Netflix’s average content ratings have declined in recent years, but this should not be mistaken for a simple drop in quality. Instead, it reflects a structural shift in content strategy. As a subscription-driven global streaming platform, Netflix is not a traditional film studio. Its content ecosystem is built around user retention, discovery, and conversion—not critical acclaim.

Rather than aiming for every title to be a high scorer, Netflix focuses on producing or acquiring a large volume of “85-point” content—mid-to-high-quality shows and films that, when delivered in diverse formats and updated frequently, help sustain user engagement and subscription growth. This approach improves distribution efficiency and lowers churn.

Netflix 的平均内容评分近年来有所下降，但这不应被误认为是质量的简单下降，而是反映了其内容战略的结构性转变。作为一个订阅驱动的全球流媒体平台，Netflix 并非传统的电影工作室。其内容生态系统建立在用户留存、发现和转化率之上，而非口碑。

Netflix 的目标并非让每部作品都获得高分，而是专注于制作或收购大量“85 分”内容——这些中高质量的节目和电影，如果以多样化的格式交付并频繁更新，就能帮助维持用户参与度和订阅量增长。这种方法提高了分发效率并降低了用户流失率。

Extended Thinking: Are We Moving Beyond Scores?

As streaming platforms evolve, traditional rating systems are becoming less meaningful. Scores are essentially a form of opinion expression, often influenced by personal emotions, cultural context, and extreme viewpoints. On platforms like IMDb, user reviews tend to skew toward either highly positive or strongly negative experiences, leading to polarization.

In this context, the success of content is no longer defined by critics or high ratings alone. Instead, it increasingly depends on behavioral indicators such as watch time, completion rate, rewatch frequency, and content sharing. These metrics offer a more accurate reflection of user engagement and content value.

At the same time, content evaluation standards across global audiences are becoming increasingly diverse. It is no longer realistic to expect one piece of content to resonate universally—each region has its own cultural expectations and its own definition of a “high-quality” show.

With the rise of AI-driven personalized recommendation systems, content evaluation is shifting from a standardized model to an individualized one. Platforms may even intentionally promote “mid-tier but widely appealing” content to optimize retention. As user-level differences become more pronounced, platforms are moving from a score-based logic to a behavior-driven model of content success.

随着平台的发展，传统的评分体系正逐渐失去原有的意义。评分本质上是一种主观表达方式，容易受到情绪、文化背景以及极端观点的影响，常表现出明显的两极化倾向。尤其是在像 IMDb 这样的评分平台上，用户往往只在极度满意或极度失望时才会留下评分与评论。

在这种背景下，内容是否“成功”越来越不取决于影评人意见或高评分标签，而是基于用户的实际行为数据，例如停留时长、完成率、复播次数与分享频率等。这些指标更能准确反映用户对内容的真实偏好和投入程度。

同时，全球观众的内容评价标准正在加速分化。很难再有一部作品能“全球通吃”，因为每个地区都有其特定的文化语境与内容偏好，也都有自己对“高分内容”的定义。

在 AI 和个性化推荐技术不断演进的推动下，内容的评价体系正从统一标准转向“个体差异化”的模式。未来，平台甚至可能更倾向于放大那些“中上质量、广泛可接受”的内容，以提升整体用户留存效率。个体喜好的差异性也将在这种机制中被进一步放大，推动平台从“评分逻辑”转向“行为逻辑”。