Distribution Playbooks
Getting a product in front of the right people — the channels founders bet on, the partnerships that scaled, and the underrated distribution plays most people skip. Each one is quoted from the founder who ran it.
266 tactics · page 8 of 9
“Everyone comes to web2app thinking they'll save 30% on fees or get better attribution. Those are rarely the reasons it actually works. The real value — different audiences, owned data, new retention levers — only becomes visible after you've run it for six months.”
Developers go web2app for the wrong reasons — and discover the real value on the way
Petit has observed a consistent pattern: apps adopt web2app expecting fee savings or attribution improvements, only to find those benefits are modest or offset by lower web conversion rates. The durable advantages — reaching audiences unreachable via App Store ads, owning the payment relationship, unlocking win-back flows — only become legible through experience. The implication: commit to web2app as a long-term channel, not a quick financial optimisation.
“When you run web campaigns and app install campaigns on Meta at the same time, you're not reaching the same pool of users twice. Meta's algorithm finds different user profiles for web objectives versus app install objectives. It's genuinely additive inventory.”
Running web and app install campaigns on Meta simultaneously reaches different people, not just more of the same
Petit's most counter-intuitive finding: web and app campaigns on Meta do not cannibalise each other. Meta optimises each campaign type differently — web conversion campaigns find users who respond to landing page CTAs, while app install campaigns find users comfortable tapping directly to the store. Running both simultaneously expands total reachable audience rather than just splitting budget against the same people. This is why ROAS on web often holds even after scaling app campaigns.
“Try sending Taboola traffic straight to the App Store — you'll see a 90% drop-off before anyone taps install. These channels require a landing page. Not because web2app is better, but because the user expects to land on a page, not get redirected to a store.”
Taboola, Outbrain, and Reddit can't work sending users straight to the App Store — web landing page is mandatory
Petit tested traffic from native advertising networks (Taboola, Outbrain) and Reddit ads going directly to App Store product pages — conversion collapsed. Users from these placements have content-browsing intent, not shopping intent; the App Store redirect breaks their expectations. A matching landing page serves as the expectation bridge, and only once intent is established does the app install conversion hold.
“People say 'web attribution is broken.' It's better than iOS. Yes it's imperfect — but you can see in hours what took weeks on iOS. For creative testing, for audience testing, for algorithm optimisation — the speed of web feedback has no equivalent on the App Store side.”
Web attribution isn't perfect either — but it enables faster feedback loops than iOS ever could
Petit rebuts the attribution-quality objection to web2app: iOS post-ATT attribution is not a high bar. Web attribution (server-side events, first-party cookies, probabilistic matching) is imperfect but delivers purchase signals in hours versus the 14-day SKAdNetwork windows on iOS. For teams running continuous creative and audience experiments, this speed difference is compounding — more learning cycles per unit time means faster improvement.
“The question is never 'should I do web2app or app install.' It's always both. Your audience is not a monolith. Some users will convert better through the App Store. Some through web. The job is to have both paths and let each user take the one that fits.”
Web2app is never 100% one or the other — always both, because audiences behave differently at different moments
Petit's closing framework: web2app is an additive channel, not a replacement. The same user who ignores a Facebook app install ad might convert on a web landing page after seeing a TikTok; or they might install directly from an App Store search a week later. Constraining to one path means abandoning the other path's addressable audience. Portfolio thinking — maintaining both channels and optimising each — consistently outperforms either/or strategies.
“As part of your Strava membership you get 20% off weather up and you can bundle the pricing. If somebody unsubscribes from Strava they're no longer getting that contingent pricing. Apple's baking this in at the App Store level.”
Contingent offers let apps bundle pricing with other apps at the App Store level — a native co-marketing model is now possible
Contingent pricing enables a formalized cross-app discount economy: App A's subscribers get a discount on App B, enforced automatically by the App Store so no backend integration is needed. When the triggering subscription lapses, the discount disappears on next renewal. This creates incentive structures for co-marketing partnerships and bundles previously impossible without custom server logic, opening a new distribution and monetization lever especially useful for complementary-niche apps.
“Your first few marketing hires generally need to be generalists because you can't afford to have a channel expert. And the saying about jack of all trades master of none — we haven't had great success with marketing generalists. What we found to be super successful is just contracting with channel experts. They can do it in half the time an internal person can and your cost is lower.”
Channel-expert contractors outperform in-house marketing generalists — channel depth beats generalist breadth for small apps
Rather than building an in-house marketing team, Reading.com assembled a roster of specialist contractors: one for Meta ads, one for Google, one for Apple Search Ads, one for lifecycle email. Each files a weekly report and requires minimal management. The model gives access to expertise that would be unaffordable full-time, and contractors bring cross-client pattern recognition that any single-app team cannot develop internally.
