Engagement signal
+10K
Generalized lift following validated test rollouts.
Product experimentation
Reducing churn through data-backed pricing and feature experimentation.
A quantitative product case study showing how KPI instrumentation, A/B testing, and dashboard baselines supported pricing, roadmap, and rollout decisions for a subscription product.
Churn signal
3K
Generalized reduction in churned users over the measured period.
Method
A/B
Pricing and feature variants tested against a KPI baseline.
Case study scope
Experimentation evidence, not a generic UX research claim.
This work is positioned as quantitative product analytics: dashboards, KPI baselines, A/B testing, variant decisions, and rollout validation.
Confidentiality note
Company-specific figures and identifying details are generalized. The page documents the method, decision logic, and product analytics workflow without exposing private source data.
01
Framed churn as a product and business decision problem, not just a monthly reporting metric.
02
Defined dashboard-tracked success criteria before variants were evaluated.
03
Compared post-rollout engagement and churn movement against the shared KPI baseline.
04
Separated variants that improved retention from variants that failed to move the number.
05
Converted test outcomes into roadmap and rollout decisions with UAT checkpoints.
06
Kept the scope honest: this is quantitative product experimentation, not qualitative UX research.
Method
From churn question to product decision loop.
The work connects business analysis, product analytics, experimentation design, metric validation, and release discipline.
Business question
Churn appeared in billing and revenue reporting each month, but the team needed to know whether the driver was pricing, feature usage, or noise before committing product and engineering time.
KPI baseline
Dashboards in Tableau, Excel, and Google Sheets tracked engagement, revenue, and churn so every test had a shared measurement baseline.
Experiment design
Business and finance requirements were translated into scoped pricing and feature variants small enough to isolate metric movement.
Decision loop
Validated outcomes fed back into requirements, roadmap choices, and UAT so winning tests could ship without downstream regressions.
Experimentation proof
1-- Experiment readout pattern used to compare post-rollout KPI movement.2-- Figures shown in the case study are generalized for confidentiality.3SELECT4 cohort,5 variant,6 engagement_delta,7 churn_delta8FROM kpi_dashboard.experiment_results9WHERE metric_window = 'post_rollout_30d'10ORDER BY churn_delta ASC, engagement_delta DESC;Findings
Churn became measurable product levers.
The key value was turning a broad retention problem into scoped experiments that could be accepted, rejected, or refined with evidence.
At-risk cohorts were visible in usage data before they appeared as cancellations.
Pricing and feature changes became testable levers rather than opinions debated in isolation.
The durable output was a repeatable decision instrument for later retention work.