To find and prioritize your ideal small and medium business (SMB) customers, getting visibility into their financial health is key.
An accurate view of an SMB’s financial health helps you tailor your products and services offering for best fit – whether that’s approving or extending a line of credit, customizing your marketing efforts, or focusing Sales’ attention on your fastest-growing customers.
For certain types of businesses, card revenues – income from card-based transactions – can give you an incredibly accurate portrait of a business’s overall financial health.
As we continue to improve the accuracy of our card revenue data, we look for publicly available, reported company revenue data to validate our monthly card revenue data. Reported revenue data tends to be about large, public companies, and our specialty is small businesses. But we apply the same data science techniques to the bigger brands in our dataset, so the accuracy will be on par with the millions of small companies we cover.
We took a look at reported revenues for a handful of different companies you’d recognize and found Enigma’s card revenue data was strongly correlated with reported revenues. Here’s how our data compared.
Reported revenue for an online furniture and home decor retailer showed a gradual increase from the second quarter of 2018 to Q1 2020, a dramatic, near-doubling spike in Q2 2020, followed by a general tapering off, with a minor spike in Q2 2021. Enigma’s card monthly revenue data closely mirrored this pattern, with a strong correlation of .9940.
A high-end sportswear retailer sells clothing and gear online and in stores. The company saw recurring revenue spikes in the fourth quarters of 2018, 2019, 2020, and 2021—a pattern reflected in a strong correlation with Enigma’s card revenue data (.9533).
Looking back to the start of 2018, a certain sporting goods retailer saw its biggest revenue dip in Q1 2020, with a quick rebound in Q2 2020 back to Q4 2019 levels. Like the online furniture and home decor retailer, the sporting goods retailer also saw fourth-quarter spikes each year. Our card revenues were strongly correlated with reported revenues for this business (.9612).
Revenues for a fast casual Mexican restaurant chain followed the smoothest upward trend line of the sample bunch, starting at nearly $1.3 billion in Q2 2018 and gradually climbing to $2 billion by the first quarter of 2022. There was a strong .9962 correlation between the restaurant chain's reported revenues and Enigma’s card revenues.
The revenues for these sample companies may be far larger than those of your small business customers. But the data science process we use to aggregate their revenues is the same, so you can expect high accuracy in our small business revenue data.
The results mean that, like the risk and underwriting teams who rely on our data for high-stakes credit decisions, you’ll have the timely signals you need to approach decisions about your small business prospects and customers with confidence, like which of my customers are growing fastest? Which are in distress? On which accounts should our Sales reps invest their time?
Revenue data in the market tends to fall into two buckets:
Enigma’s card revenue data comes from actual debit and credit card transactions at a business. To build a full picture of a business’s card revenues, we aggregate the revenues from all of that business’s physical locations, plus any online transactions. Revenue from third-party resellers may not always be captured.
Card revenue data is especially accurate for industries where card payments are common, like restaurants and retail. For industries that rely heavily on other payment methods, like ACH transactions, card revenues will be a lower percentage of total revenue, but can still be a helpful indicator of growth trends.
What could you do with more accurate financial data about your small business customers and prospects?
Learn more about how our customers are using Enigma data for customer management, improving underwriting models, sales prioritization, and building better prospect lists for go-to-market teams.