“I think at first it will be a really great LTV boost for the users who are already subscribed. But based on some of the research I've been doing it will also work the other way — folks looking for books online could become a lead gen for the subscription app as well.”
Physical books on Amazon give a two-way flywheel: subscribers get an LTV boost and Amazon buyers become app leads
User demand for physical versions of the in-app storybooks prompted Reading.com to expand into Amazon publishing. The strategy creates a bidirectional acquisition loop: current subscribers buy physical books for off-screen reinforcement (extending subscription value and LTV), while Amazon shoppers discovering the books become a new top-of-funnel for the subscription app. Physical products also create lasting brand presence in the home that digital subscriptions cannot.
“The initial exploration for deals like that usually start a year before, at least. It starts with high-level concepting. I carry a lot of the outward BD — I attend conferences, I speak from stage. The mobile space is a relatively small industry so we also know each other.”
Gaming cross-brand collabs start a year before launch — concepting is CEO-to-CEO, not agency-to-agency
The Brawl Stars x Subway Surfers collaboration was the result of a year of exploration, starting with Nørvig personally pitching high-level concepts at industry conferences. The gaming industry runs on CEO-to-CEO relationships at conferences — creative agencies and middlemen are not in the loop at the concepting stage. This also suggests that for app developers, conference attendance is not networking overhead — it is product development.
“Strava gives you a little badge you can put on your social media whenever you do something well. So we make a lot of our content shareable. A friend started sleeping better and posting Welltory's magical fuel tanks on social media to brag to people — that helps people come back to your app for more content to share.”
Bragging rights are an underrated retention driver — make your best moments shareable
Paloni highlights that users sharing app content to social media creates a secondary retention loop: the desire to have more shareable moments brings them back. Welltory makes its animated health visualizations shareable as a deliberate product choice. Strava's route maps and Duolingo's achievement badges serve the same function. The mechanism is self-reinforcing: a user brags, their network sees the brag and downloads the app, and the original user returns to generate more shareable content. This is one of the few organic distribution channels that costs nothing to maintain.
“A common thread I see at least is that apps that break out — like a Duolingo, even a Spotify — are inherently mobile-first products that for a large part of their life had very big free user bases. If you're doing a hard paywall, accept that you are probably capping this at $10-20-30M of Revenue.”
Apps that break through the ceiling almost always have a massive free user base driving organic distribution
Falzon observes that the breakout exceptions to the $10-30M ceiling built massive free communities before monetizing seriously. The free base became their distribution, their brand, and their referral engine. Hard-paywall apps that convert everyone immediately rarely build that community and therefore cap out on organic growth. For founders who want to build past the ceiling, a substantial free experience is not just generosity — it is a prerequisite for the network effect that breaks the ceiling.
“Totally organically — a high school in Los Angeles, Harvard-Westlake, reached out because students got back to them and said many of us are using this app called Opal on our own — no one's telling us to do that — but maybe you should reach out to them.”
Organic school partnerships appeared without sales effort — students emailed their principals about Opal
Schlenker describes how Opal's school business emerged: students using Opal personally recommended it to their schools when districts asked for phone policy solutions. The freemium strategy that brought students in as users was the prerequisite for this B2B2C distribution channel. The pattern mirrors Apple's college library strategy: get the next generation using your product voluntarily, and institutional relationships follow. Opal now charges schools per-student but prices competitively because the brand and word-of-mouth value far exceeds the per-student revenue.
“When you have a great brand you can go from one segment to another to another and build a billion user product. We went from professionals to students to schools — and we see employers, insurers, the family market as logical next steps. That's why it's so important to build the most loved and most important brand in that category.”
A brand can travel across segments — build the most-loved name in your category and new markets open naturally
Schlenker articulates Opal's long-range brand strategy: the same brand that resonated with productivity-focused professionals was elastic enough to capture students, and students' love of the app created a school sales channel organically. He sees expansion into employers, health insurers, and family products as natural brand extensions — each requiring only distribution into a new context, not a new product identity. The enabling condition is that the brand carries emotional weight and mission alignment across segments.
“do not go there with 20 a day it's money love like you're not going to learn anything but you can't really make the machine work at all not going to pass threshold there's a bunch of like criteria and that minimum bar of the amount you can put to actually start seeing something.”
