Case study
Helping a debt relief company achieve 5,900% lead lift with stable CAC
The Opportunity
Training algorithms on enrollments for scalable growth
The client, a leading U.S. debt relief company, sought to radically scale its enrollments. While their brand was trusted, digital acquisition was stuck at roughly 500 MQLs per month, nowhere near the growth they needed to achieve their targets.
Progress was boxed in on three fronts: strict financial-advertising policies on Google and Meta that limited targeting and segmentation; deep consumer skepticism about debt relief that suppressed conversion; and a patchwork of fragmented campaigns spread across states and channels with no cohesive optimization framework.
The mandate became clear: build a modern, signal-rich growth engine that could carry a prospect from first impression to enrolled customer within the rules and do it by training platform algorithms on what actually drives value—enrollments and payback—rather than chasing cheap clicks.
At the time, we had no historical data. Acquisition was stuck at roughly $500/day in media spend, skepticism was high, targeting was constrained, and stale creative throttled learning. The new system was designed to turn those constraints into inputs, feeding better signals into the algorithms and unlocking scalable, profitable growth without inflating cost per lead.
The Solution
Transforming the end-to-end process
To get there, the team reframed the goal from “generate more leads” to “teach the platforms what a great customer looks like.” That meant architecting data feedback loops to optimize bidding toward enrolled customers (not just form fills), consolidating media into structures that accelerate learning, and re-sequencing creative, state segmentation, and measurement so every step compounded.
The solution was to rebuild the entire acquisition engine—from creative to channels to bidding to conversion—so every step taught the next and collectively drove down effective CAC. We anchored media in Google Ads/Performance Max as the spine, unifying search, video, and display, and piping first-party enrollment events back into Google so the system optimized to revenue quality rather than form fills. In parallel, we deployed social suites across Meta, YouTube, and TikTok that deliberately “trained the algorithm”: a rotating mix of testimonials, explainers, UGC, and AI-generated variants kept freshness high and creative fatigue low. With AI creative at scale, we produced hundreds of copy/visual/video permutations each week, running rapid kill/scale loops to compress time-to-signal.
On the decisioning side, we shifted bidding from generic leads to enrolled-customer goals, feeding in state eligibility rules, debt bands, and historical payback so smart bidding could sharpen predictions. State segmentation became a growth lever: campaigns mirrored regulation, propensity, and ops capacity by state, letting platforms optimize inside smaller, cleaner pools. To convert the traffic we earned, we added AI chatbots on landing and conversion pages for real-time Q&A at critical moments—eligibility checks, appointment scheduling, and warm hand-offs to human closers—while relentless funnel testing (headlines, forms, CTAs, latency, layout, follow-ups) pushed every win back into platform learning. Finally, we monetized non-enrollees by routing ineligible or declined leads to vetted partners in loans, insurance, and credit repair—offsetting CAC and lifting total funnel yield—so the system scaled faster, learned faster, and paid for its own experimentation.
The Impact
Algorithm Led Gains in Debt Relief: Enrollments, Not Clicks
Lead growth: 500 → 30,000/month; algorithms trained on enrollments and payback drove compounding gains.
Media scaled from $500/day to $10,000/day, holding CAC within target
Landing page conversion from AI chat, funnel testing, enrollment bidding, and state-smart segmentation.
The client focused on what matters—profitable enrollments, a faster prospect experience, and smoother advisor workflows—and measured every step. By wiring dashboards to real enrollment/payback data and codifying eligibility in the bot → scheduler → CRM flow, the team made decisions in minutes, not weeks.
That automation cut handoffs from ad → chat → appointment → advisor by weeks, and decisioning logic routed each lead to the right queue (or to partner offers if ineligible). Result: less media waste, higher lead-to-enroll, lower CAC, and happier sales staff.
This improved operational efficiency and customer lifetime value. Trust and intent (brand-lift) also climbed. With clearer tools and signals, sales and advisor teams worked faster and were happier with engagement scores at an all-time company high.
Lessons Learned
Strategic Roadmap
Code the system for enrolled outcomes
Rebuild the path end-to-end—Google Ads/PMax as the spine, social for scale, LPs with AI chat, CRM/dialer, and advisor handoff—so every touch feeds enrollment and payback signals back into bidding. Standardize UTMs/asset IDs, lock tracking + disclosures, and carry decisioning eligibility metadata across platforms so the algos learn from the right outcomes, not cheap clicks.
Operating Model
Put advisors & compliance in the driver’s seat
Design the pipeline with advisors, compliance, state ops, data, and media from day one. Encode eligibility rules, disclosures, and talk-tracks into on-page/chat UX and CRM automations. Advisors ensure engineering mirrors how teams actually qualify and enroll, so campaigns launch geo-ready, reduce rework, and keep routing accurate as spend scales.
More Enrollments
Win on what matters: CAC, payback, throughput
Optimize for enrollments per dollar, not form fills. Use state-smart segmentation, enrollment-optimized bidding, and AI variant testing to lift conversion and cut waste. Shorter handoffs, faster scheduling, clearer scripts, and compliance-safe copy stack together—compounding gains in CPL/CAC, lead-to-enroll, and time-to-first-learning across channels.
Adoption & Scaling
The job isn’t done until CAC & LTV move
Ship systems + a KPI tree, not tools. Dashboards stitch media → site/chat → CRM/dialer → enrollment/payback, tracking quality, speed, reuse, LTV/CAC, and non-enrollee monetization. If CAC stalls or payback slips, we adjust signals, segments, or handoffs—and prove impact via enrollment lift, stable $10k/day scale, and durable 3× conversion from AI chat + funnel tests.
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