The Go-to-Market Guide to Better Prospecting with Card Transaction Data
What card transaction data is, where it comes from, and how go-to-market teams use it to identify fast-growing small businesses and improve ROI.
Eight in 10 Americans report having at least one credit card, and there were more than 511 million active consumer credit cards in the United States in Q1 2020. The pandemic accelerated movement away from cash: according to McKinsey, by the end of 2020, U.S. consumers used cash for just 28% of transactions, compared to 51% a decade prior.
For go-to-market leaders engaging small businesses, this increasing adoption of cards — and the data and intelligence they generate — unlocks new opportunities for prospecting and prioritizing your ideal customers.
What Is Card Transaction Data?
“Card transaction data” typically refers to data generated when a credit or debit card is used to purchase goods or services from a business. To protect privacy, individual cardholders are anonymized and transactions are aggregated.
Card transaction data can include more than just consumer credit cards. The data can be derived from all kinds of cards, including debit cards, small business cards, corporate cards, government benefit cards, and charge cards. It can also include digital transactions — also known as “card not present” transactions.
Where Does Card Transaction Data Come From?
Transaction data provided by data companies can come from a variety of sources. Data may come from a bank integration, or be aggregated by a card issuer, a credit card network, or a payment processor.
When working with transaction data, it’s crucial to understand what kind of source it comes from. Many sources skew toward certain groups of consumers, geographic areas, or types of transactions. Knowing the size of the sample and any biases in the data source enables you to better understand how to derive trustworthy insights.
Raw transaction data is notoriously difficult to analyze. In its raw form, the data is messy, inconsistent, and sometimes duplicative, requiring organization and cleanup at scale before it’s ready to use for insights.
The Entity Resolution Problem
A real-world example: different payment processors refer to the same business — a coffee shop called Bodhi Leaf in Orange, California — as “Bodhi L,” “Bodhi Leaf Coffee,” “Bodhi Leaf Coffee Traders,” “Bodhi Leaf Trading Company,” and “Bodhi Leaf Tradi.”
Uniting this data into a holistic view of transactions at a business level requires sophisticated algorithms and entity resolution techniques to clean and match the data. This is a core part of what Enigma does: our dataset aggregates and matches raw transactions to more than 10 million U.S. businesses.
How GTM Teams Use Card Transaction Data
Historically, card transaction data was used as a bellwether for consumer trends. Recently, it’s been recognized that this data can also provide valuable insights about the health of a business. Looking at trends in card revenues, transaction volumes, and customer concentrations can reveal whether a business is growing or declining. When aggregated by business, this data is often referred to as “merchant transaction data.”
Card revenue does not reflect all of a business’s revenue, but the accelerating shift away from cash makes it an increasingly reliable signal. Merchant transaction data is especially helpful for businesses operating in industries where a high proportion of transactions are made by card — retail shops, restaurants, and service providers in particular.
Four Ways GTM Teams Are Using This Data
Build custom ideal customer profile lists. Instead of manual research, you can use revenue trends to identify fast-growing businesses and prioritize prospecting targets with criteria relevant for your business — industry, revenue, or growth metrics.
Qualify leads. Visibility into your leads’ revenue trends helps you improve lead segmentation and scoring for better ROI on campaigns. Signals in this data — say, no transactions present in the past 12 months — can also help you remove closed businesses from your database, so you’re not wasting money sending direct mail to an empty storefront.
Improve segmentation. When transaction data is packaged with identity data, monthly intelligence helps you fill in and clean up your database. Better segmentation means higher response rates and less wasted spend on unqualified businesses.
Grow revenue from existing customers. Card transaction data is a valuable tool for identifying cross-sell and upsell opportunities with existing customers. Deep insights about a business’s revenue enable you to market the right products at the right time.
What to Consider When Selecting a Transaction Data Source
When evaluating a card transaction dataset, the right questions help you compare options and understand which dataset best suits your needs.
Latency. How fresh is the data? How frequently is it updated?
Coverage. How many cards are included in the panel? Is it just credit cards or debit cards as well? How many businesses are covered?
Panel bias. What is the scope of the panel? Is it just Visa or just Mastercard? Is it skewed toward certain geographies or income classes?
Permissioning. Some data providers require you to get permission from a business before accessing its transaction trends. Others — like Enigma — have already integrated privacy protection into their system so that you can immediately access data about any business.
The Data: What Enigma’s Merchant Transaction Signals Include
Enigma’s card transaction data is derived from a panel of more than 750 million anonymized credit and debit cards — across types like general purpose credit cards, consumer and small business debit, small business credit, health savings and flexible spending accounts, gift cards, and more. It’s matched to more than 10 million U.S. businesses and refreshed monthly.
| Data | Description |
|---|---|
| Card Revenues | Monthly revenue a business receives from credit and debit card transactions |
| Card Revenue Growth | How card revenue is trending over time, with seasonally adjusted and non-seasonally adjusted views |
| Card Transactions | Monthly number of credit and debit card transactions |
| Card Transactions Stability | Distribution of transactions over time — how many days, weeks, or months saw purchases |
| Customer Counts | Average number of daily customers based on card transactions |
| Card Refunds | Refunds issued to credit or debit cards, including ratio of refunds to total revenue |
For a full breakdown of every available field, see the Enigma Data Catalog.
Putting It to Work
Whether you’re headed into a challenging market or it’s clear skies ahead, reducing costs and fueling growth are business imperatives. GTM teams that rely on timely, accurate small business intelligence for marketing and sales decisions will be best positioned to make smart investments — in their budgets and in their time.
Risk teams looking for a related perspective can read the guide to mitigating risk with card transaction data.
Ready to see what Enigma’s card transaction data can do for your pipeline? Get in touch.