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 5 of 9
“it was a good time to test marketing and we kind of fastened that at that point because there was the covered the beginning of the covey then all marketing was going down so it was super cheap to try stuff there yeah so try to be opportunistic on that an influencer had like a lot of time”
COVID Made Ads Cheap and Influencers Available — Run Tests When the Market Gives You a Window
PhotoRoom timed its first paid advertising push to coincide with COVID lockdowns — when CPMs crashed and influencers had idle time. The lesson isn't to wait for a crisis, but to recognise market windows: abnormally cheap ad inventory or uniquely accessible talent are finite opportunities. Being ready to run when conditions improve is a strategic asset.
“i would be just very focused on like what i think my most viable channel is probably facebook right i would be driving as much traffic as i can to the web and trying to wrap my arms around that as much as i can right because everything you send to the app store now is going to be mostly like muddied”
Small Apps Post-ATT: One Channel to Web, Capture Email and UTMs, Then Push to App
Eric Seufert's advice for solo or small-team app developers post-ATT: master one channel driving traffic to a web landing page before touching anything else. The web retains UTMs, captures email addresses, allows rich onboarding personalisation, and is fully measurable — everything that is opaque when sending users directly to the App Store. Only expand to more channels once the web-to-app funnel is proven.
“the dirty secret is a lot of these creative shops had templates and they gave you the same ads they were giving to your competitor right there's just no way now once you've got to build this stuff yourself and it's got to really be informed by deep knowledge of your product”
Creative Agencies Used Templates Shared Across Competitors — In-House Is Now a Necessity
Eric Seufert exposes the pre-ATT creative agency model: most shops ran lightly-customised template ads for competing apps simultaneously. That drove CPM inflation as competitors bid against each other using the same creative concepts. Post-ATT, with spray-and-pray no longer viable and conceptual distinctness now the key differentiator, in-housing creative is not optional — it is the only way to build real competitive advantage in paid UA.
“I think fewer people are searching for apps and so being in a featured list in the App Store is not the thing it used to be… a lot of my downloads are coming from the word-of-mouth version of organic rather than someone coming to the App Store with a need.”
App Store Browsing Is Dead — Word of Mouth Is the New Organic
The era of users browsing the App Store for discovery is over — most users come in with specific intent or via a recommendation. For David, App Store featuring, while still valuable, no longer drives the volume spikes it once did. Durable organic growth now requires building features that generate screenshots, shares, and genuine conversation.
“You can have product market fit but kind of not fully understand it. Building that initial growth flywheel is a pretty different skill set than the operational side. You have to figure out who and why and then what channels.”
Start Building the Growth Flywheel Before You Operationalize — They Require Different Skills
Sean Ellis draws a sharp line between building the first growth flywheel (who needs this, why, which channel works, what business model fits that channel) and operationalizing growth (dashboards, team accountability, scalable systems). Most founders skip straight to ops. The flywheel phase requires deep qualitative insight and channel hypothesis testing; only after one channel proves out do dashboards have anything to manage.
“We're driving traffic to that one article because it would have a higher impact. They'll read this article and it's either going to highly resonate with them or not — and if it does they're going to dig deeper and check out the community and then go to the app with super high intent.”
Send Paid Traffic to the Blog Post, Not the App Store — High-Intent Converts Better
None to Run's paid Google spend goes to the cornerstone blog post, not directly to the App Store listing. The logic: a reader who finishes the article and then downloads the app is pre-sold. That intent difference is why they have a 20% trial start rate and 80% trial conversion — funnel quality at the top creates outsized results all the way down.
“You don't need thousands or tens of thousands of downloads a day to build a real business. If you are hitting the right audience with the right intent and have the rest of the funnel really dialed in — that's where you end up with a 20 percent trial start rate and 80 percent trial conversion.”
Hundreds of High-Intent Downloads Beat Thousands of Low-Intent Ones
None to Run generates only hundreds of downloads per day — a number many developers would dismiss as insufficient — yet has built a growing 9,000-subscriber business. The insight: trial start rate and trial conversion rate matter more than raw download volume. Community-primed users who already trust the brand arrive with intent that no paid acquisition campaign can replicate at scale, making funnel efficiency a strategic substitute for volume.
“A lot of these videos promoting apps they don't mention link in bio — that was a really popular thing from Instagram era, but if you're watching a random video in the for you page it needs to feel as authentic as possible because you haven't built that audience trust over time.”
"Link in bio" kills TikTok FYP ads — cold audiences need authentic, not promotional
The 'link in bio' call-to-action is a carry-over from Instagram where influencers have established trust with their audience. On TikTok's For You Page, viewers encounter your content cold, with zero prior relationship. Explicit promotional language — 'link in bio', 'use my code' — signals ad to a hyper-aware audience and tanks completion and tap-through rates.
“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.
“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.
“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.
“I don't care about the ads manager — show me the events manager. The first thing I'm going to check is: does the amount of conversions the platform reports match more or less what we have internally? Very often it's not the case.”
