Yes, Churn is bad — but maybe we're looking at Churn the wrong way?
Yes, Churn is bad — but maybe we're looking at Churn the wrong way?
The magic of "Net Churn"
Data from this story was originally published on November 11 in a Wall Street Journal article titled “A New Streaming Customer Emerges: the Subscription Pauser”.
Earlier this year, I published a piece titled “OK, so, streaming has a churn problem…” The headline of that piece was that Churn Rates amongst major streaming companies had tripled in the past four years.
Of course, Churn is almost always a bad thing, all else equal. We should not celebrate Churn. But, what if Gross Churn Rates aren’t the right way to evaluate these services?
Maybe streaming doesn’t have a churn problem
How could this be, you ask? The secret rests in a simple 3-letter word: n-e-t.
Gross Churn Rate = # of cancels / Subscribers
Net Churn Rate = (# of cancels - # of Resubscribers) / Subscribers
The difference: Net Churn gives services credit for returning users in a way Gross Churn does not. This might matter more than you think in the world of streaming.
We know entertainment streaming services are cyclical: whether for a hit title or live sports, users often sign-up for the season, cancel during the offseason, and resubscribe at the start of the next season. There is industry consensus that this is problematic, and most practitioners are very focused on minimizing off-season churn by programming “shoulder content” to appeal to users in between seasons, but this behavior is still very common. There are two scenarios where this behavior is acceptable:
Scenario #1: the pattern is predictable and services can build strategies around it
Scenario #2: no additional marketing spend is required to generate the resubscribe event (beyond the fixed content spend)
Why this matters in streaming
Unlike with B2B subscriptions, resubscribe behavior is extremely common. Antenna observes that 1 in 3 Subscribers resubscribe within 6 months of canceling. In many other subscription businesses, when you lose a user, you expect they will never return. In streaming, on the other hand, the default assumption is that they likely will return within the next year.
The results: Net Churn vs. Gross Churn
When we compare Net vs. Gross Churn Rates, Antenna observes a meaningful difference. Using Apple TV+ as an example, Gross Churn Rate is 6.7% vs. Net Churn Rate of 3.8%. Thought experiment: would the prevailing industry narrative be “there’s a churn problem” if Churn Rates appeared to be roughly half of current industry averages?
The Gross vs. Net gap is most pronounced for hits-driven services, while “utilities” such as Netflix have a minimal difference between Gross and Net Churn (1 percentage point differential between Gross and Net for Netflix).
Another data point: Retention vs. Survival
Survival vs. Retention is a cohort-based approach to analyzing loyalty while giving consideration to resubscription behavior.
Survival Rate: of those who Sign-up in period X, how many are still Subscribed in period X+n. For this calculation once a user cancels, they cannot re-enter the cohort.
Retention Rate: a similar calculation, but the difference is a user who cancels and returns can re-enter the cohort.
When analyzing Survival and Retention Rates, Antenna observes (simple average):
Survival: 32% of users were still Subscribed at month 12
Retention: 50% of users were still Subscribed at month 12
The optimistic view
The underlying reason why reported Gross Churn Rates have been so alarming is because they tell a negative story about the underlying unit economics of the streaming business. For example:
You pay $100 (not including fixed programming costs) to acquire a user.
That user Churns after 9 months, paying $135 ($15/mo).
And you haven’t even taken into account all of your fixed costs!
Uh-oh.
However, if that user later returns for the next season at minimal additional cost to the service, and stays for another 9 months, suddenly the math starts to look more favorable (again, excluding content spend).
The dynamic that we’re looking for here is known as the “smiling retention curve,” which is a hallmark of many great subscription businesses: after the initial fall-off, previously canceled users actually start to return to the service in great numbers than those who are leaving. This is fantastic because it shows that, the longer you run the business, the more profitable it will get.
Winning tactics
Streaming is not the only industry to exhibit these dynamics. How about dating or weight loss apps, where the value proposition of the service is to create a world in which you do not need the service any longer? Or gyms, which expect high rates of churn and seasonal resubscription?
Here are some of the tactics those industries use to successfully navigate high churn:
Annual pricing with deep discounts vs. monthly billing: if you know your user will likely only be around for 5 months, you can price at 7 months and have a positive outcome, while still offering a 40% discount to the per month price.
Upfront costs: gyms are famous for initiation fees; it’s the inverse free trial. You need to pay for the privilege of paying a monthly fee.
Bundling with related add-on services: great, you don’t need dating profiles now that you’re in a committed relationship. But you do need gift ideas and maybe even a wedding planner.
Strong user segmentation and retention marketing teams: if you know which groups of users will return, and for what reason, you should be able to generate that resubscribe with ~0 additional marketing cost, maybe just with a simple email, text, or phone call.
The bottom line
Earlier in the post, I mentioned that this churn and return behavior is only OK if it is (a) predictable and (b) users return at no additional cost. That means that streaming services should be relentlessly focused on two things:
Lowering marketing costs to returning users to ~$0 (without negatively impacting the return rate). In theory, as brand awareness grows, and using partner marketing channels, this should improve over time.
Managing fixed costs (programming spend) to ensure they aren’t actually spending tons on new programming to generate return behavior. If that were the case, those costs wouldn’t really be fixed, would they?
It’s not clear that churn will ever be low enough whereby streaming services experience the smiling curve (after all, a user who cancels and resubscribes is even more likely to cancel a second time). But it is clear that resubscription is a key behavior in this industry that Gross Churn doesn’t account for.
When calculating Gross Churn, are you not incorporating all added subs in your Subscriber number in the cancels / Subscribers math?
Therefore, when you do (cancels - Re-subscribers) / Subscribers aren't you double counting the re-subs on both sides of the equation?
I understand the impetus to factor in returning subs into this metric. But regardless of how many of the "new" subs who join in a month are returning subs, the cancellation rate is still the same.
No? Am I reading this incorrectly?
Thanks!
As a researcher who has been involved with many subscription businesses (e.g. Time, AOL, iHeartRadio, Prodigy, CompuServe, DailyCandy, "cable TV," The Movie Channel, USAToday, and many others) over a fairly long career, I start with an "unaided" question, asked of those who recently dropped a service, which is "why did you drop ______?"