Audience-Building Playbooks
Building an audience before, during, and after launch — the platforms that compounded, the posting cadences that worked, and how founders turned followers into first customers.
198 tactics · page 4 of 7
“If you can avoid on any individual pitch I would never include tracking pixels or links it's just sort of a matter of respect... especially never ever call them on oh I saw you open my email, creepy, it's really weird.”
Never Use Tracking Pixels On Individual Journalist Pitches — It Destroys Trust Instantly
Using read-receipts or tracking pixels on individual journalist pitches is a hard no. Most writers have images auto-blocked specifically to avoid tracking. If you mention you saw them open your email, the pitch is dead — it feels like surveillance, not outreach. For mass campaigns the expectation is different, but for one-to-one pitching, treat it like a personal email.
“As a business owner it doesn't matter like there's no filter on your iOS or Android person... a lot of people have small business owners there are a lot of them in the world who are Android and so they spend more, they just spend the money that the subscription makes them more successful.”
Business-Value Apps Beat The iOS Bias — Android Users Pay When The App Makes Them Money
Photoroom defied the 30X photo-app iOS ratio because their users buy for business ROI, not hobbyist enjoyment. Small business owners on Android pay just as readily as iPhone users when the product helps their store grow. The lesson: if your subscription makes users money, the iOS premium shrinks dramatically. B2C apps based on pleasure/aesthetics face a steeper Android challenge than B2B-adjacent tools.
“You can hire a creator to play just the role of the actor. If you know how to do marketing you can give them a script that you know will perform well on the For You page.”
You Are the Director, TikTok Is the Distribution — Hire Creators Just to Act
Traditional influencer marketing bundles three roles: director (script), actor (on-camera talent), and distribution (audience). TikTok unbundles them. A founder who understands the algorithm can write a script, hire a charismatic creator to deliver it, and let TikTok's FYP handle distribution. This means you no longer need to pay for access to an established audience — the platform substitutes for it, dramatically lowering the cost of influencer marketing.
“I always first with the jobs to be done try to narrow it down a bit... I also look at like what is the problem that we're trying to solve how good are we at actually solving that problem how good a solution are we for that and how much chance is there that we can differentiate.”
Narrow Your JTBD Candidates to 2-3 Before Writing Any Messaging
Most apps have 5-10 plausible jobs-to-be-done but only 2-3 are worth leading with. Daphne's filter: prioritize jobs where the app actually solves the problem well AND where there is room to differentiate from competitors. Starting with a shorter, sharper list prevents the common mistake of diluting every touchpoint by trying to cover everything at once.
“I'll narrow that down and then what I'll do before I integrate it into all the messaging of the app itself is I'll test it first... understanding which angles the competitors are going on because back to the weather app like if everyone's just saying you know check it within a moment or talk about the weather like then you're not going to stand out.”
Use Competitor Analysis to Find Messaging White Space, Not Validate Your Own
Competitor analysis in a JTBD context isn't about copying what works — it's about identifying which angles are already saturated so you can own a different one. If every weather app says 'check the weather quickly,' that job is commoditized. Daphne maps competitor messaging first to find the angles that are still unclaimed, then tests those in ads.
“We spent a lot of time defining at a very granular level who are the relevant personas for ladder... on one side of the spectrum we have Pilates influenced at home with a dumbbell with a lot of women who are newer to strength on the other side we have advanced gym goers full access to equipment.”
Define Granular Personas and Target Each One With Dedicated Creatives Before the Download
Ladder maps to two polar opposite personas — beginner home-workout women and advanced gym-goers — and creates distinct ad creatives, coaches, and team identities for each. This persona segmentation happens before the app download: the creative and quiz self-select users into the right program, so the product can immediately deliver on the promised experience rather than trying to serve everyone the same way.
