Case Study
Pricing Analytics Decision Stack
Designed a pricing analytics layer connecting elasticity signals, churn guardrails, and experiment outcomes.
Impact
De-risked pricing tests while preserving retention quality.
Challenge
Pricing tests were high-stakes and politically sensitive. Teams lacked a unified model for evaluating revenue upside against churn risk and customer trust signals.
Approach
- • Built segment-level elasticity tracking for plan and audience cohorts.
- • Defined mandatory guardrails for churn, support burden, and downgrade behavior.
- • Standardized pricing test readouts with confidence intervals and segment variance.
- • Connected decision outputs to finance and growth planning cycles.
Outcomes
- • Higher confidence in pricing rollout/no-rollout decisions.
- • Faster alignment between growth, product, and finance.
- • Reduced risk of short-term revenue lifts causing long-term churn penalties.