Streaming As A Service

Andrew Mao

2025-03-24

INTRODUCTION

Streaming services have transformed the way people watch their favorite shows, movies, and sports by moving to a Subscription Video on Demand (SVOD) model. Bundling subscriptions has gained prominence due to convenience and perceived value, yet pricing these bundles is challenging because consumer willingness to pay varies significantly across demographics and preferences.

This project explores methodologies for optimal bundle pricing, aiming to:

  • Enhance understanding of consumer behavior

  • Inform effective pricing decisions

  • Refine methods for assessing willingness to pay (WTP)

PROBLEM STATEMENT

Frequent and sometimes high price increases at established services (e.g., Netflix) have sparked user backlash, while competitor platforms (Disney+, Hulu, Amazon Prime Video) expand content libraries or bundle multiple offerings under a single rate. Meanwhile, ad-supported tiers introduce additional complexity when setting optimal prices.

Key Managerial Problem

Streaming services must balance competitive pricing and profitability while meeting diverse consumer expectations regarding content, ads, and budget constraints.

ANALYTICS OBJECTIVES

We specifically seek to:

  1. Compare Van Westendorp’s Price Sensitivity Meter, the Gabor-Granger Method, and Conjoint Analysis to assess willingness to pay (WTP) for streaming bundles.

  2. Identify consumer price thresholds, feature trade-offs, and optimal pricing strategies.

  3. Develop a unified pricing framework that synthesizes multiple methods for robust decision-making.

KEY METHODS

  • 4 methods to test different psychological and mental barriers.

    1. Van Westendorp’s Price Sensitivity Meter (PSM)

    • Analyzes acceptable price ranges and psychological price points.

    • Identifies the optimal pricing window that balances perceived affordability and quality.

    2. Conjoint Analysis

    • Evaluates price sensitivity in the context of multiple attributes (e.g., ad levels, content variety).

    • Identifies feature trade-offs and segments customers based on their preferences.

    3. Gabor-Granger Method

    • Tests consumer purchase likelihood at various price points (e.g., $7.99, $9.99, $12.99).

    • Helps determine price elasticity and potential revenue-maximizing points.

    4. Integrative Synthesis

    • Compares and contrasts insights from all three methods to spot discrepancies in stated willingness to pay vs. actual price thresholds.

EXAMPLE CHART

Below is a simple demonstration of how you might illustrate demand curves.


::: {.cell}

```{.r .cell-code}
# A sample plot (dummy data)
price_points <- c(7.99, 9.99, 12.99, 14.99, 17.99)
demand_pct   <- c(80, 68, 52, 35, 20)

plot(
  price_points, demand_pct,
  type = "b", 
  main = "Illustrative Demand Curve",
  xlab = "Price (USD/month)", 
  ylab = "Subscription Likelihood (%)"
)

:::

Questions?

Streaming As A Service