Check the events manager first — not the ad account
Thomas Petit's first diagnostic step on any ad account is not the campaign performance view but the events manager — where he checks whether conversion volumes sent to the platform roughly match internal data. A 5% gap is fine; 30–50% means nothing downstream can be trusted. Broken signal is the single most blocking problem and must be fixed before any creative or bidding optimisation.
“If it's broken you really need to fix it. Find a place where you're comfortable and dedicate attention on creative. But at some point, as you grow, becoming a little bit more sophisticated about what you're sending is probably going to unlock value.”
Signal engineering has three states: broken, default, sophisticated
Thomas Petit frames signal engineering as a progression. State one: data is broken (common at all scales) — fix before anything else. State two: data passes but uses default, unoptimised mappings — acceptable while growing. State three: engineered signals that represent real business value drive the platform's ML. Most apps spend years in state two without realising state three is available.
“If you're early in this process you probably want them all to receive 100% of events. It's almost normal that the same signal is not going to be the best for every platform.”
Send 100% of events to every network first — then filter
Before any filtering or qualification, Thomas Petit sends 100% of all event types (installs, free trials, paid conversions, renewals) to every ad network simultaneously. This baseline lets him verify data quality and gives the platforms their broadest learning signal. Only after confirming the numbers roughly match internal data does he start creating filtered or qualified variants for each network.
“The real revenue the one that passed by default on the SDK is not the one that I want to be sending because the free trial conversion is going to come too late — it's going to overvalue the yearly over the monthly.”
Engineer predicted LTV into your revenue signal — defaults lie to the platform
Ad platforms receive the literal transaction amount by default: a yearly subscription fires a large number on day one, a monthly fires a small one, and a weekly might appear tiny even if it yields high LTV through renewals. Thomas Petit replaces those real-time figures with a predicted value at a fixed horizon (he favours month 13) so the platform's ML targets the users who are actually most profitable — not just the ones who generate the biggest single transaction.
“In my opinion whatever comes after 24 hours is useless for the platform to optimise towards. You need to find something that happens on the first day.”
Events fired after 24 hours are nearly useless for platform optimisation
Ad platform ML models learn primarily from signals fired within the first 24 hours of a user's journey. Delayed events — even from free-trial conversions on a 7-day trial — arrive too late for the model to meaningfully adjust. Thomas Petit's rule: identify a first-day proxy for high intent (strong onboarding completion, early feature activation) and use that as the primary optimisation event rather than waiting for the actual conversion.
“Below 10 events per day per campaign things are going to go a little bit wrong. If you're too early you can't do qualified trials because you already only have five trials per day — don't try to filter them.”
Volume vs. quality: you need ~10 events/day per campaign before filtering
Signal engineering's quality improvements are useless if volume is too thin for the platform to learn. Thomas Petit's rule of thumb: below roughly 10 events per day per campaign, focus on increasing volume rather than filtering. At that stage, it's better to send a broader event (including pre-trial intent signals) to give the platform enough data to work with, then layer in qualification once scale is reached.
“Revisit your assumption every now and then — every 6 months or whatever — or at least control that gap between what we're sending and what is actually happening is not too big.”
Revisit signal engineering assumptions every 6 months — they drift as your business changes
Signal engineering is not a set-and-forget system. Pricing changes, monetisation experiments, new channels, and macroeconomic shifts all alter which users are actually valuable — meaning the engineered values sent to ad platforms gradually diverge from reality. Thomas Petit monitors the gap between predicted and actual value; when it widens beyond acceptable tolerances (especially at the country level), he updates the model rather than letting the platform optimise against stale assumptions.
“We don't understand why winning ads win — and our human criteria is not very good, but actually the LLMs they're not very good either. It's more like: mass-produce and see what sticks.”
AI for creative analysis is still primitive — we don't know why winning ads win
Despite excitement around AI creative tools, Thomas Petit argues that analysis of why ads work remains the weakest link. Feeding 200 losing and 3 winning creatives to an LLM and asking for patterns yields some output, but those patterns rarely reliably predict the next winner. The honest conclusion is that creative testing is still largely a volume game — produce many variants quickly, identify winners empirically, double down, and keep the exploration pipeline full.
“We made it really simple to post a link to your Rapchat to your Twitter and your Facebook — two ugly square buttons — and that worked. We saw a 10x return on that, and to date that type of flow has driven millions and millions of downloads.”
Adding two share buttons drove a 10x return — viral loops beat paid acquisition
With no paid marketing budget in the early years, Seth Miller's team obsessively refined the share-and-return loop. Adding basic social sharing (originally just Twitter and Facebook buttons) produced a 10x multiplier on installs. Later iterations auto-generated shareable video of each track — which became the app's most-used feature because native video performs better in social-feed algorithms. The compounding effect of tiny loop improvements, not any single big bet, fuelled 80% organic growth.
“What really got me across the line was their product channel fit and i feel like that's often overlooked... it's not just like is this a product people are willing to pay money for but just straight up how are you going to get this out to market.”
