string | A description of the revenue quality issue. |
| ID | id | long | |
| First Observed Date | first_observed_date | string | |
| Last Observed Date | last_observed_date | string |
Seasonality Index (Brand)
Description
The Seasonality Index identifies brands with recurring seasonal revenue patterns, measuring both the overall concentration of revenue across months and the specific month in which revenue consistently peaks.
It answers two questions for every eligible brand:
- Is the business seasonal at all? →
gini_scoremeasures how concentrated revenue is across months. - If so, when is the peak? →
recurring_peak_monthidentifies the calendar month where revenue is most concentrated.
For example, a ski resort will show high revenue concentration in winter months (high gini_score) with recurring_peak_month = December. A tax preparation office will show concentration in early spring with recurring_peak_month = April. The index captures the pattern itself, but the underlying cause can vary: weather (ice cream shop), regulatory deadlines (tax preparer), academic calendars (school supply store), holidays (Christmas tree farm), or other factors.
Layer 1 — Overall revenue concentration (gini_score)
The Gini score measures how concentrated card revenue is across months. It ranges from 0 (revenue perfectly flat) to 1 (all revenue in a single month). A score of 0.4 or above indicates meaningful seasonal concentration and is the recommended threshold for identifying seasonal businesses.
Interpretation guide:
- < 0.4: Revenue roughly flat year-round — not considered seasonal (e.g. restaurant, gas station)
- 0.4–0.6: Moderate seasonality — clear peak but revenue spread across several months (e.g. ice cream shop, garden center)
- 0.6–0.8: Strong seasonality — most revenue concentrated in a short window (e.g. tax preparation, farm)
- > 0.8: Extreme concentration — nearly all revenue in 1–2 months (e.g. Halloween costume store, Christmas tree farm)
Layer 2 — Recurring peak month (recurring_peak_month, peak_month_revenue_share)
Identifies the recurring peak month where revenue is most concentrated. The peak month must be consistent across all observed periods, not just a single year.
Data Sources
Derived from historical card transaction revenue over a rolling 36-month observation window. Eligibility criteria are applied to ensure the seasonal signal is based on sufficient history and transaction volume to produce a reliable result. A brand is eligible if:
- It has at least $10,000 in card revenue in each 12-month period in the observation window.
- It has at least ~4 years of revenue history to calculate a reliable seasonal pattern.
Seasonality is measured directly from each brand's own revenue history rather than inferred from industry or geography. Not all tax preparers are seasonal, and not all ice cream shops are. A Miami ice cream shop may have flat revenue year-round while a Minnesota one peaks sharply in summer. This individual-level approach lets the data speak for each brand rather than relying on categorical assumptions.
Pricing tier: Premium
| Field | Name | Type | Description |
|---|---|---|---|
| Gini Score | gini_score | double | A score from 0 to 1 measuring how concentrated a business's card revenue is across months. Computed as the Gini coefficient of monthly revenue shares within each 12-month period, then taking the minimum across the three periods as a conservative floor. A higher score indicates greater revenue concentration across fewer months. Scores of 0.4 and above indicate meaningful seasonality. |
| Recurring Peak Month | recurring_peak_month | string | The month (e.g. "July") where card revenue is most concentrated across observed periods. Only populated when the peak month accounts for at least 20% of annual revenue in every observed period. NULL indicates no consistent seasonal peak was detected. Note: only the single dominant peak month is surfaced. Businesses with multiple seasonal peaks will show only the month with the highest recurring revenue share. As 65% of seasonal businesses at the recommended threshold have a single peak, this captures the majority of cases. |
| Peak Month Revenue Share | peak_month_revenue_share | double | The revenue share of the peak calendar month, expressed as a value between 0 and 1. For example, a value of 0.20 means the peak month accounts for at least 20% of annual revenue in every observed period. When this value reaches 0.20, recurring_peak_month is populated. As a reference point, a perfectly flat non-seasonal business would show ~0.08 (1/12 ≈ 8%) for every month, so the 0.20 threshold means the peak month contributes at least 2.5x what a flat business would show. |
| ID | id | long | |
| First Observed Date | first_observed_date | string | |
| Last Observed Date | last_observed_date | string |
Brand Data
Attributes and metrics directly available on a Brand entity.
Reviews
PlusSummary of publicly available customer reviews for this entity.
Industry
CoreThe industry within which the business operates.
Technologies Used
PremiumIndicates third-party technologies being used at a particular operating location.
Card Transactions
PlusContains quantitative information about the card transactions processed by the brand.
Operating Locations
Legal Entities
PremiumBusinesses which have become legal entities by registering with a U.S. Secretary of State (SoS).
Also available: ID (.id), Name (.name), Is Marketable (.isMarketable).
Connected Data
Data accessed through Brand's relationships to other entities. These fields are available when querying a Brand but live on connected entities.
Address
A physical street address for the business, standardized to USPS conventions.
Contacts
A natural person associated with a business as an owner, officer, or contact.
Registrations
A business registration filed with a U.S. Secretary of State, either domestic (creating the entity) or foreign (often a requirement to operate in that state).
Registrations - People
A natural person associated with a business as an owner, officer, or contact.
Also available: Phone (via operating location), Website (via website), Online Presence (via website).
Relationships
Brand connects to other entities in the Enigma graph:
| Direction | Relationship | Target Entity |
|---|---|---|
| → | does business within | Industry |
| → | is affiliated with | Brand |
| → | operates at | Operating Location |
| → | operates website | Website |
| ← | does business as | Legal Entity |
| ← | licenses | Legal Entity |
| ← | owns | Legal Entity |
| ← | is performed at | Role |
| ← | is profile of | Website |