How a Fintech Brand Replaced Gut-Feel Decisions With Predictive Audience Targeting

In early-stage fintech marketing, instinct is often the default compass. When you're moving fast, launching products, and competing against legacy banks with ten-times your budget, you make calls based on what feels right — the channel that worked last quarter, the audience that converted last week, the copy angle your CEO liked. Claris Financial was no different.

Founded in 2021, Claris provides embedded lending infrastructure for mid-market SaaS companies. Their ideal customer is a CFO or VP of Finance at a Series B–D tech company looking to offer credit products within their own platform — a narrow, highly specific profile in a noisy market. For two years, their growth team ran campaigns on LinkedIn, Google, and a handful of fintech newsletter sponsorships. Results were inconsistent. CPAs were high. The pipeline quality was erratic. And nobody could really explain why some campaigns worked while others didn't.

The Problem with Gut-Feel Marketing

The Claris team wasn't making irrational decisions — they were making decisions with incomplete information. Their CRM held 18 months of closed-won and closed-lost deal data. Their ad platforms held three years of campaign performance. Their product analytics held detailed behavioral data on trial users. But none of this lived in the same place, and nobody had the bandwidth to synthesize it manually.

The result was what Claris's Head of Growth, Tarini Mehta, called "post-rationalization marketing" — running campaigns based on assumptions, then finding reasons why the data supported those assumptions after the fact. When a LinkedIn campaign underperformed, the team would attribute it to audience size, or creative fatigue, or targeting too broadly. When a campaign overperformed, they'd scale it immediately — often exhausting the audience before they understood why it worked.

68%reduction in cost-per-acquisition3xmore pipeline from identified channels8 wksto first measurable results$2.1Min influenced pipeline within 6 months

Connecting the Data Dots with Reacusto

When Claris onboarded with Reacusto, the first phase was a data integration sprint. Reacusto's connectors pulled in historical performance data from LinkedIn Campaign Manager, Google Ads, HubSpot CRM, and Segment — all mapped into a unified attribution schema. Within 72 hours, Reacusto's AI layer began building predictive audience profiles by cross-referencing campaign touch data with deal outcomes.

What emerged surprised the team. The contacts who converted fastest and had the highest contract values weren't responding to the broad "embedded finance" messaging Claris had been using on LinkedIn. They were responding to very specific trigger events: companies that had recently closed a Series C, had headcount between 150 and 400, were in the vertical SaaS space, and had a VP of Finance who had changed roles in the last 12 months. These weren't personas the team had explicitly built — Reacusto surfaced them by correlating attributes that appeared disproportionately in closed-won deals.

"We thought we knew our ICP. We had the slides, the personas, the messaging frameworks. But the data told us something different — and more specific — than anything we had written down."

The Three Hidden Channels

The second major revelation was channel performance. Reacusto's multi-touch attribution model revealed that three channels were driving a disproportionate share of assisted conversions — but were invisible in the team's last-touch reporting:

1CFO-focused newsletter sponsorships — A small cluster of B2B finance newsletters (combined reach under 40,000) was generating 22% of all first-touches for closed-won deals, despite being a minor line item in the budget. These weren't fintech publications — they were operational finance newsletters read by controllers and VP Finance profiles who would later champion deals internally.2Retargeting on G2 comparison pages — Visitors who had viewed competitors on G2 and then visited the Claris site were converting at 4.2x the rate of cold traffic — but weren't being retargeted with dedicated messaging. Reacusto flagged this segment as a high-intent cohort that needed a differentiated creative track.3LinkedIn matched audiences from CRM segments — Claris was uploading full CRM lists to LinkedIn for matched audiences, but Reacusto's analysis showed that only the Segment C (Series B–D vertical SaaS with recent leadership change) was converting at a positive ROI. The other segments were diluting budget with no closed-won attribution.

Implementation: From Insight to Execution

Armed with these findings, the Claris team restructured their Q3 plan entirely. They tripled investment in the newsletter sponsorships, created a dedicated retargeting flow for G2 visitors with competitive comparison content, and rebuilt their LinkedIn matched audience strategy around the high-signal segment. Within six weeks, CPA began dropping. Within eight weeks, it had fallen 68% from baseline.

But the more durable outcome was structural: Claris now runs all campaign decisions through Reacusto's predictive dashboard. Before any new channel or campaign is approved, the team simulates expected performance against historical patterns. "We stopped asking what feels right," Mehta noted. "We started asking what the data predicts — and then designing creative around that."