Merchant Transaction Signals are a family of attributes that describe the card processing volumes, card revenues, and other related information for businesses that accept credit cards as a form of payment.
There are six Merchant Transaction Signal Attributes:
These attributes share many of the same structure and methodology.
As of November 2022:
- ~8 million businesses have card revenues in the past 12 months
- 6 million of those businesses we can show granular revenue and trends on, such as the annual revenue and year over year growth
- ~16 million+ businesses have card revenues going back to 2017
- A monthly time series of historical values is available going back to 2017
- To obtain historical data via the Enigma API, use the query parameter
- Enigma publishes a new month of data once monthly
- New months are published 65-75 days after the end of a month (typically between the 5th and 15th of the month). For example, card revenues for the full month of January 2022 were published on April 10.
- Merchant Transaction Signals are derived from a panel of credit and debit card transactions. The panel is made up of over 750 million active credit and debit cards. Enigma estimates the panel covers over 40% of all credit card transactions in the US
- The transaction data is sourced from credit card issuers (as opposed to merchant acquirers or payment processors).
- Visit this section to learn more about the sources of our Merchant Transaction Signals
- Enigma models Merchant Transaction Signals from a panel of more than 40% of all credit and debit card transactions in the US
- Enigma tags each transaction in the panel to the business or business location it was associated with. Enigma maintains a 95%+ accuracy rate for this tagging.
- Enigma starts by attempting to tag the transaction to a specific location.
- If Enigma is unable to tag the transaction to a specific location, Enigma will then attempt to tag it to the business level
- Enigma aggregates the transactions that occurred at the same business or location to arrive at aggregated panel data
- Enigma applies a projection factor to the aggregated panel data to estimate the total revenue, transaction counts, and customer counts for that business
There are many instances where the product of one brand (e.g., Colgate-Palmolive toothpaste) is sold by another brand (e.g., Wal-Mart). Enigma will usually attribute revenue to wherever the end consumer swiped their credit card – e.g., in the example above, a consumer card swipe for Colgate toothpaste at Wal-Mart would show up as Wal-Mart revenue. Here are a few other examples:
Third- party purchasing/ordering platforms:
- In many instances, transactions occur through third-party platforms. For example, a restaurant may receive a pick-up order through Uber Eats or Doordash. Enigma’s goal is to tag the transactions to the merchant providing the good or service, not the platform.
- Enigma is able to accomplish this for most third-party platforms, but not all
- Amazon is one platform where Enigma is not able to attribute the revenues to the third-party seller on Amazon because there is not enough detail in the transactions strings
- In many instances, transactions may get paid for via third-party payment facilitators. For example, a consumer may purchase a product on a Shopify store and use Paypal as a payment facilitator at checkout. Buy now pay later (BNPL) companies are another form of payment facilitator. In these instances, Enigma attempts to tag the transaction to the merchant, not the payment facilitator.
- Enigma is able to accomplish this for most payment facilitators, but not all.
The coverage of different card types means that Enigma is able to provide much more accurate Merchant Transaction Signal information for consumer-facing businesses than B2B business.
Examples of industries where the Merchant Transaction Signals are quite strong:
- Retail and Ecommerce
- Personal Care (spas, hair salons, etc.)
- Doctors and Dental
- Arts and Entertainment
- Automotive Repair and Services
- Other consumer services (gyms, etc..)
Updated 3 months ago