“growth would be easier if... people focused obsessively on turning their earned audience into an owned audience... it used to be that if you got a pretty sizable following you could count on that to distribute your products i don't think that's the case any longer”
Convert Earned Audience To Owned Audience Before Algorithm Shifts Kill Your Distribution
Algorithm changes on TikTok, YouTube, LinkedIn, and SEO are reshuffling distribution. A large following no longer guarantees reach. The solution is three-pronged: create a direct channel (email), build content habits so people come proactively, and build product habits that survive any algorithm change. Owned distribution is the only distribution that compounds reliably through platform turbulence.
“The most recent article I wrote... I spoke about tracking cost per winner This is a metric that we track closely that considers production costs as well as actual ad spend costs.”
Track Cost Per Winner, Not Just Cost Per Creative — Production + Ad Spend Together
Most growth teams track CPC or CPM; Nathan Hudson argues the right metric is cost per winning creative — the fully-loaded cost of finding an ad that actually works, combining production expense and testing spend. AI lowers production cost but increases the quantity of losers to test through, so the cost-per-winner metric captures the real efficiency gain (or loss) of moving to AI-generated creatives.
“I always love it when we get an App Store review and they shout out someone on our team — if people start reading about great customer support again another reason why people might download the app in the first place.”
Great Support Reviews in the App Store Are Both Retention and Acquisition
Named shout-outs to support agents in App Store reviews create a compounding trust signal. A potential customer reading reviews sees not just product praise but evidence of a specific human who cared. This differentiates from competitors whose reviews say nothing about support quality. Captions systematically celebrates agent call-outs internally in its #c4l Slack channel to reinforce the culture that generates them.
“what we did do well was we really communicated with the mint folks primarily through Reddit and just said hey here's kind of our philosophy... people like that okay these folks sound authentic they sound like they're here to like actually listen to us.”
Use Reddit to Rebuild Trust With an Audience That Just Got Burned by a Competitor
Former Mint users arrived distrustful — they had just had an app they relied on ripped away by a company that did not care about them. Monarch's response was not polished marketing but direct, honest engagement on Reddit, leaning into the founder's history as Mint's first PM. For any app capturing users from a disrupted competitor, authenticity on community platforms matters more than ad spend during that critical window.
“of those maybe 30,000 people... all the headline oh another weather app I don't care even if they are potentially even my like ideal customer profile they already have three weather app subscriptions and they don't even get past the headline.”
Press Attention With Weak ICP Overlap Barely Converts — Don't Confuse Eyeballs With Buyers
Weather Up was covered by TechCrunch, 9to5Mac, MacRumors, and others — meaningful press by any indie standard. But David estimated the actual overlap between people who read those outlets, cared about weather apps, did not already have a preferred one, and were willing to pay $40 was tiny. For most consumer apps, press is a low-conversion channel unless the ICP and readership align tightly.
“Your subject line needs to be simple — it needs to either be very short or very long. Are you putting everything in the subject line and the body is basically just here's the contact information? Or are you trying to say please look at this because XYZ very short very simple and then get in there and make your pitch.”
Your subject line has 2 seconds — keep it ultra-short or put everything in it
Most journalists scan sender and subject line — nothing else. Two strategies work: cram all the signal into a long subject so opening is optional, or write a tiny subject hook that pulls them in to a short body. Everything in between — medium-length subject with medium-length copy — gets ignored. News-hook pitches built around current events almost never work.
“It really is about getting covered by writer why — they have an audience that they are trying to intentionally build and it's just like anything else: why do you advertise on Google instead of advertising on the street?”
Target the writer, not the publication — they carry a portable audience
A publication's name on a story matters less than which writer wrote it. Journalists build specialist audiences that follow them across publications. Pitch the fintech writer for your fintech app, the productivity beat reporter for your productivity tool. That writer's portable readership — pre-qualified and engaged on exactly your topic — is worth far more than a blast to a tips inbox.
“There really are three kind of major reasons: market intelligence — what are competitors doing, what are people investing in — recruiting, and then raising money. User acquisition is the fourth reason but it's very targeted.”
