How Monte Carlo Used Campaign Experimentation to Create $6.1M in Pipeline
See how Monte Carlo's one-person paid media team used Metadata's Campaign Experimentation and Auto-Pause Rules to generate $6.1M in pipeline with 14.5X ROI.
- Industry
- Data Observability / SaaS
- Company Size
- Mid-Market (227 employees)
- Products Used
- Audiences, Campaign Experimentation, Auto-Pause Rules
- Website
- montecarlo.ai →
Monte Carlo had a strict ideal customer profile but felt constrained by LinkedIn's native targeting capabilities. The precision they needed to reach their ICP simply wasn't available through standard platform tools.
Compounding the issue, a one-person paid media team was spending excessive time manually building experiments and analyzing performance data. The manual overhead left little bandwidth for strategic optimization or scaling campaigns effectively.
Metadata gave Monte Carlo the targeting precision and automation their lean team needed to scale.
- Custom Audiences and exclusion lists using firmographic and Salesforce targeting enabled Monte Carlo to reach their strict ICP with precision that LinkedIn's native tools couldn't match.
- Campaign Experimentation tested multiple ad creative and copy variations simultaneously, identifying top performers without manual A/B test management.
- Auto-Pause Rules automatically shifted budget away from underperforming experiments and toward top performers, ensuring spend was always optimized.
With Metadata automating experimentation and budget optimization, Monte Carlo achieved outsized pipeline results from a single-person team.
- $6.1M in pipeline generated through automated campaign experimentation
- 14.5X ROI on paid media spend
- 621 hours of manual work automated
- 66% reduction in campaign management time, freeing the team to focus on strategy