Netflix User Behavior Dataset

By shrijeetverma13 · March 16, 2026

Data source:

Users are most likely to churn at 19 months of account age, where the churn rate peaks at 23.31% based on 871 users. A bar chart has been generated showing how churn rates vary across all account ages, making it easy to spot this peak and compare trends throughout the customer lifecycle.

The analysis successfully calculated churn rates across all subscription types and produced a bar chart and data tables showing the breakdown. The visualization displays each subscription tier side by side, making it easy to compare attrition levels at a glance.

A bar chart has been generated showing churn rates for each country, with bars colored by user volume. This gives you a clear visual comparison of which countries have the highest and lowest churn rates and how many users each country has.

The total monthly revenue at risk from churned users is $122,380.36, broken down by subscription type. A bar chart and supporting data tables have been generated to visualize how this revenue loss is distributed across different subscription tiers.

The analysis reveals how favorite genres are distributed across different age groups (18-25, 26-35, 36-45, 46-55, and 56-64). A grouped bar chart has been generated showing the number of users per genre within each age group, making it easy to spot which genres dominate at different life stages.

The distribution of days since last login is remarkably similar between churned and non-churned users. Both groups have an average of 29.4 days and a median of 29.0 days, with nearly identical standard deviations (~17.3–17.4 days). The range spans from 0 to 59 days for both groups. A histogram has been generated showing the overlapping distributions visually.

The analysis reveals that churned and retained users have remarkably similar viewing behavior across all three metrics. A grouped bar chart and detailed tables were generated to visualize these comparisons. The differences between churned and retained users are minimal — almost negligible — suggesting that these engagement metrics alone may not be strong predictors of churn.

The analysis examined whether users who binge-watch more frequently tend to stay subscribed longer. A bar chart and data table were generated showing churn rates across three viewer categories: Casual (0–3 sessions), Moderate (4–7 sessions), and Heavy (8–14 sessions).

Both primary device type and number of devices used have virtually no impact on average watch time or churn. Across all device types (Laptop, Mobile, Smart TV, Tablet), average watch time hovers tightly between 154–155 minutes and churn rate is a uniform 20%. Similarly, users with 1, 2, or 3 devices show only a slight uptick in watch time (153 to 156 min) with churn remaining constant at 20%. Two visualizations were generated: a grouped bar chart showing watch time by device and devices used split by churn status, and a scatter plot mapping churn rate vs. watch time across device combinations.

The average ratings given by churned versus retained users are remarkably similar across all subscription types. A grouped bar chart and data table have been generated to visualize these comparisons clearly.

The analysis produced a chart and data table showing how monthly revenue and user counts are distributed across countries. The visualization displays a dual-axis chart with bars representing total revenue per country and a line tracking user counts, making it easy to compare both metrics side by side.

Two interactive charts were generated to show how subscription types are distributed across genders and how average monthly fees compare. The first bar chart displays the count of each subscription type (grouped by gender), and the second shows the average monthly fee per subscription type for each gender group.

The analysis examined whether payment method has a statistically significant relationship with churn rate. Three data tables were generated showing churn rates broken down by each payment method, along with chi-square test statistics. The tables reveal how each payment method compares to the overall average churn rate, highlighting which methods are associated with higher or lower customer churn.

Among the 4,331 users with account ages under 6 months, the overall churn rate is 20.4%. A bar chart has been generated showing how each engagement metric correlates with churn for this group. The visualization ranks metrics by their correlation strength, making it easy to identify which behaviors most strongly predict early churn. The color scale highlights positive vs. negative correlations — metrics shown in red tend to increase churn risk, while blue ones are associated with retention.

Out of 14,873 Basic subscribers, 5,563 (37.4%) demonstrate Premium-level engagement across at least 4 of 6 key metrics — making them strong upsell candidates. Of these, 4,537 are still active and can be targeted immediately. These users actually outperform Premium benchmarks: they watch 200 min/session vs. the Premium median of 155 min, have 13 sessions/week vs. 10, and complete 75% of content vs. 65%. Tables showing the top candidates and their engagement profiles have been generated.

Users were divided into 4 engagement segments based on their content interactions and watch sessions per week, using median splits (Content Interactions median = 24, Watch Sessions median = 10 per week). Two data tables were generated summarizing the segment characteristics and key metrics for each group.