Context · Growth motion
Lifecycle-led
Existing customers, repeated contact, and a contact budget that runs out.
What changes here
The same four questions, and what each one runs into in this context.
Attribution
Sends are easy to attribute and easy to over-credit, because the audience was selected for propensity in the first place.
Incrementality
Persistent holdouts are cheap here and pay for themselves. There is little excuse for not having one.
Allocation
The budget is contact capacity and customer patience, not media spend.
Decisioning
The home of uplift. Targeting propensity spends the contact budget on people who needed nothing.
Written for this context
Attribution
- Planned
Defining Events That Survive Contact With Operations
A lead, an activation, and a qualified opportunity are organizational agreements before they are data. What happens to measurement when the definition drifts.
Incrementality
- Planned
Choosing Between User, Geo, and Cluster Holdouts
The design decision is about what you can withhold cleanly, not about statistical efficiency. A decision procedure for picking the unit of randomization.
- Planned
When the Experiment Leaks
Noncompliance, contamination, and spillovers. Why intent-to-treat is usually the right estimand even though it answers a slightly different question.
Decisioning
- Planned
From Segment to Audience to Treatment Policy
Segments, audiences, scores, assignments, and policies are five different objects. Conflating them is why lifecycle programs become unmeasurable.
- Planned
Prediction Is Not Persuasion
A high-propensity customer may convert without you. A high-risk one may be unreachable. The best target is whoever would change their mind.
- Planned
The Case for Doing Nothing
Contact fatigue, capacity, and cost mean no-action is a real treatment arm. Why it belongs in every decisioning system by default.
- Planned
Uplift Modeling in Practice
Estimating heterogeneous treatment effects when you have a randomized holdout, and how to validate a policy prospectively rather than offline.
- Planned
Why Agentic Marketing Still Needs Holdouts
Automating a decision does not exempt it from measurement. Logged decisions, persistent controls, and causal evaluation for autonomous systems.
- Worked example
Broad Nurture, Propensity, or Uplift?
One trial-to-paid nurture sequence, four targeting policies, and a randomized holdout. Compared on incremental conversions rather than model accuracy, which is the only comparison that changes the answer.
Systems
- System Planned
Customer State Model
One row per person or account carrying consent, lifecycle stage, eligibility flags, and contact history. The foundation every audience, experiment, and policy reads from.
- System
Audience Builder and Capacity Planning
Audience definitions as versioned, reusable objects instead of one-off SQL, and the capacity check that decides whether a test was ever going to work.
- System Planned
Consent and Suppression
Lawful basis, channel permission, global opt-outs, and complaint handling as one shared layer rather than a filter each campaign reimplements.
- System Planned
Assignment and Experiment Registry
Deterministic hash-based assignment, persistent holdouts, overlap rules, and a registry that stops two campaigns from colliding on the same people.
- System Planned
Exposure and Decision Logging
Logging what was decided and what was actually delivered, not just what was sent. The component that makes everything downstream measurable at all.
- System Planned
Automated Measurement
A standing readout that runs eligibility, assignment, delivery, exposure, outcome, and guardrails on a schedule instead of as a bespoke analysis each time.
- System Planned
Next-Best-Action Policy
Turning scores into decisions under real constraints: capacity, contact fatigue, margin, cooldowns, and the explicit option of doing nothing.
- System Planned
Governance for Agentic Campaigns
Logged decisions, persistent controls, consent enforcement, and causal evaluation when an autonomous system is choosing the action.