How Writer Reduced Cost Per MQL by 86% With Metadata

Writer, an enterprise AI writing platform, was scaling demand gen to reach marketing and content leaders at mid-market and enterprise companies. In the competitive generative AI space, every marketing dollar needed maximum impact. The team ran campaigns across multiple paid channels but lacked visibility into which spend was driving qualified leads versus inflating MQL counts with low-intent contacts.

Without intelligent budget allocation, spend was distributed evenly across campaigns and channels, missing the opportunity to concentrate investment on the audiences and experiments generating the highest-quality MQLs at the lowest cost.

Metadata provided Writer with data-driven budget allocation and targeting infrastructure to maximize lead quality while reducing cost.

• Budget Optimization used performance signals to dynamically shift spend toward campaigns and channels producing the lowest cost per MQL, rather than distributing budget evenly.
• MetaMatch audience targeting enabled Writer to reach marketing and content decision-makers with precision, filtering out irrelevant audiences before a dollar was spent.
• Campaign Automation let the small demand gen team manage more experiments across channels without adding headcount, accelerating the pace of testing and optimization.

With Metadata managing budget allocation and audience targeting, Writer achieved dramatic efficiency gains in lead generation.

• 86% reduction in cost per MQL through intelligent budget allocation
• Higher lead quality by concentrating spend on audiences most likely to convert
• Faster scaling of demand gen programs without proportional increases in team size