What is lifecycle tracking?

Why acquisition metrics alone mislead

Cost per install and cost per acquisition measure the moment a user arrives. They say nothing about whether that user was worth acquiring, and the two frequently point in opposite directions.

The cheapest sources reliably deliver the least valuable users. A channel at half the CPI and a third of the retention is a worse buy, and no acquisition-stage metric will ever reveal that. Lifecycle tracking is what connects the source to the outcome.

The stages worth instrumenting

• Acquisition. Where the user came from, held against everything downstream.
• Activation. Whether they reached the moment the product becomes useful — the strongest early predictor of everything after.
• Retention. Whether they returned, measured by cohort — see retention rate.
• Monetisation. What they spent, and when — see lifetime value.
• Churn. When they stopped, and whether anything preceded it.

Cohorts are the unit

Lifecycle data is only interpretable by cohort. Aggregate retention across all users blends a large recent intake with a small long-tenured one and moves for reasons that have nothing to do with the product.

Grouping users by acquisition period and source, and following each group forward, is what makes the numbers comparable. It is also what lets you answer the question acquisition metrics cannot: which sources produced users who were still here, and still paying, six months later.

What privacy changes did to it

Lifecycle tracking inside your own product is first-party measurement and is largely unaffected — you observe your own users because they are using your product.

What broke is the join back to acquisition. Where the source cannot be attached to the user, you have excellent lifecycle data and no way to attribute it. On iOS the conversion value schema is the narrow channel through which a compressed version of that link survives, which is why choosing what it encodes is a lifecycle decision rather than a technical one.