The problem

A database full of members. No way to know which ones were ready to borrow.

Advia Credit Union’s mission is to provide financial advantages to its members at every stage of their financial lives — from auto loans to mortgage refinancing to debt consolidation. They had the products. They had the members. What they didn’t have was a reliable way to identify which members were actively in the market for their next financial product right now.

Their marketing approach was the same one most financial institutions default to: broad campaigns to large audience segments, hoping the message landed at the right moment for the right person. The benchmark response rate they were working against was 1.19% — meaning for every 100 members who received their auto loan campaign, fewer than 2 completed an application. The rest either weren’t in the market, had already borrowed elsewhere, or simply ignored the outreach as irrelevant to where they were financially.

The cost of that irrelevance adds up fast. Every dollar spent reaching members who weren’t ready was a dollar not spent on the ones who were. And with the average American holding 8 financial products — only 19% of whom have 3 or more with their primary institution — the revenue sitting idle inside their own member base was substantial. They just couldn’t see it.

The Obstacle

Traditional segmentation tells you who your members are. It doesn’t tell you what they need next.

Conventional financial marketing relies on first-party behavioral data: past transactions, product history, account activity, engagement with previous campaigns. These signals are useful but inherently backward-
looking. They tell you what a member did, not what they’re about to do.

A member who refinanced their mortgage two years ago and hasn’t interacted with an email in six months looks identical to a member who is actively researching auto loans today — if you’re only using internal data to evaluate them. Both have the same account age, the same transaction history, the same open rate. First-party data can’t distinguish between them because neither has signaled intent through the credit union’s own channels yet.

Advia needed a way to identify which members were showing financial readiness and borrowing intent in the real world — not just inside the credit union’s ecosystem. That required data that went beyond anything they could observe on their own.

Why Lead Intelligence?

Predicting who will borrow next — before they tell you.

Lead Intelligence works by cross-referencing an institution’s existing member database against an identity graph covering 1,500+ financial, behavioral, demographic, and lifestyle attributes across nearly every adult consumer in the United States. For each member, the platform runs a predictive model that estimates their likelihood of completing a specific financial product application — in this case, an auto loan — based on patterns found in historical borrower data combined with external behavioral signals.

The output is a scored, ranked list of members: the individuals whose current offline financial behavior most closely resembles the profile of someone who is actively in a borrowing window. Instead of broadcasting an auto loan campaign to tens of thousands of members and hoping for a 1.19% response, Advia could focus their entire campaign on the top percentile of members most likely to convert — while the rest of the database remained untouched until they entered their own window.

The approach also solved the “wallet share” problem at its root. Rather than asking “which of our members could theoretically use an auto loan?”, Lead Intelligence asked “which of our members are most likely to take one right now?” — a fundamentally different question with a dramatically different answer.

The results

$1.1MM in 30 days. $2.7MM in 90 days. More than double their benchmark conversion rate.

Advia used Lead Intelligence to score their full member database for auto loan propensity, then built a targeted audience from the highest- scoring members. That audience became the sole focus of their auto loan email and direct mail campaign — no broad blasts, no wasted impressions on members who weren’t ready.

Their benchmark for a successful campaign was a 1.19% response rate. What Lead Intelligence delivered was something else entirely.

30 Days:
2.41% response rate — $1.1MM in new auto loans closed

90 Days:
5.18% response rate — $2.7MM in auto loans closed

The response rate didn’t just exceed their benchmark — it more than doubled it within the first month, and kept climbing as the campaign ran. By day 90, Advia was converting members at 5.18% — more than four times their original baseline — because every dollar of campaign spend was focused exclusively on the members who were already in a borrowing window, not the ones who weren’t.

The campaign generated $2.7MM in closed auto loans without acquiring a single new member. Every dollar of that revenue came from existing relationships that previously had no way to surface their readiness.