📈 economics

Scenario-Based Markov Modeling: An Exploratory Analysis of AI's Potential Impact on Customer Retention – The Netflix Case

This exploratory study utilizes a Markov chain simulation and theoretical frameworks to demonstrate that, under specific assumptions, AI-driven personalization mechanisms could hypothetically more than double customer retention for Netflix compared to a low-AI baseline, while highlighting the need to view AI as a dynamic capability and addressing associated ethical concerns.

Original authors: Tahar Braknia

Published 2026-07-15

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Tahar Braknia

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you're running a massive, 24-hour digital video store called "Netflix," but instead of shelves, you have a giant, glowing screen that knows exactly what you want to watch before you even do. Now, imagine a rival video store down the street that just throws random movies at you and hopes you stay. This paper asks a big question: How much better is the smart store at keeping customers compared to the clueless one?

The authors didn't just guess; they built a digital time machine called a "Markov simulation." Think of this simulation as a giant, virtual video game where they dropped 1,000,000 imaginary people into two different worlds for 12 months.

The Two Worlds

In World A (The High-AI Scenario), the store uses super-smart AI. It watches what you pause, rewind, and click. It changes the movie posters (thumbnails) to match your taste and even bets on which new shows to make based on what you like.
In World B (The Low-AI Scenario), the store is the "theoretical baseline." It's like a video store from the past with no smart features—just basic lists and generic covers. The paper is very clear: this isn't a real competitor; it's a "what-if" scenario to see how powerful AI really is.

The Big Reveal

After running the simulation 100 times to make sure the numbers were steady, the results were pretty wild.

In the Smart Store (High-AI), 68.71% of the customers were still subscribed after a year.
In the Clueless Store (Low-AI), only 32.67% stayed.

That means the smart store kept more than double the number of people. The paper says this is a 110.3% relative improvement. To put it in plain numbers: for every 1,000,000 subscribers, the smart store kept about 360,000 more people than the clueless one would have.

But here is the catch: The authors are very careful to say this isn't a crystal ball predicting the future. It's a simulation based on assumptions. They didn't have secret access to Netflix's private data; they used public reports and educated guesses to build their game. So, while the numbers suggest AI is a massive game-changer, they are illustrating a potential, not proving a guaranteed fact.

How the Magic Works (The Three Tricks)

The paper breaks down why the smart store wins using three "tricks" that the AI plays:

The Mind-Reader (Hyper-Personalized Recommendations):
Imagine a shopkeeper who knows you love sci-fi and hates horror. Instead of showing you a scary movie poster, they slide a sci-fi one right into your hand. The paper suggests this AI drives 80% of what people actually watch. It's like the store anticipates your hunger before you even feel it.
The Shape-Shifting Window (A/B Tested Interface):
This is like the store changing the color of the door or the font on the sign depending on who is walking by. The AI runs hundreds of tests to see which movie cover makes you click. The paper notes this can boost clicks by 20–30%. It's not just about what you see, but how it looks to you.
The Crystal Ball (Predictive Content):
Before a new show is even filmed, the AI looks at the data and says, "Hey, people who like this director and these actors will love this story." It's like a chef guessing the perfect recipe before tasting the ingredients. This helps the store invest in shows that are almost guaranteed to be hits.

The "What If" Safety Check

The authors didn't just stop at the big numbers. They played a game of "what if" to see how sturdy their results were. They asked: What if the AI is slightly less good at getting people to come back?
They tweaked the numbers by 20% up and down. Even when they made the AI slightly worse, it still kept way more people than the clueless store. This suggests the idea that "AI helps retention" is robust, even if the exact percentage might wiggle a bit.

The Rules of the Game (What the Paper Says "No" To)

It's important to know what this paper doesn't claim:

It does not say AI is a magic wand that solves every problem.
It does not claim these numbers are the exact reality of Netflix today (real Netflix might be even better because of brand loyalty and exclusive shows the model didn't count).
It does not say the "clueless store" actually exists. The paper explicitly states that no major streaming service today is that dumb; they all have some AI. The "Low-AI" world is just a theoretical floor to measure against.
It does not ignore the dark side. The paper warns that while AI is great, it brings risks like privacy invasion and "filter bubbles" (where you only see what you already like).