TechCrunch readers want market intel, recruiting leverage, and fundraising fodder — not user acquisition
Understanding why people read TechCrunch tells you what story to pitch. Founders use TC coverage for investor deck replacement, recruiters use it for talent narrative, and competitors use it for market tracking. User acquisition happens as a side effect — but pitching TC purely for downloads misses its real leverage and leads to poorly framed pitches.
“When you pitch TechCrunch you need to be thinking about that kind of storytelling — and when you pitched 9to5 Mac you don't send them the same pitch that you sent to the writer at TechCrunch, you send them a totally different pitch.”
Match your pitch to the outlet's job-to-be-done — 9to5Mac and TechCrunch need totally different pitches
9to5Mac covers new apps as a service to enthusiasts who want to know what exists — they care about design, features, and novelty. TechCrunch covers companies as historical artifacts in an industry narrative — they care about what changes and why it matters to builders and investors. Sending the same pitch to both signals you haven't done the homework and reduces the chance either publishes.
“When scan was the only way of attributing traffic it was a real big issue if you weren't spending 10K plus a month you weren't going to hit your privacy thresholds you weren't going to see your postbacks I would not want to launch any kind of app promotion campaign with a 1K budget just because I'd be pretty cautious about what we're going to get back.”
Spend $1K on Web and Actually Learn Something — App Requires $10K+ to Hit Privacy Thresholds
Meta's SKAdNetwork required $10K+ monthly spend before privacy thresholds unlocked meaningful postbacks for in-app campaigns. On web, UTM parameters give clean channel-level attribution from dollar one — no MMP required. This asymmetry makes web2app the right testing environment for early-stage apps that need to learn before they scale, not the other way around.
“Affiliates and influencers amazing with web again it comes back to the attribution piece if I'm working with influencers and creators and I want them to push my product it's hard to attribute when it's mobile on web I can just take them to a dedicated landing page UTM parameters perfect.”
Influencer Attribution Finally Works via Web — Dedicated Landing Page + UTM Solves the Black Box
Influencer campaigns for mobile apps have always had a black-box attribution problem — you don't know which creator actually drove conversions. Web2app solves this cleanly: give each influencer a dedicated landing page with UTM parameters and you get exact conversion data per creator. Nathan calls this one of the most overlooked benefits of web2app — it doesn't just unlock new ad channels, it makes existing influencer channels measurable for the first time.
“It's a little bit of a different audience than you might get on some of your traditional app-based marketing channels the audience tends to skew a little bit older they actually tend to be a little bit non-technical when you're talking linear TV radio and for us it worked really well these people are usually high income and they buy.”
TV and Radio Reach Older High-Income Buyers That App Channels Systematically Miss
The audience reachable on linear TV and radio is structurally different from Meta and TikTok: older, less technical, and with higher willingness to pay. For subscription apps that struggle to monetize under-30 users, offline channels provide direct access to the cohort that actually converts. Stephen notes this is why so many app companies ignore it — they're measuring the wrong audience problem.
“For digital you're generally you know measuring an ad click and offline we're leveraging how did you hear responses it's just another data point that we get to better tell the story of what is happening and where everyone's coming from and some of those people are saying that they heard about us through TV or radio.”
"How Did You Hear?" Survey Data Fills the Attribution Gap for Offline Channels
Offline attribution can't rely on click data. Babbel's approach combines web analytics, MMP click data, incrementality tests, media mix modeling, spike attribution (measuring traffic lifts when TV spots air), and 'how did you hear' survey responses from all new users. No single signal is sufficient — the goal is triangulation, not precision. Stephen's framing: attribution was always messy; offline just makes you accept that reality earlier and build a multi-signal model sooner.
“We saw that about 70 even more% of the people coming through onboarding were saying that they were beginners we did research to validate that so now really we're really focusing the product on that beginner experience the intensity of the classes the complexity of the classes to try to cater for that audience.”
75% of Users Are Beginners — So the Whole App Should Be Built for Beginners
Zumba had a brand built on die-hard fans but discovered through onboarding data that 75%+ of app users were self-identified beginners. The correct response was to stop trying to serve all segments and double down on beginner content — simpler choreography, lower intensity — so that audience actually reached the aha moment and stayed.
