Context · Business model

B2B SaaS

Long cycles, account-level outcomes, and a sales team standing in the middle of the journey.

What changes here

The same four questions, and what each one runs into in this context.

The unit is an account or an opportunity, not a person. Multi-contact buying groups break person-level credit entirely.

Outcomes lag by a quarter or more, forcing either a long window or a validated earlier proxy. Say which you chose.

Pipeline is not revenue. Allocating against MQLs optimises the number the sales team already distrusts.

Rep capacity is the binding constraint, not send capacity, and it is far scarcer. Scoring that ignores it produces a queue nobody works.

Attribution Is Not Incrementality

Attribution assigns credit for conversions you observed. Incrementality estimates the ones that would not have happened otherwise. The gap between them is where budgets go wrong.
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.
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.
Worked example Planned

Leads Versus Incremental Qualified Pipeline

A B2B lead program that hit every MQL target and moved no pipeline. Tracing where the funnel definition and the incentive diverged.

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

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.

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.