David Wolfe — AI-driven revenue systems

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SamplePricing sample data8 rows · updated May 6, 1:13 AM

Pricing Control Tower Docs

Variables used to test price/packaging variants against segment economics and guardrails.

Input data fields

Field	Meaning	Example / range	Used by
`experiment.name / hypothesis`	Test identity and expected business effect.	Pro plan packaging test / increase ARPU without churn	Experiment registry and decision record
`experiment.owner`	Accountable operator for the pricing test.	Growth lead	Decision ownership
`experiment.holdoutPercent`	Share of eligible traffic preserved as control.	0.10	Holdout health and confidence
`experiment.minimumSampleSize`	Minimum sample required before confident action.	1,000	Extend vs promote decision
`experiment.minGrossMarginPercent`	Lowest acceptable gross margin for treatment.	0.72	Margin guardrail
`experiment.maxChurnDeltaPercent`	Maximum tolerated churn increase.	0.03	Rollback guardrail
`variant.monthlyPrice / annualPrice`	Price points being tested.	$29 monthly / $290 annual	ARPU and price response modeling
`variant.packagingChange`	What changes in the offer beyond price.	Adds team reporting	Operator context and decision rationale
`segment.eligibilityRule`	Who belongs in a pricing segment.	active SMB accounts with usage > threshold	Segment inclusion
`segment.baselineConversionRate / baselineChurnRate`	Pre-test conversion and churn behavior for the segment.	0.08 conversion / 0.04 churn	Simulation baseline
`segment.baselineARPU / monthlyVolume`	Existing revenue and scale of the segment.	$65 ARPU / 4,000 monthly users	Revenue lift sizing
`scenario.conversionLift / churnSensitivity / demandElasticity`	Operator assumptions for treatment response.	0.08 lift / 0.02 churn sensitivity / 0.35 elasticity	Simulation stress test

Model variables and ranges

Variable	Type	Range	Used by
`monthlyPrice/annualPrice`	currency	$0+	Variant ARPU modeling
`baselineARPU`	currency	$0+	Segment baseline revenue
`baselineConversionRate/baselineChurnRate`	float	0-1	Experiment simulation
`grossMarginPercent`	float	0-1	Margin guardrail
`minimumSampleSize`	integer	1+	Holdout health and decisions
`conversionLiftPercent`	percent	any practical numeric	Simulation scenario
`churnSensitivityPercent`	percent	any practical numeric	Guardrail stress test
`demandElasticity`	float	0+	Price response modeling

Formulas and recommendation rules

Model step	Formula	Decision rule
Conversion rate	`baselineConversionRate * (1 + conversionLift - priceDelta * demandElasticity)`	Feeds segment-level treatment revenue.
Net revenue lift	`treatmentNet - controlNet - churnCost`	Primary economic output for promotion decisions.
Recommendation	`rollback on unhealthy holdout/margin/churn, pause on support overload, extend on low sample/confidence, otherwise promote`	Controls decision queue state.