How GoodTime Grew Pipeline 50% While Spending 13% Less With Metadata
See how GoodTime used Metadata's Experiment Engine to increase pipeline by 50%, reduce spend by 13.4%, and lower cost per opportunity by 42%.
- Industry
- HR Tech / SaaS
- Company Size
- Growth Stage (~70 employees)
- Products Used
- Campaign Automation, Google Search, Experiment Engine
- Website
- goodtime.io →
GoodTime was managing 50+ active Google Search keywords with CPL targets being consistently exceeded. The team needed to differentiate between similar-performing keywords to identify which were truly driving qualified pipeline versus just generating clicks.
Decision-making was based on gut instinct rather than data. Without a structured experimentation framework, the team couldn't determine the optimal mix of spend, keywords, bids, and budgets needed to maximize pipeline while controlling costs.
Metadata's Experiment Engine gave GoodTime the structured testing framework to optimize their Google Search programs scientifically.
- Keyword intent tiering categorized 50+ keywords by purchase intent, enabling differentiated bidding strategies based on keyword quality.
- Single keyword ad groups (SKAGs) isolated keyword performance for accurate testing, eliminating the noise of grouped keywords.
- Aggressive bidding on proven converters concentrated budget on keywords with demonstrated pipeline impact.
- Auto-pause rules automatically stopped spend on low-performing keywords, preventing budget waste without manual monitoring.
By replacing gut instinct with structured experimentation, GoodTime achieved more pipeline with less spend.
- 50% pipeline increase driven by keyword optimization and intent-based bidding
- 13.4% less spend through auto-pause rules and budget concentration on proven performers
- 42% lower cost per opportunity by eliminating waste on low-converting keywords
- 320% more dollars in pipeline from the same search programs