Context · Growth motion
Product-led
The product does the converting, and marketing brings people to a self-serve motion.
What changes here
The same four questions, and what each one runs into in this context.
Attribution
Activation happens in-product, often days after any marketing touch and frequently on another device. The join is the whole problem.
Incrementality
In-product experiments carry their own hazards: novelty and primacy effects, network interference between users, and guardrail metrics that must not move.
Allocation
Acquisition spend competes with product investment for the same growth, and the two are rarely compared on the same terms.
Decisioning
Onboarding state is the strongest signal available, and the intervention is usually a product change rather than a message.
Written for this context
Attribution
- Planned
The Same Click, Different Funnels
One ad click means something different in a self-serve checkout than in a sales-qualified pipeline. Why the funnel grain you choose determines what you can measure.
- Planned
What Tracking Loss Actually Costs You
Consent, identity resolution, and cross-device loss are not one problem. Separating random loss from systematic loss, and why only one of them threatens validity.
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
Counterfactuals, Estimands, and Saying What You Mean
Before choosing a method, state precisely what you are trying to estimate: which units, which treatment, which outcome, over what horizon.
- 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
Choosing the Right Campaign Optimization Event
The deepest event that stays reliable, timely, and frequent enough to train on. A three-way tradeoff that moves as volume grows.
- 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 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
Governance for Agentic Campaigns
Logged decisions, persistent controls, consent enforcement, and causal evaluation when an autonomous system is choosing the action.