Founder Playbook · Sub Club by RevenueCat
12 tactics from Marcus Burke
Scaling Your Subscription App with Meta Ads – Marcus Burke, Independent Consultant
Watch the full episode“the age group below 25 is really neglected by most of the advertisers because they are usually very cheap to buy because they use social media more often and they don't have high purchasing power so cpms are low meta really loves spending on them because they're so cheap and they're going to create a cheap cost per trial but then the conversion down funnel is going to be very very poor”
Exclude users under 25 by default — cheap CPMs mask terrible trial-to-paid conversion
Meta's algorithm gravitates toward under-25s because they are cheap to reach, which looks great on cost-per-trial but collapses on the metrics that matter: trial-to-paid conversion and renewal rates. Burke's default is to exclude under-25s (sometimes under-30s) until enough data exists to model their actual LTV. The auction premium for older audiences pays for itself.
“if you just run one campaign with one adset in it all that data will be used to basically fine tune targeting for you so you always want to find the right balance of where do I need granularity to guide the algorithm”
Start with a maximally consolidated Meta account — one campaign, one ad set, all signal in one place
Meta's algorithm needs enough signal data to optimize effectively — scattering budget across 10 campaigns and 50 ad sets starves each one. Burke's default for new accounts is maximum consolidation: one campaign, one broad ad set, all signal funneled to the same place. Add campaign branches only as spend scales and after confirming what you're splitting on.
“postbacks are coming in delayed and you need to make sure that you're basically evaluating campaigns that way and always match data from two days back with the right spend level additionally I would say also give meta a little bit more time because they don't get these events super quickly so you shouldn't be evaluating results after three four days”
Never evaluate a new Meta campaign before 7 days — SKAdNetwork postback delay makes early data meaningless
SKAdNetwork postbacks arrive 2-3 days late, meaning a campaign launched Monday will not show trial events until Thursday. Pausing or killing a campaign at day 3 because it shows zero results is almost always a mistake — the data simply has not arrived yet. Burke's rule: wait at least 7 days and always compare spend levels to the correct 2-day-delayed event window.
“sub apps just were in a good position for having that trial event and onboarding... their payment usually happens in onboarding already so you start a trial event there meaning you have a pretty good value indicator early on in the product experience that you can feedback to that model”
Subscription apps have a SKAN advantage over games — the trial event fires in the first few days
Games could not track their highest-value payers under SKAdNetwork because big in-app purchases happen days or weeks after install — too late for postbacks. Subscription apps trigger a trial event right in onboarding, so Meta gets a signal within the postback window. This structural advantage over gaming is why sub apps recovered faster post-ATT.
“if your pricing is very high let's say 80 90 bucks a year you're going to have a hard time converting these people because they're just not as high intent and don't have as much purchasing power so your pricing what your pay wall looks like in the onboarding needs to be really synced up with the ads you're running”
Align your paywall price with the Meta placement — a $90/yr price on Reels traffic will destroy conversion
UGC short-form Reels ads attract younger, lower-purchasing-power users — pairing that placement with an $80-90/year paywall is a recipe for wasted spend. Burke's framework: understand who each placement delivers (Reels is close to TikTok audience, Facebook feed is older and higher intent), then make sure the paywall price matches their willingness to pay.
“always test your creatives in a separate campaign not in the one that you're scaling and that is running smoothly because it will interrupt them and things can go wrong there's this infamous learning phase so whenever you make a change to your running campaign it resets it meaning the campaign recalibrates”
Test creatives in a separate campaign — never let experiments reset your scaling campaign's learning phase
Uploading a new creative directly into a live scaling campaign triggers Meta's learning phase reset, causing performance fluctuations that can last days. Burke runs a dedicated test campaign in the cheapest markets where he is already live, so results are transferable to the scale campaign without disrupting it. Winners then graduate with confidence.
“one of them is incrementality so you want to switch ads on switch ads off and see what's happening to your baseline which is always of course a good position for smaller developers if you don't have a ton of organic yet”
Use incrementality testing — switch ads on and off to isolate channel impact without a data science team
Before building data science infrastructure, Burke validates channel ROI using a simple incrementality check: pause spend for a period and observe baseline installs and trials. If they drop materially, the channel was contributing. This works especially well for early-stage apps with limited organic that can cleanly isolate the ad effect.
“that's definitely one of the changes I would say I've seen that UA managers need to be more and more product focused as well and they need to look into full funnel performance not just purely on the ad side because much of that efficiency from the algorithm is gone and you need to find additional levers to improve performance”
Post-ATT, UA managers must become full-funnel product thinkers — bid optimization alone is not enough
Pre-ATT, UA was largely a data and bidding game — Meta's algorithm did the heavy lifting. Post-ATT, that efficiency eroded and the remaining lever is funnel quality: onboarding conversion, paywall clarity, trial-to-paid rates. The best UA practitioners now instrument product experiments the same way a growth PM would.
“if meta is one of your main channels then that already creates coherence people click through they see I've landed in the right place and this is the app that I was looking at that often already creates an uplift”
Bring Meta creative winners into your App Store listing — coherence from ad to store lifts CVR
When a Meta ad creative proves itself, the same visual language can move straight into App Store screenshots and preview videos. Users who click through and see the same framing feel immediate recognition — this is the app I was looking at — which reduces bounce before the paywall. Meta becomes both an acquisition engine and a continuous A/B testing lab for store messaging.
“I like to build my account foundation on scan because I know it's future proof it's what's Apple wants us to do and it's also getting better with each version... I use a for scaling if I see performance is good with it then why not use that opportunity I wouldn't just not do it because it's unsecure I rather take my chance there but it shouldn't be the foundation”
Build your Meta account on SKAdNetwork, not AEM fingerprinting — SKAN is Apple-proof
AEM (Meta's aggregate event measurement, effectively probabilistic fingerprinting) can deliver strong results right now — but Apple can remove it just as they removed the IDFA. Burke uses AEM opportunistically for scaling but keeps SKAdNetwork as the foundational measurement layer. Any strategy built entirely on AEM recreates the same fragility that crashed performance post-ATT.
“most advertisers are asking for age and gender of users which is super helpful because meta always reports you their data on age and gender level and if you then in your product also apply that level again then you can basically cross check and look into I don't know I'm seeing in my app that age group 45 to 54 is 5X more valuable”
Ask age and gender in onboarding to build a channel-quality model without user-level tracking
Without user-level attribution, the only way to understand channel quality is through demographic cross-referencing. By collecting age and gender in onboarding, Burke can compare in-product LTV by cohort against the same breakdown from Meta's ad reporting. If 45-54s are 5x more valuable in product but Meta charges only a 2x premium to reach them, that is a clear bidding signal.
“you're going to see differences in someone that answered I came from TikTok compared to someone that answers I came from meta for sure and same for age groups which is then the data that you can use to personalize and to inform your data modeling”
A 'where did you hear about us' survey is a channel-quality signal, not precise attribution
A 'where did you hear about us' question in onboarding does not give precise channel attribution — users often cite the first touchpoint, not the converting one. But it creates a directional quality sample: comparing LTV for people who say Meta vs. TikTok vs. organic gives a relative channel-quality index that informs bidding targets without a full data science stack.