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Customer Story

How Monotype Reduced Cost Per Opportunity by 83% With Metadata

Discover how Monotype lowered cost per opportunity by 83% using Metadata's automated experiment optimization and budget management.

83%Lower Cost Per Opportunity
Industry
Design Technology / SaaS
Company Size
Mid-Market (500-1,000)
Products Used
Experiment Automation, Budget Optimization, MetaMatch
1 The Challenge

Monotype, the company behind some of the world's most widely used typefaces and font technology, markets to a diverse set of B2B buyers, from brand designers to enterprise procurement teams. With a product spanning creative and technical decision-makers, the demand gen team faced the challenge of optimizing campaigns across different audiences, messages, and channels without a clear signal on what was driving opportunities.

Running experiments manually across platforms was time-intensive, and the team lacked the data infrastructure to quickly identify which combinations of audience, creative, and offer were generating the best cost-per-opportunity outcomes.

2 The Solution

Metadata gave Monotype the ability to run structured experiments at scale and automatically optimize toward pipeline outcomes.

  • Experiment Automation enabled the team to test multiple audience, creative, and offer combinations simultaneously, with the platform automatically shifting budget toward the best-performing experiments.
  • Budget Optimization moved spend away from underperforming experiments in real time, concentrating budget on campaigns generating the lowest cost per opportunity.
  • MetaMatch targeting improved audience precision, reducing wasted spend on impressions unlikely to convert to qualified opportunities.
3 The Results

Through systematic experimentation and automated optimization, Monotype dramatically improved the efficiency of their demand gen investment.

  • 83% lower cost per opportunity with automated experiment optimization
  • Faster identification of winning audience and creative combinations through structured testing
  • More efficient budget allocation with real-time optimization toward pipeline outcomes