The Bottom Line

This paper is like a scientist building a model of a rocket to see how high it could fly. The model suggests that if you use AI to personalize the experience, you could keep 68.71% of your customers, compared to 32.67% if you didn't. It's a strong hint that AI is a superpower for keeping people happy, but the authors remind us that this is a scenario-based estimate, not a final verdict. The real world is messy, and while the simulation points to a huge advantage, the exact numbers depend on how perfectly the AI works in real life.

Technical Summary: Scenario-Based Markov Modeling of AI's Impact on Customer Retention (The Netflix Case)

1. Problem Statement and Research Gap

The global streaming market is projected to reach $332 billion by 2027, creating intense competition for subscriber retention. While Artificial Intelligence (AI) is widely recognized as a critical differentiator for platforms like Netflix—driving 80% of viewed content through recommendation engines—existing literature suffers from three primary limitations:

Lack of Quantitative Simulation: Most studies rely on qualitative descriptions of AI features or cross-sectional surveys of user perceptions, failing to simulate the dynamic, cumulative effect of personalisation on churn over time.
Theoretical Silos: Frameworks such as the Stimulus-Organism-Response (S-O-R) model and Dynamic Capabilities Theory are typically applied in isolation, lacking an integrated lens to connect micro-level user psychology with macro-level firm strategy.
Outdated Empirical Grounding: Foundational research on Netflix's AI predates 2020, missing the post-pandemic shifts in streaming behavior and the intensified market competition of 2023–2025.

Consequently, there is no quantitative model estimating how AI-driven retention might evolve under different hypothetical scenarios. This study addresses this gap by posing three research questions regarding Netflix's AI utilization, the quantitative impact of personalisation on retention compared to baselines, and the associated ethical challenges.

2. Methodology

The study employs an exploratory sequential design combining thematic qualitative analysis with a quantitative scenario model.

2.1 Qualitative Component

A thematic analysis was conducted on Netflix's publicly documented AI applications, drawing from peer-reviewed literature, SEC filings, and corporate technology blogs. This identified three core mechanisms:

Hyper-personalised Content Recommendations: Using collaborative filtering and Restricted Boltzmann Machines to process billions of interactions (play, search, pause/rewind) to reduce cognitive load and predict needs.
A/B Tested Interface Optimisation: Continuous testing of thumbnails, row ordering, and notification timing, with internal data suggesting a 20–30% lift in click-through rates (CTR) for personalised thumbnails.
Predictive Analytics for Content Acquisition: Using AI to forecast audience size and guide investment decisions (e.g., House of Cards), shifting content marketing from reactive to proactive.

2.2 Quantitative Component: Markov Chain Simulation

To quantify the potential magnitude of AI's effect, the authors developed a discrete-time Markov chain simulation.

State Definitions: Users are categorized into four mutually exclusive states: Engaged (E) (>5 hours/week), At Risk (R) (<5 hours/week), Dormant (D) (no activity for 30 days), and Churned (C).
Scenarios:
- High-AI Scenario (Netflix): Parameters reflect reported AI capabilities (e.g., 2.17% monthly churn for Engaged users, 0.25 reengagement probability from At-Risk).
- Low-AI Scenario (Theoretical Baseline): A counterfactual platform without advanced personalisation (e.g., 5.0% monthly churn for Engaged users, 0.10 reengagement probability).
Simulation Parameters:
- Cohort: 1,000,000 simulated users.
- Duration: 12 months.
- Initial Distribution: 85% Engaged, 10% At-Risk, 4% Dormant, 1% Churned.
- Method: Monte Carlo approach with multinomial random draws (100 iterations) to calculate mean retention rates and 95% confidence intervals.
Sensitivity Analysis: The model tested the robustness of results by varying the reengagement probability ( $p_{R \to E}$ ) by $\pm 20\%$ , as well as dormant-state churn and initial cohort distribution.

Note on Causality: The authors explicitly state that the model does not provide causal estimates of Netflix's actual retention. The transition matrices are deterministic inputs derived from literature and assumptions; the results illustrate the potential magnitude of AI effects under specified conditions, not statistical inferences.

