The "Average Viewing Time by Genre & Plan" chart shows Action,Drama reaching 160 minutes, indicating deep viewer engagement for that genre combination. This supports the FMEA insight that some content types are indeed driving retention and should be prioritized. However, if metadata for such high-performing content is missing or incomplete—as seen in the NA analysis—it limits discoverability and weakens analytics. For decision-makers, this emphasizes the need to safeguard and spotlight well-performing genres through better labeling, recommendations, and promotional focus.

The **Monthly Subscriber Trend** shows a significant spike in new sign-ups around **May 21**, indicating a successful campaign, content drop, or seasonal appeal. This surge is promising, but the FMEA warns that without strong content engagement and complete metadata, retention risks remain high. If the new subscribers encounter missing episode data or low-performing original content, churn may quickly follow. Decision-makers should capitalize on this acquisition momentum by ensuring high-quality, well-labeled content and reinforcing viewer engagement strategies immediately after sign-up.

The spike in churn on November 1, 2023 suggests a possible reaction to poor viewer experience, such as content fatigue or dissatisfaction with available offerings. This aligns with the FMEA finding that high churn risk stems from low engagement and missing metadata, especially in Episodes, Seasons, and Original Content. For decision-makers, it signals an urgent need to improve catalog completeness and promote underutilized but high-potential content to prevent further subscriber loss.

The "Top-Watched Title per Genre (Cleaned)" data reveals which specific titles are driving engagement within their respective genres across our platform. While these top titles consistently attract viewers throughout the observation period, their peaks in viewership often coincide with both periods of subscriber acquisition and, interestingly, high churn, suggesting they may be the last bastion for some leaving users. This broad engagement across genres points to a diverse audience, yet the concentration of success in only a few strong titles highlights a significant opportunity to expand and diversify content appeal throughout our entire catalog.

it's fascinating to see Show_11 consistently at the top of our most-watched titles. Even as our previous FMEA model highlighted a troubling churn rate, Show_11 somehow managed to captivate viewers, pulling them in even during those high-risk months when many others were bailing. This isn't just luck; it speaks volumes about the show's inherent quality and ability to genuinely resonate with our audience. While it's certainly a bright spot and a testament to what's possible, we can't afford to rest on its laurels. The real challenge, and our biggest opportunity, lies in understanding why Show_11 is so successful and then strategically elevating the rest of our content to match that magnetic appeal, transforming those churn risks into lasting engagement.

The missing data plot shows that Episodes and Seasons have the highest missing rates, affecting over 87% of the dataset, followed by OriginalContent, RuntimeMinutes, and AvgRating at around 65%. These missing values are largely tied to content released between 2021 and early 2022, indicating a failure in metadata ingestion or inconsistencies during that upload period. This data gap significantly limits accurate analysis of series-type content and overall viewer engagement trends. For investors, this represents a blind spot in assessing content value and portfolio performance during a critical growth window.

Viewer engagement significantly differs between original and licensed content, with original content currently underperforming and potentially not resonating well with the audience, while licensed content, likely due to recognizable titles, drives more consistent watch times. The substantial 65% "NA" entries for content type suggest widespread missing labels, hindering accurate classification and weakening insights critical for effective content strategy decisions. This indicates an urgent need for comprehensive content categorization to enhance analytical capabilities and optimize content investment.