Pricing Experimentation Control Tower
A pricing operations system that makes segments, variants, scenario assumptions, simulation runs, guardrail bands, and decisions editable and auditable.
The problem
Pricing tests are often spread across spreadsheets, analytics dashboards, and decision meetings. That makes it hard to preserve the hypothesis, cohort rules, guardrails, and final decision rationale in one place.
The thesis
Pricing experiments become safer when they are managed as governed operating loops: define the test, edit the inputs, simulate the risk, inspect the guardrails, and record the decision.
System flow
Step 1
Experiment registry
Store the hypothesis, owner, segment eligibility, variants, and minimum sample rules.
Step 2
Editable inputs
Let operators update price variants, segment baselines, margin, conversion, churn, and assumptions.
Step 3
Scenario simulation
Run conversion, churn, support-load, ARPU, margin, and confidence logic against each segment.
Step 4
Decision record
Persist segment results, guardrail bands, recommendations, decisions, and audit entries.
Case-study artifacts
Recommendation
Runs can promote, extend, pause, or roll back based on policy.
Segment lift
Each segment stores simulated net revenue lift and guardrail band.
Holdout health
Sample size and control health influence the recommendation.
Decision frame 1
Input
Hypothesis, segment eligibility, price variants, segment baselines, and scenario assumptions.
Decision frame 2
Decision
Evaluate conversion, churn, margin, support load, holdout health, and confidence.
Decision frame 3
Output
Segment results, guardrail bands, recommendation, decision record, and audit trail.
Evidence links
Shows the control tower framing, reset controls, and operating model.
Shows editable experiment, variants, segments, and assumptions.
Shows scenario controls, guardrail response, and segment lift map.
Shows latest run KPIs, segment results, and simulation output.
Shows promote/extend/pause/rollback decisions and rationale.
Guardrails
Sample and holdout checks influence whether a test can be promoted.
Treatments are paused or rolled back when margin or churn pressure breaks policy.
Promotion decisions require explicit rationale and actor history.
System architecture
1. Experiment registry
Store the hypothesis, owner, segment eligibility, variants, and minimum sample rules.
2. Editable inputs
Let operators update price variants, segment baselines, margin, conversion, churn, and assumptions.
3. Scenario simulation
Run conversion, churn, support-load, ARPU, margin, and confidence logic against each segment.
4. Decision record
Persist segment results, guardrail bands, recommendations, decisions, and audit entries.
KPI callouts
Scenario output
Each run produces a recommendation based on confidence, holdout health, and guardrails.
Guardrail bands
Segment results expose revenue lift and guardrail status before rollout.
Editable economics
Operator inputs use dollar fields for variants and segment economics.
Commercial framing
The system protects monetization decisions from spreadsheet drift by keeping the hypothesis, assumptions, guardrails, simulation output, and decision trail together.
What I built
I built the pricing schema, editable segment and variant inputs, scenario assumption form, simulation endpoint, guardrail visualization, output tables, decision queue, audit logs, and docs.