3. Key Results

3.1 Quantitative Findings

After 12 months of simulation:

High-AI Scenario: Achieved a retention rate of 68.71% (95% CI: 68.31% – 69.11%).
Low-AI Scenario: Achieved a retention rate of 32.67% (95% CI: 32.17% – 33.17%).
Relative Difference: The High-AI scenario retained more than double the proportion of users, representing a 110.3% relative improvement.
Absolute Difference: A gap of 36.04 percentage points (equivalent to ~360,000 additional retained users per million simulated subscribers).

Temporal Dynamics: The divergence in retention rates became observable from Month 3 and widened progressively, suggesting that AI benefits accumulate over time through repeated positive engagement experiences. By Month 6, the gap was ~15 percentage points; by Month 12, it expanded to 36 percentage points.

Primary Drivers: The simulation identified two parameters as the main drivers of the retention gap:

Reengagement Probability ( $p_{R \to E}$ ): Higher in the High-AI scenario (0.25 vs. 0.10).
Dormant-to-Churn Probability ( $p_{D \to C}$ ): Lower in the High-AI scenario (0.10 vs. 0.20).

3.2 Sensitivity Analysis

The results showed positive sensitivity to the reengagement parameter. A 20% decrease in $p_{R \to E}$ reduced 12-month retention by approximately 6.6 percentage points, while a 20% increase raised it by 5.5 percentage points. The directional conclusion—that higher reengagement probability substantially improves retention—remained robust across the tested range.

3.3 Contextual Benchmarking

When compared to real-world benchmarks, the High-AI simulation (68.71%) fell within the range of major streaming platforms but was lower than Netflix's actual estimated retention (~77%), likely due to the model excluding factors like brand loyalty and exclusive content. The Low-AI scenario (32.67%) was significantly lower than any real-world platform, confirming its status as a theoretical construct rather than a realistic competitor benchmark.

4. Key Contributions

4.1 Methodological Contribution

The study offers a replicable scenario-based simulation methodology that combines thematic literature review with quantitative Markov modeling. This approach provides an assumption-bounded estimate of possible AI-related retention differences, serving as a hypothesis-generating tool for subscription-based platforms where user-level data is unavailable.

4.2 Theoretical Contribution

The paper integrates the Stimulus-Organism-Response (S-O-R) framework with Dynamic Capabilities Theory.

Micro-Level: It applies S-O-R to explain how AI features (stimuli) affect user cognition and affect (organism), driving engagement or churn (response).
Macro-Level: It utilizes Dynamic Capabilities Theory to frame AI as a firm-level capability for sensing (identifying preferences), seizing (acting via recommendations), and reconfiguring (adapting marketing mix).
UGT Extension: The findings extend Uses and Gratifications Theory (UGT) by suggesting AI shifts user behavior from active need gratification to predictive need anticipation, reducing search costs but raising questions about user agency.

4.3 Practical Contribution

The study provides actionable insights for marketing managers:

Strategic Investment: AI personalisation should be treated as a strategic priority, not a marginal feature, given the potential for significant retention gaps.
Holistic Personalisation: Efforts must extend beyond content recommendations to interface elements (thumbnails, notifications) via continuous A/B testing.
Ethical Governance: Managers are advised to embed ethical considerations early, including algorithmic audits, explainable AI interfaces, and privacy-preserving techniques.

5. Significance and Claims

The paper positions itself as an exploratory and hypothesis-generating study rather than a definitive causal analysis.

Modest Claims: The authors explicitly state that the 110.3% improvement is an upper-bound estimate under idealised conditions and a "theoretical illustration" of the maximum possible effect. It is not a prediction of what would happen if Netflix abandoned AI, nor does it claim to estimate actual prevented churn.
Value Proposition: The primary significance lies in demonstrating the order of magnitude by which personalisation could enhance retention under transparent assumptions. It bridges the gap between qualitative case studies and quantitative modeling, offering a framework for future empirical research to prioritize measuring the causal effect of reengagement probabilities.
Ethical Awareness: The study highlights the trade-offs of AI, including privacy, algorithmic bias, and the risk of "filter bubbles," advocating for responsible deployment.

In conclusion, the paper argues that AI is a strategic capability capable of reshaping competitive advantage in digital marketing, but its value must be understood through both its operational effectiveness and its ethical implications.