Evaluate Product-Channel Fit Before Joining — Organic Traffic Is The Moat, Not Just The Product
Ron's reason for joining AllTrails in 2015 was a Google Analytics screen showing millions of free organic trail-search visitors — before the company even had polished apps. He calls this 'product channel fit': a separate question from PMF that asks whether sustainable, scalable distribution already exists. Without it, even great products stall. This lens applies to any acquisition decision or founding choice.
“Tracking is broken even if performance is not right... if you look at the itunes dashboards you're like you think all right you know you didn't just go crashing down which is what i was afraid would happen right right that has not happened.”
Tracking Is Broken — But Performance May Not Be. Check Blended Numbers Before Panicking
After ATT launched, reported CPAs from Facebook skyrocketed because the privacy threshold hid attribution — but many apps' iTunes dashboards showed stable or growing trial numbers. The lesson: platform-reported metrics became unreliable directional signals, not ground truth. Before cutting iOS spend, check blended acquisition cost (total spend / total trials) against the App Store revenue dashboard to see if real performance actually degraded.
“A subscription app is less impacted by that uh just because uh you know again your cost per trial which is the primary metric nearly every subscription app optimizes for is generally under fifty dollars which means for the same five hundred dollar budget you're getting uh you get uh you're getting ten purchases so you're less susceptible to that privacy threshold.”
Subscription Apps Have A Structural ATT Advantage Over Games — Cost Per Trial Clears The Privacy Threshold
Apple's SKAdNetwork privacy threshold hides purchase events when campaign volume is too low — a killer for high-CPA gaming apps spending $150+ per purchaser ($500/day = 3 events, suppressed). Subscription apps with $50 or lower cost-per-trial generate 10+ events per $500 campaign, consistently above threshold. This structural difference means subscription app advertisers get more signal and less blind spending than gaming advertisers under ATT.
“The webpage can do a much much better job of selling than the app store can uh while we're still making it clear that this is an app uh and while the actual conversion happens within the app itself.”
Web Landing Pages Sell The Product Better Than The App Store Ever Could
The App Store forces every app into the same template: icon, title, screenshots, short description. A web landing page lets you control the exact value proposition, social proof, emotional framing, and CTA structure. Shamanth recommends a simple web-first flow — landing page → App Store link — as the first post-ATT experiment, well before building a full web checkout. Apps using this saw month-on-month growth through the ATT transition.
“The post pack goes to the networks but not the advertiser with ios 15 if the post back goes to the advertiser you can at the very least verify they're telling the truth with bonkers considering i think david you imagine uh until all this time you you just have to take the platform's word for it even pre-att.”
With ATT, Advertisers Have To Trust Platforms — iOS 15 Postbacks Finally Let You Verify
Before iOS 15, SKAdNetwork postbacks went to ad networks (Facebook, Google, TikTok) but NOT to the advertiser — meaning advertisers had to take platform-reported numbers on faith. TikTok was caught silently changing null conversion values to zero (different semantics). iOS 15 postback delivery to advertisers (via their MMP) finally enables verification that platform-reported data matches reality — a fundamental accountability shift in mobile advertising.
“I identified five Facebook groups for moms of kids with learning differences specifically dyslexia and then I did another five for ADHD I found the Reddit groups and I set a goal that I would post on all of them. I messaged six of my best friends and was like if I don't post in every single one of these groups by 10am Monday I have to run 10 miles.”
Reddit + Facebook Group Posting With Accountability — Early Niche Distribution Hack
Cliff's first distribution strategy was manual: identify the five most relevant Facebook groups and Reddit communities for his niche, then use social accountability with friends to ensure he actually posted. It worked well enough that Reddit flagged him as a bot and he had to create multiple accounts. The lesson is simple but often skipped — go where your specific users already congregate and post there systematically before spending on ads.
“I got a Google Sheets list of the top 100 best-performing consumer subscription companies in the world and I emailed every CEO, every head of growth. Then I hopped on Zoom calls with them and I flew to wherever they were in the world. I literally sat behind them in their office and looked at how they bought Instagram ads.”
Cold-Outreach to Top-100 Subscription CEOs — Sit in Their Office Watching Instagram Ads
Cliff built Speechify's ad playbook not by hiring an agency but by personally shadowing the people already doing it at scale — founders of Reflectly, Blinkist, Dollar Shave Club, Grammarly, and Whoop. His tactic: cold email the top 100 subscription CEOs, get one call, show up in person. He learned at Reflectly that stories convert better than posts and polls drive more engagement than static images — insights that shaped years of Speechify ad strategy.
“TV worked really well for us pre-pandemic and during the pandemic because that older cohort was there and that was the medium in which they trusted for advertisement. Digital was not something they necessarily trusted as widely. Facebook was and still is to some extent King of that demographic — but TV worked really well when that audience was there.”
TV and Radio to Reach Older High-LTV Demographics — Meet Them in Their Medium
Pray.com's primary audience is 45+ users who came to faith later in life and have higher lifetime values than younger cohorts. Rather than forcing this demographic into Instagram, Ryan ran TV and radio campaigns that met them in the medium they trusted. The result: TV buyers were 'blown away' by the effectiveness. The lesson is channel–audience fit: older high-LTV users respond better to broadcast trust signals than to performance digital.