To address these limitations, Deimos-One reimagined CLV beyond basic summations of discounted future revenues, addressing the core complexities of streaming economics.

In this industry, CLV isn’t just revenue minus costs; it’s a probabilistic forecast incorporating variable viewing hours, ad impressions (for hybrid models), upsell paths (e.g., upgrading to family plans), and churn probabilities influenced by content freshness, platform exclusivity, and external disruptions like blackouts or competitor launches.

We adopted a shifted beta-geometric distribution model, enhanced for streaming-specific variables, outperforming linear regressions, geometric averages, and even complex ML ensembles.

Why this superiority? Generality and robustness. Probabilistic distributions capture the non-normal spread of subscriber behaviors—where a small cohort of “superfans” (top 10% driving 40% of watch time) skews averages—without overfitting to noisy data like seasonal holiday surges or algorithm-driven recommendations.

In streaming, subscribers defy Gaussian assumptions: viewing distributions are long-tailed, with most users consuming sporadically while outliers marathon entire seasons.

The shifted beta-geometric model parameterizes this heterogeneity, modeling churn as a function of time-since-subscription with beta-distributed individual probabilities, shifted to account for initial “honeymoon” periods post-signup.

It integrates factors like churn dynamics, where probability of cancellation rises non-linearly, peaking at 3-6 months due to content exhaustion or bill shock, modulated by engagement signals (e.g., completion rates above 70% predict 20% lower churn); upsell and revenue variability, incorporating probabilistic paths for tier upgrades, add-ons (e.g., 4K streaming or offline downloads), and ad revenue from free tiers, using geometric series for recurring payments adjusted by demographic priors (e.g., younger users upsell 15% more via app notifications); and external constraints, factoring in regional limitations like bandwidth in developing markets or content licensing expirations, using Bayesian updates to refine predictions as new data streams in.

Compared to alternatives: linear regression oversimplifies, assuming constant churn and ignoring outliers, leading to 10-15% errors in LTV forecasts; geometric distributions handle basic survival but miss segment differences, underestimating revenues from loyal niches; machine learning (e.g., random forests) excels in accuracy on historical data but black-boxes insights, overfits to transient trends like a blockbuster release, and struggles with sparse data from new markets; shifted beta-geometric strikes balance: 0.5-2% variance from actuals, transparent parameters for interpretability, and scalability without retraining.

Implementation involved integrating this into the client’s analytics pipeline, linking real-time data from CDNs (content delivery networks), user profiles, and A/B tests on pricing elasticity. Dashboards visualized CLV cohorts, enabling simulations: e.g., a 10% price increase on basic tiers might boost short-term ARPU but spike churn 3% among price-sensitive users, netting negative LTV in simulations.

Note: Customers don’t fit a normal distribution as large differences exist between customers. Let’s explain and investigate what this means.

Graphed below is a comparison of four CLV model approaches: linear regression, geometric distribution, machine learning, and shifted beta-geometric distribution. Also included is the actual reality (dotted line).