30-second proof
Problem
The team needed to identify, prioritize, and coordinate product experiments that could improve advertisement spend while relying on clear metrics, operational signals, and cross-functional execution.
Product move
Tied experiment selection and iteration to spend behavior, performance signals, and cross-functional measurement instead of treating experimentation as isolated feature output.
Result
Drove a 10% increase in business advertisement spend.
Context
Meta's advertising products operate in a high-scale environment where small improvements can create meaningful business impact. The work required collaboration across product, engineering, data, and stakeholder teams.
Problem
The team needed to identify, prioritize, and coordinate product experiments that could improve advertisement spend while relying on clear metrics, operational signals, and cross-functional execution.
Role
As Senior Product Manager, I coordinated experiments with engineering and data teams, monitored product performance metrics, and translated signals into prioritization and iteration decisions.
Impact mechanism
Tied experiment selection and iteration to spend behavior, performance signals, and cross-functional measurement instead of treating experimentation as isolated feature output.
Product approach
Aligned experiment goals with business advertisement spend outcomes and measurable product success metrics.
Partnered with engineering and data teams to coordinate execution and measurement.
Used performance metrics and operational indicators to inform prioritization and ongoing improvements.
Decision log
- Decision
- Prioritized experiments with measurable advertisement spend signals instead of loosely defined engagement improvements.
- Constraint
- At Meta scale, even small product changes needed clear instrumentation, stakeholder confidence, and careful readouts.
- Tradeoff
- Moved faster on narrower experiment bets while resisting broader feature scope that would slow learning.
- Result
- Created a clearer path from experiment execution to the 10% spend increase.
- Decision
- Used performance metrics and operational indicators as the main iteration input.
- Constraint
- The work sat across product, engineering, data, and business stakeholders with different success lenses.
- Tradeoff
- Reduced preference-driven prioritization in favor of evidence strong enough to align the group.
- Result
- Kept the team oriented around measurable business impact throughout experimentation.
Technical judgment
High-scale experimentation environment involving product metrics, performance signals, data-team partnership, and engineering coordination.
- Defined experiment success around measurable spend behavior and product performance indicators.
- Coordinated with data and engineering partners so iteration decisions were grounded in instrumented signals.
- Balanced speed of learning with confidence in measurement and product quality.