Spending $20/day on algorithmic ad platforms is money wasted — the machine can't learn
Modern ad platforms (Meta, TikTok, Google UAC) require enough conversion events to feed their optimization algorithms. At $20/day you'll almost never generate the 10–20 daily events per campaign needed to exit the learning phase. The result: premium CPMs, garbage traffic quality, and nothing learned. Petit's floor is $10–20K/month before algorithmic channels make any sense.
“one advice maybe if you've got 5 or 10K to test probably would be to avoid algorithmic platform so search on this I mean the nature of the inventory is very different but influencer is also one where you might want to try because you can find influencer of the size that you are.”
Early stage, limited budget? Start with search ads and influencers, not Meta
Apple Search Ads and micro-influencers are the two channels that can work at small budgets because they don't rely on algorithmic optimization for conversions. Search ads win on intent; influencers win on trust and can be scoped to any budget. Petit recommends these as the entry point before a founder has the cash or data volume to make algorithmic platforms viable.
“more than what my Blended cost but paying subscriber up or whatever is what is the trend of this brand.”
Track blended subscriber acquisition cost trend — not just the paid ROAS dashboard
Paid UA creates secondary lifts in reviews, ASO rankings, and word-of-mouth that don't show up in the MMP. Petit advocates measuring blended subscriber acquisition cost — total marketing spend divided by all new subscribers regardless of source — as the North Star. This keeps teams honest about whether paid is genuinely additive or just cannibalizing organic numbers.
“I'd say for me like the area that I find really hard to nail is the 0 to 50K because 50k months is where you can start having two three channels running in parallel or even I can split the budget between campaigns and start making a bit more of experiments.”
The $0–50K/month budget zone is the hardest in paid UA — most agencies won't touch you until you're through it
Below $50K/month, you can barely run a single channel properly with almost no room for parallel experiments. At $50K you can split across two or three channels, test campaign variants, and start generating enough signal to optimize. This is also the threshold where serious freelancers and agencies start taking you on — making the 0–50K zone both the hardest technically and the loneliest operationally.
“what RUA did is they enabled people to go from being on the couch to running a 5K to running a 10K and now they're an athlete so for Strava that's a massive TAM expansion number one right so now all of a sudden you can actually be for people that are not athletes but want to be an athlete.”
Runna/Strava is the blueprint: be the TAM expansion a giant cannot build internally
Crowley advised the Runna sale to Strava. The acquisition thesis was TAM expansion plus a second pricing tier: Strava had strong retention but weak tiered pricing, while Runna onboarded people who weren't yet athletes. Rather than building this internally — which Strava couldn't do without cannibalizing their identity — they acquired it. The lesson: build for the consumer a giant's core user base doesn't yet include.
“as just you get more and more content being published it gets harder to stand out I mean that's just like a pretty fundamental premise I mean I don't think anybody would disagree with that right.”
Distribution has always been the bottleneck — easier building makes it harder to stand out, not easier
Seufert's core argument: every historical wave of easier publishing (blogging platforms, Unity for games, the App Store itself) has made discovery harder proportionally. AI-assisted vibe coding is inflationary for the app ecosystem — more supply competing for the same user attention, meaning distribution becomes even more valuable, not less. The winners are the ones who solve distribution, regardless of how the code gets written.
“everything's an ad network on steroids right so now you've got everything running ads well okay well who's buying these ads all that companies that didn't exist before but can via the sort of miracle of AI you know launch a product and they've got to get distribution they've got to get eyeballs on their stuff.”
Race-to-zero software creates an ad economy on steroids — distribution winners capture everything
Seufert maps the macro economics of an AI-abundant software world: if building costs collapse, pricing follows, pushing more apps to ad monetization. But new AI-born apps also need distribution, so they become ad buyers themselves — creating demand that keeps CPMs from collapsing. The net result is a turbocharged ad economy where apps with strong owned distribution (brand, community, ASO) are insulated while new entrants fund everyone else's growth.
“when this comes to fruition it will at some point is like yeah maybe the category winners just run away with everything because I'm not going to say hey Siri open up one of my ride sharing apps and book me a ride to the movies I'm going to say open up Uber.”
AI agents favor category leaders — users say the brand name, not a generic query
Seufert's counterintuitive take on AI agents: if voice or agent-driven interfaces become common, they'll dramatically favor category leaders because users specify brand names, not categories. 'Book me an Uber' routes to Uber; a generic query routes to whoever wins the platform's default. Dominant apps may actually benefit more than they suffer from agent interfaces — the risk falls disproportionately on second-tier competitors without strong brand recall.
