Choice-Based Conjoint Analysis
Market research technique using discrete choice modeling to understand consumer preferences for Netflix subscription features.
Executive Summary
Choice-Based Conjoint Analysis (CBC), also known as Discrete Choice Modeling, is a powerful market research technique for understanding consumer preferences. This project applies CBC to analyze Netflix customer survey data, helping to predict how consumers value different subscription features.
What is Conjoint Analysis?
Conjoint analysis answers the critical business question:
“Which features of a product are most important to customers, and what trade-offs are they willing to make?”
Instead of asking customers to rate features individually, conjoint analysis presents realistic choice scenarios—mirroring real-world purchasing decisions.
Key Concepts
| Concept | Description |
|---|---|
| Attributes | Product features being evaluated (e.g., price, content library, video quality) |
| Levels | Possible values for each attribute (e.g., price: $9.99, $14.99, $19.99) |
| Part-Worth Utilities | Numerical values representing the desirability of each level |
| Choice Sets | Combinations of attribute levels presented to respondents |
Methodology
1. Survey Design
Design choice tasks where respondents select their preferred option from realistic product configurations.
2. Data Collection
Gather responses from a representative sample of the target market (Netflix customers in this case).
3. Statistical Modeling
Use multinomial logistic regression to estimate part-worth utilities for each attribute level.
4. Market Simulation
Predict market share for different product configurations based on the estimated utilities.
Interactive Analysis
Explore the complete conjoint analysis workflow, from data preprocessing to utility estimation and market simulation:
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Business Applications
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Pricing Optimization: Determine optimal price points that maximize revenue while maintaining customer satisfaction.
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Feature Prioritization: Identify which features drive subscription decisions and allocate development resources accordingly.
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Competitive Analysis: Simulate how changes in competitor offerings might affect market share.
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Customer Segmentation: Discover distinct preference patterns across different customer groups.
Key Insights
The analysis reveals:
- Price sensitivity varies significantly across customer segments
- Content library size is often valued more than video quality
- Optimal bundles can be constructed to maximize perceived value