[DNB] Dun & Bradstreet
If all you knew about Dun & Bradstreet was that it sold mission critical data on more than 520mn companies, that all this data was linked to its widely referenced proprietary identifier, that 96% of its customers retained every year and were mostly monetized through high margin subscriptions, you might be tempted to comp to a peer set of profitable data and benchmarking stalwarts like Moody’s, Verisk, or CoStar. But this is a case where naive pattern matching would lead you wildly astray.
Dun & Bradstreet was formed in 1933 through the merger of two commercial credit agencies, Lewis Tappan’s Mercantile Agency (later sold to Benjamin Douglass) and the John M. Bradstreet Company, both founded in the 1840s to assess the ability and willingness of businesses to repay their obligations. You can think of Dun & Bradstreet as the commercial analog to consumer credit bureaus like Equifax and Experian who, as I discussed in FICO and the Consumer Credit Bureaus: Part 1:
…effectively function as managers of data coops. Just as 100 friends, each with a separate piece to the same puzzle, will have a more comprehensive understanding of the puzzle by joining their pieces together, lenders mutually benefit from combining the payment histories of their customers. Each “puzzle piece” is separately of little worth but collectively valuable. Lender B in Cleveland wants to know if Customer 1 is has already taken out a big loan from Lender A in Portland. And just as you would rather see a puzzle that is 3/4 of the way complete than one that has just started, a lender will find it more valuable to access and contribute to a national bureau database that captures payment data from every lender in the country than one that only gathers repayment data from, say, 10 banks in Chicago. Centralizing data also acts as a stick, since if a consumer knows that his payment behavior is going to be captured no matter what – that a bank in New Jersey will be privy to his delinquent loan in Montana – he will be more likely to stay current on his obligations.
Likewise, the proprietary kernel of Dun & Bradstreet’s formidable dataset comes from the contributions of its clients, mostly large enterprises, who extend trade credit to customers. Vendor A sells to retailer B on credit, expecting to be repaid in 30 days. When B fails to do so, A reports the delinquency to Dun & Bradstreet, and other vendors who use Dun & Bradstreet data to evaluate customer credit risk now know to think twice before extending trade credit to B. Relying on a centralized third party like DNB to gather delinquency data and turn that data into predictive credit scores reduces redundancies at the system level, since otherwise each vendor would need to individually evaluate the creditworthiness of their customers…and even then their parochial assessments would be an incomplete version of what’s on offer from DNB, who hoovers trade credit data from thousands of sources and consolidates its findings into a Paydex score, which you can think of as a FICO score for businesses.
Dun & Bradstreet has a rigorous process for not only collecting data and scrubbing it for accuracy but tying seemingly unrelated entities together so that clients have a more complete and accurate picture of their receivables concentration. A pet food processor could have one department extending credit to Cat Food and another department extending trade credit to Dog Food without realizing that Cat Food and Dog Food are actually wholly owned subsidiaries of the same retail holding company, Pet Food Inc. On the flip side, a pet food retailer that fills its shelves with products from both Cat Food and Dog Food and makes up a significant proportion of those two subsidiaries’ combined revenue, can push on Pet Food Inc. for better terms. Or the retailer might see that Pet Food is trending toward a financially precarious position and line up a second supplier, just in case. Or the pet food processor and the pet food retailer might see that Pet Food Inc. is itself majority-owned by a shell company commandeered by a shady Russian oligarch who they shouldn’t be doing business with.
DNB data can be used not only to manage risk but also to generate revenue. A salesperson at the pet foods processor sees that Pet Food owns another financially viable subsidiary, Pet Toys, that they can cross-sell to, with DNB also supplying the contact information of the guy in charge of procurement there.