Establishing Structured Acquisition Foundations Across Meta and Google Ads — Mighty New
case-study-mighty

Partner

Mighty

Industry

Youth entrepreneurship / online education platform

Engagement

Paid acquisition strategy and structural optimization

Challenges

Limited early conversion volume restricted algorithm learning and prevented confident audience and bid optimization

Goal

Establish a stable, scalable acquisition framework capable of producing consistent account creation conversions

Results

Defined a validated targeting profile, creative direction, and campaign architecture that improved signal clarity and optimization stability

Services

Paid Acquisition Strategy, Funnel Architecture, Conversion Optimization

Channels

Meta Ads, Google Ads

Timeframe

2020

The Situation

Mighty needed to generate account creations and shop launches through Meta Ads and Google Ads. The objective was efficient user acquisition, but early campaigns faced a structural constraint: limited conversion volume.

Without sufficient event density, automated bidding systems could not stabilize. Lookalike audiences lacked depth. Expanding spend under those conditions would have amplified inefficiencies rather than improved results.

The Primary Challenge

The core issue was signal density.

Conversion events were too sparse to support confident optimization. Testing activity also risked disrupting stable campaigns because the account lacked clean separation between experimentation and production performance.

Without structural refinement, scale would introduce volatility instead of consistency.

The Goal

Establish a disciplined acquisition framework capable of producing reliable account creation conversions while preserving room for controlled experimentation.

Stability first. Then scalable growth.

Our Approach

We prioritized structural clarity over expansion.

On Meta Ads, we optimized campaigns around meaningful conversion events and used shorter-window website visitor audiences as an interim signal source while account-based audiences matured.

On Google Ads, we separated testing from stable acquisition flows to protect performance while refining keyword structure and bidding strategy.

The operating principle was isolation: isolate audiences, isolate creative variables, isolate keyword themes.

Execution Highlights

Meta Ads

  • Shifted campaigns to optimize specifically for account creation conversions
  • Implemented Campaign Budget Optimization to consolidate learning across ad sets
  • Identified a high-performing demographic cluster: women aged 35–44, primarily on Instagram mobile feed placements
  • Validated 1% lookalike audiences based on recent website visitors as the strongest interim targeting layer
  • Confirmed parent-and-child imagery outperformed child-only creative variations
  • Validated homepage performance over alternative landing page variants
  • Confirmed “Learn More” as the most effective CTA within testing conditions

Google Ads

  • Structured campaigns around Target CPA bidding with disciplined budget-to-target ratios
  • Built a dedicated testing campaign to protect stable acquisition performance
  • Consolidated converting search terms into tightly themed exact-match clusters
  • Identified “business ideas for kids / teens” and “how to start a business for kids” as high-intent drivers

Results

The engagement produced a defined acquisition profile across both platforms.

On Meta, demographic, placement, and creative variables were clarified and aligned, reducing noise and improving optimization consistency.

On Google, high-intent keyword clusters were isolated and structured into repeatable scaling units.

Most importantly, campaign architecture was reorganized to separate experimentation from revenue-driving performance, reducing volatility and increasing control.

Constraints We Navigated

Limited early conversion data restricted immediate access to robust conversion-based lookalike audiences and aggressive bid automation.

Instead of delaying execution, we used high-signal website audiences as a bridge while allowing conversion density to accumulate organically.

Why This Worked

The leverage point was structural discipline.

By aligning optimization events, isolating testing environments, and consolidating validated signals into scalable units, the account transitioned from exploratory testing to controlled iteration.

Clear structure improves signal clarity. Clear signal improves optimization reliability.

Strategic Takeaway

When conversion data is limited, scale is not the priority. Signal quality is.

Build the architecture that allows platforms to learn. Protect stable campaigns from experimentation. Let validated insights compound into durable acquisition systems.

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