Lifecycle Revenue Engine
A working AI-enabled lifecycle system that turns entity-level change signals into scored campaign opportunities, generated outreach, and modeled revenue outcomes.
The problem
Most lifecycle programs still start with a calendar, a broad segment, and a message template. That structure misses the moments when a user has fresh intent tied to a specific person, place, record, or entity change.
The thesis
Lifecycle marketing becomes a revenue system when the unit of work shifts from campaigns to opportunities: detect a meaningful change, prove user interest, score the commercial value, then generate the right action.
System flow
Lifecycle pipeline diagram
Signal flow from change detection to modeled commercial impact.
Entity Deltas
Campaign Candidates
Generated Messages
Projected Outcomes
Text alternative: Entity Deltas flow into Campaign Candidates, then Generated Messages, then Projected Outcomes.
Case-study artifacts
Signal volume
Seeded and simulated deltas exercise the prioritization and generation flow.
Opportunity score
Explainable priority score from interest, recency, segment, and change type.
Revenue view
Estimated revenue stored with each campaign run for trend comparison.
Decision frame 1
Input
Editable users, tracked entities, interest relations, assumptions, and entity-level change events.
Decision frame 2
Decision
Rank candidate opportunities and select the highest-value records for campaign generation.
Decision frame 3
Output
Persisted campaign run, generated message assets, score breakdowns, and modeled revenue outcomes.
Evidence links
Shows signal flow, graph context, and operating model.
Shows editable users, entities, interest edges, scoring weights, and assumptions.
Shows runs, trend charts, segment breakdown, and generated messages.
Shows candidate filtering, scoring evidence, and generated campaign runs.
Guardrails
Pages and APIs degrade with compatibility messaging when product tables are missing.
Seed and simulation endpoints are controlled by environment flags.
Run details connect assumptions, candidates, score components, and generated messages.
System architecture
1. Change detection
Seed and simulate entity deltas such as address, phone, associate, and legal-record changes.
2. Interest graph
Connect users to entities they searched, viewed, saved, or otherwise demonstrated interest in.
3. Eligibility layer
Apply segment and subscription context so free, trial, active, and lapsed users can be treated differently.
4. Priority scoring
Rank candidates using interest strength, recency, segment value, and change-type weight.
5. Generation and outcome loop
Create message assets, persist campaign runs, and model downstream funnel/revenue outcomes.
KPI callouts
Priority score
Inspectable score built from interest, recency, segment, and change-type contribution.
Generated assets
Each selected opportunity can produce subject, preview, body, landing copy, and CTA.
Revenue model
Campaign runs store estimated revenue so scenarios can be compared over time.
Commercial framing
The system is not trying to send more lifecycle messages. It is trying to identify which moments deserve action, why they matter, and what revenue path they create.
What I built
I built the lifecycle schema, editable input surfaces, entity/user graph, scoring logic, generation endpoint, simulation flow, output views, documentation, and audit-aware run details.