Build a metric tree for Ad-supported Plan after a major redesign

Problem Statement Description

Product context: Netflix is a streaming entertainment company; its products include subscription video, original films and series, recommendations, profiles, games, and ad-supported plans.

Netflix has recently launched a major redesign of its Ad-supported Plan experience, with particular attention to binge watchers who consume multiple episodes or titles in a single session. The redesign may affect how users discover content, tolerate ad breaks, continue watching, and perceive value relative to ad-free plans and competing entertainment options.

Your task is to build a metric tree that helps Netflix evaluate whether the redesigned Ad-supported Plan is succeeding. The metric tree should connect high-level business outcomes to user behavior, ad monetization, content engagement, and experience quality, while making clear how each metric would be defined and interpreted.

Focus on metrics that are decision-useful after a redesign: what Netflix should monitor, how teams should diagnose movement in the top-line metric, and which guardrails would prevent optimizing ads or engagement at the expense of long-term retention and satisfaction.

The experience should consider:

- A clear north-star or primary success metric for the redesigned Ad-supported Plan, including its denominator and time window.

- Supporting branches for acquisition, activation, engagement, retention, monetization, and plan switching behavior.

- Binge-watcher-specific metrics such as multi-episode session continuation, ad interruption tolerance, completion rates, and return frequency.

- Ad-specific metrics such as ad load, fill rate, impressions per viewing hour, completion rate, relevance, and revenue per member.

- Experience-quality guardrails such as buffering, playback failures, latency, content discovery success, customer support contacts, and negative feedback.

- Cohort cuts by new vs. existing members, geography, device type, content genre, subscription tenure, and exposure to the redesign.

- Instrumentation needs for events like ad start/completion, skip or abandonment points, playback sessions, title starts, episode progression, and plan changes.

- How the metric tree would support decisions such as iterating the redesign, adjusting ad load, improving recommendations, or rolling back problematic changes.

The goal is to demonstrate that you can structure a practical, measurable framework for evaluating a redesigned subscription-and-ad experience at Netflix, balancing user satisfaction, content consumption, advertiser value, and long-term business health.