Netflix Subscriptions Data Analysis

Identifying where Netflix subscribers get the the best value globally.

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Context

Product teams often use insights from product analytics to inform product strategy. This project explores how AI can be applied to improve the quality of insights generated from standard data analysis and visualize the results in a dashboard.

Objective

The overall objective is to use AI to uncover overlooked patterns, deepen analytical rigor, and produce more actionable insights with minimal human context. This is a low-context data analysis meant to understand what type of insights and visualizations AI can generated when given limited context on the data analysis goals (why the analysis is being done) and the analysis methodology (what to analyze and how).

Analysis Overview

The 2025 analysis examined Netflix subscription pricing and content availability across 128 countries—nearly double the 65 countries analyzed in 2021. The dataset captures all four subscription tiers (Basic with Ads, Basic, Standard, Premium) alongside each market's total content library. The AI conducted this analysis with minimal human guidance, receiving only the raw dataset and a general directive to identify pricing patterns and value disparities.

Scope: 128 countries across six continents (Europe, Asia, Africa, North America, South America, Oceania)

Key metrics analyzed:

• Monthly subscription costs per tier

• Content library sizes (total titles available)

• Cost per title (value metric)

• Regional pricing patterns

• Ad-supported tier availability and savings

Key Insights

1. Extreme Price Variation Reflects Market-Specific Strategies

Premium subscriptions vary by approximately 6-7x globally, with Switzerland and Liechtenstein charging over $7.50/month while countries like Nigeria and Egypt charge under $2.00/month. This dramatic disparity reveals Netflix's localized pricing approach that prioritizes market penetration in developing economies over revenue maximization.

Product implication: Pricing decisions are decoupled from content volume—high-priced markets don't necessarily offer larger libraries. This suggests Netflix bases prices on local purchasing power and competitive dynamics rather than production costs or content investment.

2. Content Availability Varies by 2:1, But Pricing Varies by 7:1

Library sizes range from 4,883 to 9,765 titles (2x difference), while premium pricing ranges from $1.01 to $7.67 (7.6x difference). Iceland offers the largest catalog with 9,765 titles, while the Democratic Republic of the Congo has the smallest at 4,883 titles.

Product implication: Value perception in streaming is driven by affordability relative to local income, not absolute content volume. A market with 5,000 titles priced at $1.50 may be perceived as better value than a market with 8,000 titles priced at $6.00.

3. Regional Patterns Reveal Strategic Market Positioning

• Europe (highest prices, largest market coverage): Average premium cost of $4.76, reflecting mature market status and established user bases. Netflix can maintain premium pricing in these markets.

• Asia and Africa (lowest prices, best value): Average premium costs of $3.48 and $2.67 respectively. Aggressive pricing aims to build market share in price-sensitive regions with high growth potential.

• North America (high prices, mature market): Despite being Netflix's home market, the U.S. charges $6.25 for Premium—among the highest globally. This reflects market maturity and limited competition from traditional TV.

Product implication: Netflix's pricing follows a portfolio approach—extracting maximum revenue from wealthy markets while investing in long-term growth through aggressive pricing in emerging economies.

4. Ad-Supported Tier Provides Strategic Flexibility

The ad-supported tier is available in only 15 select markets and delivers 30-60% savings compared to the Basic plan. This tier exists primarily in developed markets (U.S., U.K., Australia) where Netflix faces competition from ad-supported alternatives like Hulu and Peacock.

Product implication: The ads tier functions as a competitive response mechanism rather than a global pricing strategy. Netflix deploys it selectively in markets where free or low-cost alternatives threaten subscriber retention.

5. Library Size and Cost Show Weak Correlation

The correlation coefficient between library size and subscription cost is weak (correlation < 0.3), indicating pricing is determined by local economic factors, not content volume. Some expensive markets have relatively small libraries, while some affordable markets offer extensive catalogs.

Product implication: Content investment decisions and pricing decisions operate independently. Netflix can maintain high prices in wealthy markets regardless of library size, while content licensing restrictions in some regions don't justify price reductions.

