Debug a spike in complaints from mobile-first viewers on Recommendations

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 seen a sudden spike in customer complaints from mobile-first viewers related to Recommendations. These are members whose primary discovery and viewing behavior happens on mobile devices, often across varied network conditions, app versions, regions, languages, and content preferences.

Your task is to frame how you would investigate the issue as a product manager. Focus on understanding whether the complaints reflect a true deterioration in recommendation quality, a mobile experience issue, a data or instrumentation problem, a localized content/catalog issue, or a change in user expectations driven by recent product, content, or algorithm updates.

The investigation should cover how you would define the anomaly, segment the affected population, validate signals, generate hypotheses, identify evidence, and coordinate mitigation without jumping directly to a solution.

The experience should consider:

- How to define and quantify the complaint spike, including baseline period, complaint rate denominator, and severity of user impact.

- Segmentation by mobile platform, app version, geography, language, plan type, tenure, network quality, and viewing behavior.

- Recommendation surfaces involved, such as home rows, “Top Picks,” continue watching adjacency, search-adjacent suggestions, previews, or push/email-driven recommendations opened on mobile.

- Instrumentation checks to confirm whether ranking, impressions, clicks, plays, dismissals, thumbs ratings, watch time, and complaint events are being captured correctly.

- Hypotheses across recommendation relevance, content availability, localization, UI rendering, latency, personalization freshness, A/B tests, and catalog changes.

- Evidence needed to distinguish algorithmic issues from mobile UX, streaming performance, content supply, or customer support classification problems.

- Short-term mitigations, escalation paths, and communication needs if the issue is materially affecting discovery, engagement, or retention.

- Longer-term prevention through monitoring, alerting, experiment guardrails, and post-incident learning.

The goal is to demonstrate a structured RCA approach that protects the member experience, isolates the likely cause, prioritizes high-impact segments, and enables Netflix teams to make an informed decision on mitigation and follow-up actions.