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A Discrete Choice Take on the Netflix Price Hike

Last week, Netflix announced that it would be increasing prices for new subscribers by one dollar for the Basic plan (Up to $8.99 from $7.99/month) and by two dollars for its Standard (Up to $12.99 from $10.99/month) and Premium (Up to $15.99 from $13.99/month) plans. The price increase will roll-out to existing users over the next three months. 

What does this mean for Netflix users and potential prospects? Well, Mike Snider from USA Today responded with an article titled “Netflix price increases could cause some subscribers to downgrade, cancel streaming service”. You can read the full article here.

But I’d have to guess that Netflix did it’s research to mitigate their losses and guarantee more revenue. They might have even used a discrete choice technique called conjoint analysis to do this! (Note - I personally have no idea if Netflix actually used conjoint analysis, I can only dream they did!)

Just imagine using a technique that solicits feedback of both users and prospects and can help answer questions like - How much are people willing to pay for a Premium subscription? Do we need to offer more features/benefits in the Premium plan to mitigate detractors or can we just raise the price given our stellar original content (i.e., Bird Box)?

Choice-Based Conjoint, or CBC, will help answer all of these questions. First, Netflix needs to break down its plans into their component parts. Pretty easy, as this is how they define their subscription plans. See their new pricing plan below and on their website.

Netflix can then turn each of the items on the left into Attributes, followed by their subsequent levels. But that’s not all! They could test different levels of price for all three plans - Basic, Standard and Premium - so that they can optimize on the appropriate prices for each plan. They could also test different configurations where Ultra HD might only be available on the Premium plan, and even test new ideas for features/benefits! A simple example CBC question might look like the following:

In a CBC exercise, respondents complete a series of questions like the one above, typically around 8 to 15 questions. The questions are carefully designed using experimental design principles of independence, balance, etc. so that based on each respondent’s choices, we can statistically deduce at what point a user might downgrade from a Premium plan to another plan, or detract entirely. The results of conjoint analysis include a full set of preference scores, or part-worth utilities, for every level of every attribute tested in the study. These scores would allow Netflix to gauge the price sensitivity and elasticity of their different plans. The scores can be taken into a market simulator to test a variety of what-if scenarios, estimate shares of choice and even understand substitution and cannibalization effects. Advice that is invaluable to any subscription based product. 

While this is a hypothetical scenario, you can see that by asking respondents to make choices among a set of products we can arrive at more realistic estimates than many other pricing techniques. Not to mention, much richer data! But there is so much more to applying conjoint analysis than is explained here - my goal is just to help you understand what conjoint analysis can do for you.

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