Each week, I’ll document how I’m using AI as a marketing co-pilot at Rezerv.

Biggest mistake this week?
Step 3 – Drafting the Landing Page.

Started with cold traffic paid ads on Meta.

• SG hit 12.5% CR (solid).

• But 2 countries got zero leads (not great).

Here’s the actual system I built to launch and improve paid ads with AI at Rezerv.

Share 10x Playbooks

Why Paid Ads for Rezerv?

• Churn < 5% → Once a studio is onboarded, they rarely leave

• Strong LTV → Most stay for over a year

• Demo-to-paid ~50% → Product delivers, SDRs convert. The funnel works when people enter it

But Here’s the Gap:

• Homepage branded/organic traffic converts at 0.5%

That’s super low. And to me that’s not a traffic issue. It’s a positioning and copy issue.

Why Paid Ads Make Sense:

They let us control the narrative, tailor messaging by ICP, and drive high-intent traffic into a dedicated funnel that actually converts.

System Snapshot: AI-Powered DR Ad Loop

Objective
Build, test, and iterate cold traffic funnels using AI tools and DR frameworks.

Inputs

• Psychographic ICP profile (via NotebookLM + Perplexity)

• VoC clusters from G2, Capterra, Trustpilot

• Direct response copy frameworks (PAS, AIDA, Enemy Hook, etc.)

Process

1. Research and extract ICP fears/desires

2. Cluster VoC to identify dominant pain

3. Feed pain into ad copy + visual prompt generator

4. Route high-performing hooks to a pain-specific LP

5. Measure CTR/CR + annotate feedback

6. Update prompt framework or LP structure based on weak point

Results (7 Days)

• 3%+ CTR on cold ads (signal: hooks/visuals resonated)

• <2% cold traffic → LP → Booked Demo CR (signal: landing page mechanism weak)

Inside Step 1: Deep ICP Research

• Open NotebookLM

  → Prompt: “I want to research first-time fitness studio owners and build a psychographic profile.”

• Review the sources pulled
  → Prioritize articles, interviews and YouTube transcripts from studio founders

• Extract patterns
  → Fear: tech overwhelm, data loss, churn
  → Wants: peace of mind, bookings on autopilot, human support
  → Drivers: full classes, clean UX, control

🧠 Why: Your ICP’s pain will shape the landing page, ad copy, and ad visuals. Paid ads only work if the message hits where it hurts.

Inside Step 2: Pulled VoC Data

• Go to G2, Capterra, or Trustpilot → search competitors

• Copy reviews manually or scrape with Apify

• Drop all reviews into a Google Sheet

• Upload the sheet into ChatGPT
  → Prompt: “Cluster the top recurring pain points. Prioritize by intensity and frequency.”

🧠 Why: VoC (Voice of Customer) data strengthens your ICP research and reveals the one pain your audience actually cares most about 

Inside Step 3: Drafted Landing Page (Biggest Mistake)

I made a classic mistake here.

I asked ChatGPT to write the headline using “DR best practices” but didn’t give it actual structure or examples.

What went wrong:
❌ No copywriting framework
❌ No guidance beyond “use the main pain”

The result? Decent copy, but it missed critical elements:

• Big transformation

• Specific timeframe without common pain

• Clear mechanism

What I should’ve done:

• Given this structure: Get [Transformation] in [Timeframe] Without [Pain] — Powered by [Mechanism]

• Fed it proven headline examples

Inside Step 4: Generated Ad Copy

• Used ChatGPT to generate 15 cold traffic hooks across Pain, Enemy, Curiosity, and Identity types

• Paired each hook with body copy using PAS, PASTOR, or AIDA frameworks

• Shortlisted top 3 based on clarity, emotional intensity, and angle-message match

For the copy, I edited key elements like the opening hook and audience callout to sharpen the message.

🧠 Why: These hook types and copy frameworks are generally proven to convert cold traffic. 

Inside Step 5: Generated Visuals

• Used ChatGPT to brainstorm 1–2 visual ideas per top hook (e.g. WhatsApp bookings, admin chaos)

• Generated the images directly in ChatGPT

• Made minor edits in Canva for brand consistency (top = AI, bottom = edited)

🧠 See the actual copy + visual used:here & here

What the Data Told Me?

• Copy and visuals hit → CTRs across most markets were 3%+, which means the pain-based messaging resonated

• LP mistake hurt conversions → missed key DR elements (no big transformation, no clear mechanism), which likely caused the drop to <2% Booked Demo CR in most markets.

Next Steps

• Fix the landing page
  (Already done. Details coming in Week 2 breakdown)

• Broaden the ICP to increase spend
  Meta campaign is underspending

  → Instead of targeting Pilates studio owners only, expand to fitness studio owners
  → Update LP and ad copy to match the broader audience

• Test different ad angles and copies

  → Launch five AI-generated angles based on VoC (Zero-Admin, Price-Slash, Migration, Human Support, Revenue Boost), run 3-day split tests, kill any ad below 3 % CTR, and scale the winners.

• Plan an AI evaluation workflow to catch mistakes

  → Build a prompt-based checklist agent to review LPs and ad copy

Wrap Up: My Journey Using AI for Marketing

Just an honest and transparent sharing of how I actually used AI to solve real marketing problems.

Because I think we need more open, practical sharing in this space.

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

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