Patterns & Correlations

1. The "Value Paradox" in Developing Markets

Pakistan, Nigeria, and India offer the best cost-per-title metrics (under $0.0020 per title), combining low prices with reasonably sized libraries. However, these markets likely generate the lowest revenue per subscriber despite offering the best numerical value.

Unexpected connection: The AI identified that markets with the best value metrics aren't necessarily the most strategically valuable markets. Europe's relatively poor value-per-dollar metrics generate substantially higher revenue, funding content production that benefits all regions.

2. Tier Spread Reveals Upgrade Economics

The price difference between Basic and Premium varies dramatically—from $1.50 in some markets to over $4.00 in others. Markets with smaller tier spreads (Europe, North America) suggest that upgrade behavior is less price-sensitive, allowing Netflix to maintain substantial margins on premium features like 4K streaming.

Unexpected connection: The AI noticed that markets with the largest Basic-to-Premium spreads tend to be in regions where 4K TV adoption is lower. This suggests Netflix prices premium features based on device penetration rather than uniform feature valuation.

3. Regional Clustering in Content Libraries

European countries cluster tightly around 7,500-8,500 titles, while other regions show more variation. This uniformity likely stems from pan-European licensing agreements and EU content regulations that mandate regional availability.

Unexpected connection: Despite Europe's unified content approach, pricing varies substantially within the region ($2.58 in Slovenia vs. $7.67 in Liechtenstein). Economic factors override licensing homogeneity in pricing decisions.

Visualizations

• Price Distribution Charts: Global pricing follows a long-tail distribution where a minority of markets (Europe, North America) subsidize aggressive pricing elsewhere.

• Regional Comparison Charts: Netflix's footprint prioritizes developed markets with established payment infrastructure over market size or population.

• Value Rankings: Value disparities are even more extreme than raw price differences, suggesting that content library sizes partially compensate for price gaps but don't eliminate value inequalities.

• Correlation Scatter Plot: Netflix treats pricing and content licensing as separate strategic levers, adjusting each independently based on market conditions.

• Ad-Tier Savings Analysis: Ad-tier pricing is calibrated competitively—Netflix offers deeper discounts in markets with strong ad-supported alternatives (e.g., U.S. with Hulu/Peacock).

  
  

Takeaways

What the AI Uncovered Without Guidance

1. Decoupling of pricing from content volume: The AI independently calculated correlation coefficients and recognized that library size doesn't drive pricing—a finding that required synthesizing multiple data dimensions.

2. Regional pricing clusters: Without being told about economic development levels, the AI grouped markets by pricing patterns and inferred that these clusters aligned with regional economic conditions.

3. Ad-tier strategic deployment: The AI noted that the ad-supported tier appears only in select developed markets, suggesting competitive positioning rather than global rollout.

4. Value paradox identification: The AI recognized the counterintuitive finding that best-value markets likely aren't the most profitable, connecting numerical metrics to business strategy implications.

What Types of Insights Emerged

• Descriptive insights (expected): Basic statistics like average costs, library size ranges, and regional counts. These are table-stakes findings that any analyst would identify.

• Comparative insights (moderate value): Rankings of most/least expensive markets, best/worst value countries, and regional comparisons. These required synthesizing multiple data points but followed predictable analytical patterns.

• Strategic insights (high value): Observations about pricing strategy decoupling from content, the ad-tier's competitive function, and the value paradox. These insights connect data patterns to business implications without explicit prompting.

Implications for AI-Assisted Product Analytics

Best use cases for low-context AI analysis:

• Exploratory data analysis to identify unexpected patterns

• Generating comprehensive market comparisons across many dimensions

• Creating visual dashboards for stakeholder communication

• Calculating statistical relationships and ranking metrics

Where human-AI collaboration is essential:

• Interpreting business implications of data patterns

• Evaluating strategy effectiveness and recommending adjustments

• Connecting findings to competitive context and market dynamics

• Prioritizing which insights warrant action vs. monitoring

Process recommendation: Use AI for initial pattern discovery and visualization, then inject human context through iterative refinement—asking AI to evaluate specific hypotheses or explore particular strategic questions based on initial findings.

The quality of insights from low-context AI analysis is sufficient for hypothesis generation and exploratory research, but insufficient for strategic decision-making without additional business context and human interpretation.