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 complaints from mobile-first viewers related to Recommendations. These are members who primarily discover and watch content on mobile devices, often across varied network conditions, screen sizes, languages, and content catalogs. The complaints may involve irrelevant titles, repetitive rows, poor localization, difficulty finding something to watch, or perceived changes in the home experience.

Your task is to investigate the anomaly as an RCA problem. You should frame what “spike” means, determine whether the issue is real or instrumentation-driven, identify which mobile viewer segments are affected, and build a structured set of hypotheses across recommendation models, app experience, content catalog, personalization inputs, experimentation, and delivery systems.

The investigation should be grounded in Netflix’s recommendation workflow: a member opens the mobile app, lands on a personalized home surface, scans rows and artwork, interacts with previews or title details, and decides whether to play, save, search, or abandon. The RCA should connect complaint signals to measurable product behavior and business impact, including discovery quality, engagement, retention risk, and trust in personalization.

The experience should consider:

- How to define and quantify the complaint spike, including baseline period, severity, complaint categories, and affected geographies or app versions.

- Segmentation by mobile-first behavior, device type, OS, app version, plan type, language, country, tenure, content preferences, and network conditions.

- Instrumentation checks to validate whether complaint volume, recommendation impressions, ranking logs, feedback signals, or support tags changed.

- Hypotheses across recommendation ranking, personalization data freshness, artwork/title metadata, localization, catalog availability, A/B tests, app UI changes, and backend latency.

- Evidence needed to separate correlation from causation, including cohort trends, funnel changes, experiment exposure, release timelines, and comparison with non-mobile viewers.

- Short-term mitigation options that reduce member pain while preserving recommendation quality and avoiding unintended impacts on other segments.

- Longer-term prevention mechanisms such as monitoring, alerting, experiment guardrails, complaint taxonomy improvements, and recommendation quality diagnostics.

The goal is to present a clear RCA plan that moves from anomaly validation to segmentation, hypothesis testing, evidence gathering, mitigation, and prevention. Focus on how you would reason through the issue, what data you would request, how you would prioritize investigation paths, and how you would communicate findings and next steps to product, engineering, data science, customer support, and content teams.