“one user told us like oh my girlfriend would love that she's selling on depop and after multiple users asking us in support talking at the user interview at mcdonald we realized that oh that's a niche that we should kind of focus on”
E-Commerce Niche Wasn't Planned — It Emerged From Support Tickets and McDonald's Interviews
PhotoRoom's defining market positioning — the best photo editor for entrepreneurs selling on Depop, Poshmark, and Shopify — was not a strategic decision made in advance. It emerged from repeated signals in customer support tickets and McDonald's user interviews. The team noticed the pattern, validated it, then deliberately doubled down. Niche clarity often comes after launch, not before.
“We're working with a pretty high-intent audience and that gives us a lot of surface area to test different paywalls and different things at that onboarding funnel — that's been the biggest single factor in our retention.”
High-Intent Organic Users Are Your Paywall Test Lab
Burner's UA strategy focuses on harvesting existing intent — people searching for a second phone number or burner app — rather than broad-reach branding. A high-intent install base allows rigorous paywall and onboarding experiments because the signal isn't contaminated by people who were only mildly curious.
“You built an email list, you built a community, you built demand for a product that didn't yet exist to the point where people were begging you to build an app. That's such a different mindset than a lot of developers who start with the app as the end all.”
Build the Community Before the App — Demand Pulls Better Than Acquisition Pushes
Mark Kennedy spent years building the None to Run blog, email list (20K subscribers), and Facebook group before a developer approached him to build an app. The result: 1,000 paying subscribers in the first month, an 89% trial conversion rate from that launch cohort, and steady organic growth ever since — with zero paid acquisition at launch. Community-first flips the unit economics of an app launch.
“We got a 10 percent lift from the new screenshots. You put the old screenshots to 50, the new screenshots to 50, it runs until it gives you a high confidence rate and it tells you what percent better the new screenshots are as far as impressions to app downloads.”
App Store Screenshot A/B Test Delivered 10% Lift With Apple's Built-In Tool
None to Run used Apple's native A/B testing in App Store Connect to compare old developer-designed screenshots against a professionally redesigned set. The test ran until statistical confidence was reached and showed a clean 10% lift in impression-to-download rate. It's a reminder that App Store creative is a lever most indie developers leave untouched — and the test infrastructure to measure it is now free and built in.
“Consumers in general — not just on TikTok — are hyper aware of things that feel like ads. On the For You Page it needs to feel as authentic as possible because you haven't built that audience trust over time — they're just watching a video cold.”
TikTok FYP is cold-trust territory — build genuine value before any install ask
TikTok's interest-graph distribution means every video is a cold impression. Unlike Instagram followers or email subscribers, FYP viewers have no pre-existing relationship with you. This changes the entire marketing contract: you have to deliver genuine value — entertainment, information, or a compelling story — before any commercial ask lands. The install is the reward the creator earns, not the opener.
“talking to users is really interesting because you learn a lot about your app and how it's used especially for an app like that where the it's a utility that has a number of different applications right”
User interviews reveal power-user segments and use cases that product teams never imagine
Burner discovered through user conversations that some customers manage 50+ separate numbers for individual contractors — a power use case product never anticipated. This insight directly informed the 10-line subscription tier, which became a strong performer. Utility apps in particular have user workflows that are invisible until you talk to people.
“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.
“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.
“If you manage to identify who are these small business one-person business solopreneurs they typically have a completely different value — because it's their business the price sensitivity is obviously very different from somebody who's just trying to make their photo look better.”
Identify consumer vs. prosumer/solopreneur users — their LTV is categorically different
Many apps serve both casual consumers and one-person businesses without distinguishing between them. Thomas Petit finds that solopreneurs and small-team users — people whose livelihood depends on the app's output — show dramatically higher LTV, lower churn, and different price sensitivity than purely recreational users. Identifying which onboarding answers or behaviour patterns predict this persona allows you to steer ad platforms toward acquiring more of them.