David Wolfe — AI-driven revenue systems

TOOL

SampleLifecycle sample data739 rows · updated May 30, 12:17 AM

Lifecycle Engine Docs

Variables used to match users to tracked entities, score campaign opportunities, and model funnel outcomes.

Input data fields

Field	Meaning	Example / range	Used by
`user.fullName / email`	The person who may receive a generated lifecycle message.	Jordan Lee / jordan@example.com	Message personalization and candidate context
`user.segment`	Commercial lifecycle segment assigned to the user.	FREE, TRIAL, LAPSED, ACTIVE	Segment contribution and eligibility
`user.subscriptionStatus`	Billing/subscription state used to choose appropriate outreach posture.	NONE, TRIALING, ACTIVE, CANCELED, EXPIRED	Campaign targeting and suppression logic
`user.lastActiveAt`	Most recent observed activity timestamp for the user.	Recent date or blank	Recency context and message tone
`entity.name / entityType`	The person, place, record, or object the user has shown interest in.	123 Main St / property	Interest graph and generated copy
`entity.city / state`	Optional location context for the tracked entity.	Austin / TX	Message specificity and landing context
`interestEdge.interestScore`	Strength of the user's demonstrated relationship to an entity.	0.72	Interest contribution to priority score
`interestEdge.source`	Where the relationship signal came from.	search, view, saved	Auditability and operator interpretation
`entityDelta.changeType`	The type of change detected on the tracked entity.	ADDRESS_CHANGE, EMPLOYEE_RECORD_ADDED, OTHER_RECORD_ADDED	Change-type contribution and message angle
`entityDelta.oldValue / newValue / deltaSummary`	Before/after values and plain-language explanation of the change.	Old phone -> new phone; phone added	Generated message evidence and landing copy
`simulation.topN / deltaCount`	Operator controls for how many opportunities to generate and how many new deltas to inject.	Top 20, 8-20 deltas	Simulation volume and campaign run size

Model variables and ranges

Variable	Type	Range	Used by
`segment`	enum	FREE, TRIAL, LAPSED, ACTIVE	Priority score segment contribution
`subscriptionStatus`	enum	NONE, TRIALING, ACTIVE, CANCELED, EXPIRED	User context and campaign targeting
`interestScore`	float	0-1	Interest-edge contribution
`recencyScore`	float	0-1	Delta freshness contribution
`minPriorityScore`	float	0-1	Candidate filtering
`openRate/clickRate/engageRate/purchaseRate`	float	0-1	Outcome simulation
`avgOrderValue`	currency	$0+	Estimated revenue
`topN/deltaCount`	integer	1-100 / 1-200	Simulation volume controls

Formulas and recommendation rules

Model step	Formula	Decision rule
Priority score	`interestContribution + recencyContribution + segmentContribution + changeTypeContribution`	Candidates below minPriorityScore are filtered out.
High-priority flag	`priorityScore >= highPriorityThreshold`	High-priority candidates drive campaign generation and revenue projection.
Projected revenue	`purchases * avgOrderValue, with purchase rate adjusted by highPriorityLift`	Used to compare scenario assumptions across campaign runs.