Cold Funnels Built for Speed: How AI Helped Us 4x Leads and Cut CAC by 60%
Built this to stop burning CAC on cold traffic.
Most resource-thin SaaS teams burn CAC by sending cold traffic to their homepage.
We did too, until we built a cold funnel that 4x’d leads in 5 days.
Here’s why most homepage fail with cold traffic ads:
SEO-first copy → written to rank on Google, weak message match with ads.
Generic messaging → tried to speak to everyone, not one ICP
No clear offer or urgency → no strong CTA, no reason to act now
Too many exits → nav bar, links, footer etc
Paid traffic is expensive and it needs a controlled funnel. A homepage just isn’t one.
Here’s our cold traffic funnel metrics after 5 days of running ads:
+400% leads
60% lower CAC
3.2% booked demo conversion rate (industry average is 1-2%)
How We Engineered the LP for Cold Traffic That 4x’d Paid Leads
1. Match audience awareness
They weren’t shopping for software. They were stuck in admin pain. The LP spoke to that.
2. Create a category of one
We positioned Rezerv as a Zero-Admin Studio Setup and not another tool.
3. Lead with speed and ease
Cold traffic buys simplicity. We promised live in 24 hours, zero tech needed.
4. Stack proof to reduce risk
Social proof and testimonials focused on fast, easy launches and not product depth.
5. Drive urgency
Real capacity limits (“5 slots this week”) moved studio owners to act.
Where AI Helped?
Here’s exactly how we used AI to go from raw customer data to a cold funnel in 2 days, not weeks.
The workflow:
→ Surface customer insights (synthesis)
→ Generate copy variations (generation)
→ Assemble full pages (assembly)
→ Simulate ICP feedback (feedback)
Synthesis of customer data.
Fed scraped data (G2, Trustpilot, Capterra) into AI to surface patterns in customer pain points, wants, needs, and desires.
From scraping 300+ reviews, AI identified a surprising pain point I hadn’t focused on: ‘wasting hours on WhatsApp back and forth.’ This became our #1 hook on the LP.
Copy bulk generation.
Used AI to generate 10–20 variations of each landing page component such as headlines, CTAs, benefit stacks, and more.
In headline generation, AI consistently gives me better outputs when given customer pain quotes as direct input vs. generic product positioning prompts.
Landing page bulk assembly.
Asked AI to mix and match component variations and output 7 complete landing page drafts with the highest potential.
I noticed that it's best to provide AI with the exact section order to follow. Otherwise it often creates disjointed flows.
Simulated ICP feedback.
Asked AI to roleplay as the target ICP and rate the 7 drafts (1–10).
This surfaced the strongest contenders and gave me a clearer signal on which versions to refine.
Really like using this feedback step as it gives me a fresh ‘pair of eyes’ on the drafts. One small tip: always do this in a clean new chat to avoid context from affecting the results
Where the human (me) came in.
I selected the top 3 drafts based on scores.
Then hand-picked the best components across them to build the final landing page that was launched to paid traffic.
AI gave me the drafts. But choosing to lead with admin pain, position Rezerv as a category of one, surface proof of ‘launch in 24 hours,’ and downplay deep product features, that was 100% human judgment.
Lessons
Not every part of this was AI-driven.
The biggest lift came from structuring the LP to match audience awareness and making the offer feel faster, easier, and safer.
AI helped us iterate faster and surface stronger combinations, but the core marketing principles still mattered most.
Wrap Up: Fundamentals First, AI Second.
This wasn’t about some AI trick.
It was about doing the fundamentals right, audience, narrative, offer, proof and using AI to speed up what works.
I test AI workflows on real marketing problems.
And turn the useful ones into 10x Playbooks.
So business owners and solo marketers doing it all themselves can skip the guesswork… and copy what actually works.
→ I drop one of these 10xPlaybooks 🚀 every week, don’t miss the next.
John
Love the part where you scraped data from G2, Trustpilot, and Capterra to analyze and find patterns. This way, we can proactively use data from reliable, up-to-date sources instead of relying entirely on AI’s built-in knowledge.