📌 Case Study

From Chaos to Control: Optimizing CAC through Structured Media Buying

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🧩 The Problem

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My company was struggling to find a dependable marketing agency. While we had a CMO, his bandwidth was stretched too thin to take a deep dive.

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That’s when the CEO—knowing I had a good grasp of numbers—asked me to take charge of performance marketing on Google and Meta.

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🎯 North Star Metric: Bring Customer Acquisition Cost (CAC) down significantly.

I wasn’t alone—I had a talented copywriter, a designer, and a supportive CMO. Together, we rebuilt the foundation.

🔍 Step 1: Data Deep-Dive

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We began by analyzing two years' worth of campaign data, looking at:

• Ad copy, headlines, and creatives

• Audience targeting

• Offers and landing pages

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We correlated ToF metrics (like CTR, CPC) with MoF outcomes (signups, bookings).

💡 Key Insight:
There was weak correlation between top-of-funnel metrics and actual conversions. Most agencies had been optimizing for CPC/CPM—but with poor pixel data, their strategies were bound to fail.

🧪 Step 2: Building a Testing Framework

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To course-correct, we focused on testing one variable at a time, while simultaneously fixing our pixel infrastructure to unlock deeper-funnel tracking.

Here’s how we structured it:

• Sourced 40+ images (stock + iPhone), and ran dynamic creative ads

• Tweaked last year’s best copy to fit our updated user personas

• Built multiple LLA (Lookalike Audiences) using a proxy for high-intent members

• Ran 20+ landing page A/B tests

• Focused on capturing cheap email opt-ins while deeper funnel metrics stabilized

📈 Step 3: Initial Results

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Within 3–4 months, we saw significant traction:

• Landing page conversion rate doubled

• Funnel leaks were plugged through constant iteration

• Support team insights led us to revamp the offer for clarity

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This clarity came at a short-term cost:
⚠️ Signup cost increased by 70%, but…
✅ Mid-funnel and bottom-funnel performance improved dramatically