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2️⃣
Myth 2. Revenue from bundles should be allocated based on usage
Our Myth-maker lives in the Bay Area tech scene where Usage is seen as the Great Equalizer, so when asked the question “how should revenue from a bundle be distributed among providers”, our Myth-maker quickly and energetically exclaims: “By Usage of course!!”
A number of years back, I was looking through cable viewership data, and found an interesting data point. At the time, ESPN and History Channel were getting almost the same amount of usage - e.g. if you looked at their rating points, viewership share, etc, they were quite comparable. And yet if you were to look at their carriage fees (the amount of money the cable company pays in fees to each provider out of your monthly cable bill), there is a ~20x difference. History Channel can get from $0.20-$0.40 per month per subscriber, while ESPN collects $4-6 per month per subscriber.
So is this unfair? Shouldn't they be paid the same, based on Usage?
Thesis 2: The most fair way to distribute revenue to providers in a bundle is by Marginal Churn Contribution, not Usage.
I propose that there’s an alternate valuation scheme here. Colloquially, it is referred to as “Anchor Value”. I would describe it more precisely as “Marginal Churn Contribution” (aka MCC) . In other words, I think that the best way to distribute money to provider X within a bundle is to ask “if I were to remove X from the bundle, how many people would churn?”. Similarly, you could ask the same question from a subscriber acquisition perspective (“If I were to add X to the bundle, how many new subscribers would I earn?”) ー also known as Marginal Subscriber Acquisition Cost Contribution (aka MSacC). But for simplicity, I’ll pretend they are interchangeable for now and focus on the churn-based model.
If you believe this framework, then the reason ESPN would receive ~20x the carriage fee of the History Channel is because removing ESPN from the bundle would theoretically cause roughly 20x the number of subscribers to churn as would removing History Channel from the bundle.
For example, let’s imagine that if ESPN were removed from the cable bundle then 10% of subscribers would churn. If the average monthly cable bill is $50/month, and roughly 100M households subscribe to cable, this would mean 10M households canceling, or a loss of 10M * $600/yr = $6B in annual revenue at risk. So the cable companies agree to pay the $6B, but distribute it across all 100M households, resulting in a carriage fee of $60/yr or $5/month.
One way to calculate WholesalePrices
So not only do I believe that the correct way to distribute money in a bundle is not by Usage, I also believe that the value of a product in a bundle can be determined mathematically in a way that approximates the Marginal Churn Contribution. The general statement of this principle would be as follows:
Corollary 2.1
WholesalePrice (ProductX in BundleY)
= RetailPrice (BundleY) * MarginalChurnContribution (ProductX in Population of BundleY)
Here’s the definition of some of those terms
WholesalePrice is an interesting concept. The idea is that every provider in the bundle gets paid a portion of the customer’s bundle payment for every customer. In some sense, we’re distributing the SuperFan value of the bundle over the broader bundle population which includes both CasualFans and NonFans.
Putting this corollary in layman’s terms, it says that if I’m going to distribute money from my bundle between products, I should run the exercise of removing that product from the bundle and seeing how many customers I lose, and establish a total amount of money I would lose. Then I should turn around and distribute that sum across the population of my bundle and pay the provider that amount per subscriber.
Another way to calculate WholesalePrices
So that’s interesting and useful, but is less practical because it requires removing products in order to establish fair WholesalePrices. I think we can come up with an even more interesting and powerful corollary:
Corollary 2.2:
The best bundling outcome will have:
WholesalePrice (ProductX in BundleY) = RetailPrice (ProductX)
* SuperFan% (ProductX in Population of BundleY)
Let’s define the terms again
Now let’s try an example. For example, imagine that I am trying to include a music service (ProductX) into my bundle (BundleY).
This may seem counter-intuitive at first. How can I take a service that (successfully) charges $10/month and only give it $1/month per subscriber? Let’s run through a hypothetical negotiation:
In fact, I think this pattern of negotiation is roughly what each cable carriage dispute oscillates between and where it eventually settles.
Ok but I said that this holds true in the “best bundling outcome” - what does that mean?
Let’s take two extremes:
And just to make the math specific, let’s assume that BundleY has 40M subscribers paying $10/month and ProductX has 10M subscribers paying $10/month.
Let’s start with Case #1: Fully overlapped SuperFan base. So in this case, of the 40M BundleY customers, 10M of them are already SuperFans of ProductX and are paying $10/month for the service. There are two interesting perspectives:
Ok let’s look at Case #2: Fully Distinct SuperFan base. Now in this case, none of the current 10M ProductX subs are part of the 40M BundleY subs. Let’s look at the perspectives again, perhaps in reverse:
Another way to state this is that the bundle is “optimal” when Corollary 2.1 and Corollary 2.2 are equal: i.e.
The bundle is "optimal" when:
RetailPrice (BundleY) * MarginalChurnContribution (ProductX in Population of BundleY)
= RetailPrice (ProductX) * SuperFan% (ProductX in Population of BundleY)
This is interesting because establishing SuperFan% is much easier than understanding MarginalChurnContribution. For the former, you can look at the a-la-carte subscribers as a proxy, whereas for the latter, you eventually have to remove the product from the bundle to get a proper estimate.
But perhaps even more interestingly, this leads naturally to a very counter-intuitive conclusion in Myth 4: that the best bundles are ones that minimize superfan overlap. But let’s get through Myth 3 before going there.
Some things I’m leaving out for a separate discussion
For brevity, I’m leaving a few things out so I’ll just list them briefly:
A closing example
Just to close this section, here’s another real-world example: In January 2014, the WeatherChannel and DirecTV went through a contract dispute haggling over their carriage fee (AKA WholesalePrice in our constructs). The WeatherChannel put out a as a part of their dispute arguing that DirecTV would lose 1.6M subscribers without the WeatherChannel. The that the typical WeatherChannel carriage fee is $0.13 per subscriber per month, implying that the MarginalChurnContribution is ~0.1-0.2% (on a DirecTV ARPU of $50-100/mo). In other words, of the 20M DirecTV subs, all that is necessary to justify that carriage fee is for 20k-40k subscribers being WeatherChannel SuperFans and at risk of churn… much much less than the 1.6M than the WeatherChannel argued. This is an interesting case because the MCC is so small that it is very difficult to assess - this is no surprise since as bundles grow, the goal of the bundler is to add enough goods that any individual anchor would have very little impact on the bundle if it were to be removed. In doing so, it makes MCC very hard to measure, which leads to brinksmanship negotiations - i.e. sometimes ProductX actually has to be removed from BundleY to see the true MCC. One guess (just a guess) is that DirecTV took WeatherChannel dark, measured customer complaint volume, and then came back to the negotiating table with better data.
Next Section: , shift from the provider perspective to the consumer perspective
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