💎 Growth Gems #41 - Gems from "Revisiting the Fundamentals of App Marketing Post IDFA"
Hi there,
Today's gems are mined ⛏️ from a Sub Club podcast episode where Thomas Petit (Growth Consultant) talks about why most apps should do at least some paid user acquisition, how the UA manager role has evolved and how to think about marketing subscription apps in the post-IDFA area.
💎 #1
Organic is never enough to scale very significantly. Every single one of the huge apps that make it to the top of the charts are at least partly pushed by paid acquisition. Example: TikTok. You need a combination of a lot of paid acquisition, a lot of virality and a great product to stay at the top for long.
💎 #2
You should start working on organics way before your app is live: building an email list, content, community, etc.
💎 #3
You need to fuel your cohorts so you can work on improving retention. You want a minimum of new users every day so you can know if the latest release you ship is better than the previous one. If you don’t have a strong organic foundation, then paid UA is probably required.
💎 #4
Be careful about the biases that your early adopters can bring (if you have a strong organic baseline) because their behavior is not necessarily representative of the mainstream audience. This can make running paid UA healthy: to prove your hypotheses and optimize the product towards the mainstream audience.
💎 #5
You might want to exclude the cohorts for days where you get App Store features (if it represents a big portion of your audience), because the audience is often on the opposite end of early adopters and not representative of what your product should become.
💎 #6
If you have a very small budget (like $500/month), focus on organic or specific paid actions(e.g., promotion on Reddit, very specific keywords on ASA, etc.). Do not go to Facebook or Google, because they are machine-learning driven.
💎 #7
Building your owned and earned audiences is a must from Day 1 and all the way (even after you start paid UA). You need to find the ways that work for you to do that.
💎 #8
The only real lasting thing you get from App Store features is the “Editor’s Choice” badge that you can keep permanently on your listing.
(Jacob, Revenue Cat)
💎 #9
The core of the UA manager job today is producing, testing and deploying creatives. The role is also more strategic, as you have to think more like a data analyst so you can feed the right events to the machine learning algorithms.
💎 #10
Marketers look a lot at the impression to install ratio, but Facebook is now taking engagement more into account (“like” vs. “comment” or “shares”, view rates, etc.) You need to trigger these positive signals through your creatives and keep people engaged on the Facebook platform for the algorithm to favor you in the auction.
💎 #11
Be careful about using “free” in your communications: start a free trial, etc. because you might attract users that just want free stuff. It’s probably best to have less users coming in but more qualified.
💎 #12
Facebook’s algorithm wasn’t looking only at the data from your app, it was also looking at the data from pretty much all the apps in your category. With ATT, the duopoly is now attacked at the heart of their differential advantage.
💎 #13
With the new attribution system it’s going to be even trickier to optimize for the free trial event. Smart subscription advertisers often use a more complex event than the free trial, by layering it with some other things that are specific to their app. Example: free trials for users that have not cancelled within the first 2 hours + engagement.
💎 #14
Subscription apps with no free trials that don’t have the budgets to optimize for purchases have to find earlier signals to create a “bundle of events”: onboarding answers, accepting notification requests, etc.
💎 #15
Customizing too much the combination of events you’re optimizing for on Facebook can also prevent its algorithm from optimizing correctly, because the comparison with the data that it has from all the other apps becomes less relevant.
💎 #16
There is no single source of truth anymore. What the ad networks do to optimize and the way advertisers interpret it is going to be more complex. Example: Google might use a blend of signals (SKAdNetwork, the IDFA when available, Android app, first-party data around contextual placements and other apps, etc.)and advertisers are going to need to use different data sets to interpret what’s happening (SKAdNetwork – not the full picture, IDFA, media-mix model or probabilistic model, known behaviors from specific-channels – like how fast users go through onboarding).
💎 #17
Try to better understand the interdependencies between channels (e.g., email list, Instagram, offline, etc.) so you can assess how much you should be putting back into the paid acquisition machine.
Stay savvy!
⛏️ Sylvain