One in five letters
went to a business that was gone.

Direct mail is central to this provider's acquisition strategy — more than $25 million of campaigns a year. The team starts from a list of more than 30 million U.S. businesses, then segments by industry and rough revenue estimates to cull it to the 5–10 million highest-value prospects.

Under pressure to improve campaign performance, the data and analytics leader had two questions: how could they better identify closed or distressed businesses to reduce wasted spending, and how could they better qualify prospective customers?

Improving segmentation accuracy would directly affect top-line revenue, so both questions were really the same question.

Each month Enigma sends a list of several million merchants and their financial health metrics.

The client matches it to its existing marketing list — appending data for prospects already there, and adding new prospects to the database.

The client then runs its own prioritization models to segment the list for campaigns. Nothing about the existing process had to be replaced.

With Enigma's transaction stability data, the client discovered that 20% of the businesses it was mailing had ceased operations or were in financial distress — businesses that would never have made it through underwriting. Two million closed businesses came out of the lead database, avoiding $5M in wasted marketing dollars.

Growth rate data cut the other way: it identified 200,000 high-growth and high-spend businesses, more likely to pass underwriting and more profitable once through. Hundreds of thousands of them weren't on the company's marketing list at all and had been left out of campaigns entirely.

On the underwriting side, the team saw a 33% increase in approval rates for applicants arriving through campaigns that used the refreshed list process.