How to Judge Whether Outside Growth Help Paid Off
Advisory ROI is not only the revenue lift you can attribute. It is decisions made faster and better, mistakes avoided, and a team that can do the next one without help. Here is how to measure all three.
How should a studio measure the return on advisory work?
On three layers, agreed before the work starts:
- Direct impact. The measured effect of the changes that were implemented — ideally through controlled tests with baselines and guardrails, read on mature cohorts.
- Decisions. Mistakes avoided (a scale step that would not have paid back, a migration done badly) and decisions made faster. Harder to quantify, often the larger value.
- Capability. Definitions, review rhythms, test discipline and judgment the team keeps after the engagement ends.
How do you set it up?
- Name the decision or metric the work should move before starting, with its current baseline.
- Agree how it will be read: cohort age, segments, and the protected metrics that must not deteriorate.
- Assign an internal owner for each recommendation. Unowned recommendations are the main reason advisory work fails.
- Review at the end against the plan: what was implemented, what moved, what the team now does differently.
What are the common traps?
- Attributing everything to the engagement. Markets move; seasonality happens. Controlled comparisons protect both sides.
- Reading too early. Monetization and UA changes need matured cohorts — see data maturity.
- Counting recommendations instead of changes. A long list of good ideas nobody implemented has a return of zero.
What does good look like?
A small number of implemented changes with measured results, a few decisions the team is confident it got right, and a system — definitions, guardrails, review cadence — that keeps working after the advisor leaves. That is the standard I hold my own work to, and it is why every engagement starts by naming the decision it has to support.