The Superwall Podcast · 2026-06-24
Marcus Burke - How to Build a $2M/Month App (Copy Me)
Meta ads consultant Marcus Burke (ex-InnoGames, Blinkist, StudySmarter, Tandem) walks through subscription-app ad creative: priming viewers for the App Store, pain-language text walls built from Reddit + Claude research, AI production via Higgsfield/Weavy, and why app ads differ from web-to-app ads. He then shows his account framework: message + media + content type x funnel = audience, age/placement charts, value rules for older users, and ad-set clustering with per-bucket cost targets.
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Numbers mentioned
| Subject | Metric | Value | Quote |
|---|---|---|---|
| InnoGames | ad spend Meta spend, among top mobile advertisers alongside King and Supercell | millions every month approximate | “we were like up there with like a king a supercell um spending like millions every month” |
| Moongate | ad spend lifetime spend on the green-noise concept across all iterations | USD 1M estimate | “i would say over the course of that it's lifetime and all the iterations it's probably well above like a million dollars or so” |
| Moongate | count total iterations of one ad concept (copy variants, scenarios); ~100 versions of the specific ad shown | 400 iterations estimate | “So of this concept, there was probably in total 400 iterations that we did” |
| Moongate | count versions of the specific green-noise ad shown | 100 versions estimate | “probably 100 versions of this for this client for example” |
| attachment/emotional-regulation app (text-wall ad) | count length of the text-wall ad (three clips, text on screen throughout) | 10 seconds | “we made this app to rewire toxic attachments and support your emotional regulation 10 seconds long” |
| Marcus Burke consulting | count share of client ad production his freelancers now do with AI | 99% approximate | “And we like do 99% of it with AI by now.” |
| Marcus Burke consulting | count iterations made on established ad formats across clients | 400–500 iterations approximate | “had ads that we made 400 500 iterations on as soon as the format is established” |
| benchmarkInstagram Reels feed after deliberately engaging with a target audience's content | count share of feed from that audience after ~5 minutes of engaging | 80% estimate | “if you do this for five minutes you're gonna have like 80 of content from that audience” |
| marketTypical forced annual subscription plan price for consumer apps | price cited as too expensive for 18-24 year olds | USD 70/year approximate | “to spend like 70 bucks a year on like a yearly plan of an app, which is what many apps like force people to do.” |
| aggregateMeta subscription-app advertisers: cost per trial start, age 65+ vs younger buckets | cpa cost per trial start for 65+ roughly double younger users, offset by much better trial->paid; illustrative ('I don't know') | +100% estimate | “100% higher trial start cost on a user 65 plus, but they're also going to convert a lot better.” |
| aggregateSubscription apps in Marcus's experience: older users convert trial->paid better | count share of apps where trial->paid is higher for older age groups | 99% estimate | “if it's the same pattern as for 99% of apps that older people convert better” |
| anonymized client ad account (charts shown) | cpa cost per target event (likely trial) goal for this advertiser | USD 60 estimate | “Let's say, I don't know, for this advertiser, it's like 60 bucks or something.” |
| anonymized client ad account (charts shown) | cpa lowest cost per result among ~30 ads; these ads had significant Facebook feed spend | USD 32–35 approximate | “the lowest cost per result are like $33, $32, $35.” |
| anonymized client ad account (charts shown) | ad spend share of budget on Facebook feed for outlier ads with favorable cost per target (typical top ads ~50/50 Instagram Reels / Facebook Reels) | 40–50% approximate | “They have a very favorable cost per target and they spend 40-50% of their budget on Facebook feed.” |
| aggregateMeta delivery within a single ad set | ad spend share of ad-set budget Meta concentrates on the one ad it picks as best; long tail gets little | 80% estimate | “And they're going to spend 80% of the budget on that.” |
| Marcus's unspecified previous employer (weekly user interviews) | activity volume | 1 user interviewsper UA team member /week | “everyone on the UA team was doing one user interview a week.” |
| Marcus Burke consulting | activity volume | 2–3 LinkedIn posts/week approximate | “primarily on LinkedIn I share content like two or three times a week” |
| Marcus Burke consulting | activity volume | 2 Substack posts/month approximate | “maybe like two pieces a month” |