Build a metric tree for Lightroom after a major redesign

Problem Statement Description

Product context: Adobe is a creative, document, and marketing software company; its products include Creative Cloud, Photoshop, Illustrator, Acrobat, Adobe Express, Firefly, and Experience Cloud.

Adobe Lightroom has undergone a major redesign, and you are asked to define how the team should measure whether the redesign is successful for students. The redesign may affect how students import photos, organize projects, learn editing tools, use presets or AI-assisted features, export/share work, and decide whether Lightroom is worth continuing to use.

Assume the student segment includes users with varying skill levels, from beginners editing class or social content to more advanced photography/design students building portfolios. The redesign may improve discoverability and modernize the interface, but it could also disrupt familiar workflows, reduce task completion, or change how users engage with core editing depth.

Your task is to build a metric tree that connects Lightroom’s product and business goals to measurable user behaviors after the redesign. The tree should help Adobe evaluate adoption, engagement, creative workflow success, retention, and any unintended negative effects across relevant student cohorts.

The metric tree should consider:

- A clear north-star or primary success metric, including what is counted and the denominator.

- Input metrics that explain student workflow progress, such as onboarding, import, edit creation, feature discovery, export, sharing, or project completion.

- Cohort breakdowns, such as new vs. existing students, beginner vs. advanced users, platform, geography, subscription status, or redesign exposure group.

- Instrumentation needed to capture events reliably across the redesigned Lightroom experience.

- Guardrail metrics for workflow friction, errors, latency, crashes, undo/revert behavior, support contacts, churn, or downgrade/cancellation signals.

- How to distinguish novelty effects from durable behavior changes after launch.

- How the metric tree would support product decisions, such as whether to iterate on onboarding, navigation, AI/editing tools, sharing, or monetization flows.

The goal is to show how you would structure a practical, decision-useful measurement framework for a redesigned creative tool, without jumping directly to feature recommendations.