Discovering $8.9M in Revenue with Smarter Prospect Lists

The Problem: Prioritizing the Wrong Leads

A consumer financing company was faced with lower sales ROI than expected. As they dug deeper, they uncovered the root cause: they’d been prioritizing the wrong leads.

Their existing lead sources were providing many leads outside their ideal customer profile, wasting their sales team’s time on merchants who would never qualify for financing or generate meaningful lifetime value. The sales pipeline was busy — but busy with the wrong prospects.

The cost wasn’t just wasted sales effort. Every hour spent pursuing a merchant outside the ICP was an hour not spent on a high-value prospect who could drive real revenue growth. The opportunity cost was invisible but substantial.

The Approach: Transaction Data Meets Prospect Lists

The team’s insight was that traditional prospect list criteria — industry, location, employee count — weren’t sufficient to predict which merchants would be good financing customers. What they needed was visibility into actual business economics: revenue, transaction patterns, and growth trajectory.

By leveraging Enigma’s Merchant Transaction Signal data, the team built customized prospect lists that incorporated real economic signals. Rather than targeting “restaurants in Texas with 5-20 employees,” they could target “merchants with $500K-$2M in annual card revenue showing stable quarter-over-quarter growth.”

The customization was specific to their financing products:

• Revenue thresholds aligned with minimum loan amounts and expected repayment capacity
• Transaction stability signals indicated consistent cash flow, reducing default risk
• Growth patterns identified businesses likely to need expansion financing

The Results: $8.9M in Incremental Revenue

The impact of better prospect lists was dramatic and measurable.

The $8.9M in incremental revenue didn’t come from a larger sales team or more aggressive outreach. It came from redirecting existing sales effort toward merchants whose business economics matched the company’s ideal customer profile.

The Math of Better Lists

Consider the economics. If a sales team processes 1,000 leads per quarter, and switching from generic to customized lists improves lead quality by even 20%, the downstream impact compounds through every stage of the funnel: better conversion rates, higher average deal size, lower default rates, and ultimately more revenue per sales hour invested.

The $8.9M figure represents the cumulative impact of this compounding effect — starting at the top of the funnel with which merchants the team chose to contact in the first place.

Key Takeaways

1. Lead quality trumps lead quantity. The sales team didn’t need more leads — they needed better leads. Transaction data provided the filter they were missing.

2. Revenue signals predict financing fit. A merchant’s actual revenue and transaction patterns are far stronger predictors of financing suitability than firmographic data alone.

3. Customization is the difference. Generic prospect lists from data brokers cast too wide a net. Lists customized with transaction signals match the specific criteria of your products.

Technical Specifications