“everything that I have professionally one way or another I can trace back to Twitter and friends I've made on Twitter building in public turns out is great for getting a job CEOs were like hey man I'd love to have you come work here you've got it though if you want to go out on your own you've got it and I would be your first client”
Building in public is the highest-leverage distribution any solo founder has
Within a week of the layoff tweet, multiple CEOs offered Aaron a job — and several said they'd be his first paying client if he went solo. Years of public writing and shipping had served as a portfolio that companies could evaluate without an interview process.
“focusing basically says maybe we build for trucking companies for one to two years and then we expand later into construction the features the content the marketing the channels all the goto Market strategy it's all going to focus on either that one vertical”
Focus on one vertical with a planned expansion sequence, not a permanent niche
'Niching' locks the brand to one vertical forever; 'focusing' picks a starting vertical with a documented expansion path. ConvertKit started with food bloggers, then expanded every 1-2 years to adjacent audiences. Pick a launch vertical that's easy to enter and write down the next two — the second market isn't a pivot, it's the plan.
“trying to run experiments too fast and trying to do too many too many fast if you're just a Founder trying to be on 10 different marketing channels at once is a recipe for best case scenario just burning out you want to like one an experiment and you want to make sure it's running for at least 3 months if you have no signal after three months then Burn It To The Ground”
Run one marketing experiment at a time and give it three months
Founders default to spraying across ten channels and learn nothing. Pick one channel based on where customer interviews say the audience hangs out, run it for three full months, and if no signal appears by then, kill it cleanly and move on to the next.
“I love what you've done on the homepage too where you show just how recently you updated the product here is the feed of like the GitHub commits that just integrates into to your website”
Show the GitHub commit feed on the landing page
For a $200 one-time purchase, buyers need positive recency signals that the product won't be abandoned. Embed a live GitHub commits feed on the landing page — it turns 'is this still maintained?' anxiety into a positive trigger. Free, automatic, and directly addresses the #1 objection to lifetime-license tools.
“I had like the best customer support call the other day because their marketing team was not upset that their knowledge base wasn't getting indexed they were upset that it was outperforming their dub dub dub like people were getting to the documentation”
Public docs outranking your marketing site is a feature, not a bug
A public knowledge base ranks for long-tail 'does tool X do Y' queries that marketing sites rarely answer in depth. Prospects researching a purchase land in the docs, evaluate the product, and convert before ever requesting a demo. One customer's docs outranked their own marketing site — exactly because docs win the specific commercial-intent queries copy can't.
“the book is much more in the vein of like an O'Reilly book where it kind of it explains these topics in a comprehensive way whereas the Udi course is much more of like a quick hit because that's what like what udy people want the udem course blew up right so it was like way more successful than our our our Tech business”
Repackage one body of IP into multiple formats — Udemy out-earned the SaaS
One body of knowledge — prompting principles — was repackaged into a blog post, an O'Reilly book, and a Udemy course. The Udemy course ended up out-earning the underlying SaaS. Audience expansion comes from format, not new content. Different formats serve different buyer intents: cerebral readers buy the book, quick-hit learners buy the video course.
“if you give them one like gold nugget they're like I want more of this stuff the design check-in that's one fix but then you can do a full day and then it goes bigger and bigger until it's 40 hours a week or a productized service”
Lead with a small, productized 'gold-nugget' offer
Nick's 'Design Check-In' — a 30-45 minute fix for one stuck point — is both a lead magnet and a standalone product. Tangible single wins convert skeptical prospects into recurring buyers. Design a single sub-$200 fixed-scope deliverable that solves one named pain in under an hour, then use it as the qualification step for larger engagements.
“I had very early on a a Hacker News Post that was quite successful and also a Reddit post that was quite successful and that kind of kicked things off and then like I would have been stupid to to to uh stop doing it somehow because there was there was a constant interest in it”
A single HN + Reddit hit can carry you through the dip
Early traction came from one successful Hacker News post and one Reddit post. That distribution event created enough momentum that quitting would have been irrational — the constant inbound interest carried Neural Frames through the dead-end phase before the V2 rebuild. For visual/AI tools, a strong HN + Reddit launch is the proof signal that justifies continuing through the dip.
“Stop building it and telling them about it like have them come in the kitchen have them come to your house have them come in your WhatsApp group have them come in your live chat I literally did that to a thousand people over months that was the secret”
Build WITH the public — pull them into the kitchen
Building 'in public' (broadcasting updates) is the watered-down version. Noah's reframe: build WITH the public — pull readers into WhatsApp groups, Discords, live chats, DMs. His book launch team came from manually DMing ~1,000 people one by one over months. Explicit ask, individual messages, no automation.