Founder Playbook · Sub Club by RevenueCat
16 tactics from Daphne Tideman
Boost Conversion and Retention with Jobs to Be Done
Watch the full episode“Use a platform like user interviews.com or respondent.io to recruit users who have used similar solutions in solving the job to be done... give them a load of bogus options in there that they don't know what's the right answer. Don't ask them hey have you struggled with this painpoint yes or no because everyone will just say yes.”
Recruit interview subjects from competitor user pools and bury the pain in decoys
Pre-launch apps can still do JTBD research by recruiting on UserInterviews.com or Respondent.io from people who use a competing solution. Critically, never ask leading yes/no pain questions — bury the real pain among plausible decoys so participants can't pattern-match to what you want, and the genuine priorities surface.
“I ask them have you recommended this to friends and family... what questions did they ask you? Because they'll forget what they might have been worried about because they want to confirm hey no I made the right choice I wasn't concerned. But they'll remember the things that other people asked them.”
Ask referrers what questions their friends asked — not what worried them
Existing happy users rationalize away their original objections, so asking what worried them yields nothing. The clever workaround: ask what questions their friends asked when they recommended your app. Those second-hand objections are the real hesitations holding new users back — and exactly what your funnel needs to pre-empt.
“Look at with survey data working out who are the ones who are staying the longest. So who's been subscribed for a while who's actively using your app — they're not basically have a subscription they've forgotten about but they're actively using your app, they're making the most of it. Asking them through a survey — that's a really good technique if you have a big database to make sure that your interviews are more focused.”
Filter your subscriber base before booking interviews — long-tenured AND active
Random user interviews waste slots on passive subscribers who forgot to cancel. First segment via survey to find subscribers who are both long-tenured AND actively engaged, then run deeper interviews against that narrowed list. Every interview slot now skews toward your high-LTV future, not noise.
“If you've done that review mining and gone through all those reviews and kept a nice log of the reviews — yes you can speed it all up with AI but I really believe strongly in also just spending a few hours just completely immersing yourself in the language. You'll have this extra benefit on top of that with the messaging for the copy side of it where suddenly your language becomes more human.”
Read reviews end-to-end yourself — AI summaries strip the linguistic osmosis
AI can tag and cluster reviews 10x faster, but skipping the manual read costs you the unconscious absorption of customer phrasing. Future ad copy, landing pages, and push notifications will sound like a marketer wrote them instead of a user. Spend a few hours actually reading reviews end-to-end before letting AI summarize.
“All their ads were 35% off on GPS and health trackers. If your ad is just saying 'Get this disc this. This is what we do.' That's not speaking to someone's job to be done. But then I read one of their reviews and it was about this Siberian husky that had run off... If I saw an ad showing this I would have bought this years ago.”
Mine reviews for the breaking-point install moment, then restage it as your ad
Tractive sold dog GPS trackers via feature/discount ads and missed Daphne for years despite repeated lost-husky incidents. The unlock would have been an ad dramatizing the actual JTBD story — pulled directly from a customer review. Ads showing the rescue, not the SKU, convert latent buyers who don't yet know your category is their solution.
“Before I integrate it into all the messaging of the app itself I'll test it first. If you're running ads meta ads are great for this... I've literally tested very similar visuals with different messaging overlay on them... tested those against each other first on the job to be done level with a more general image that isn't skewing people too much.”
Pressure-test JTBD framings in Meta ads before touching in-app copy
Don't rewrite onboarding, paywall, or App Store copy on a hunch. Run cheap Meta ads with near-identical visuals but different JTBD messaging — click-through and cost-per-trial deltas tell you which framing wins before you spend cycles changing in-app surfaces. The visuals stay neutral so they don't bias the read.
“I've also right away used a usability hub to run 5-second tests with two messaging and ask people what their preference is and why and what stood out. That's also really really good in refining it because sometimes you use a word that just triggers people the wrong way or people don't understand what you mean.”
When CTR is too noisy, run UsabilityHub 5-second tests on the copy
Click-through rate doesn't always correlate with monetization, and small ad budgets produce noisy results. Run 5-second qualitative tests on UsabilityHub alongside CTR tests — they catch words that trigger people the wrong way or messaging that's trying to be too smart, which raw CTR can't explain.
“If you have an aha moment can you work that into the ad in some way that kind of brings people into the app already excited already motivated already kind of understanding that aha moment... With the competition there is now it's never been easier to make an app... you can't wait until they're in the app to get them to that moment.”
Pull the aha moment into the ad — never wait for onboarding to deliver it
Ladder failed Daphne's host because he only discovered the killer feature (custom-swappable exercises) deep inside onboarding — he never even saw their ads. With cheap app production making competition fierce, the aha needs to land in the ad surface itself, otherwise interested users churn before activation. Cal AI's TikToks of someone snapping a meal mid-routine is the canonical example.
“Rather than trying to tell people with your messaging — messaging is so much more than that. It's how you're bringing things across by showing them things rather than telling them. Going back to that Welltory example, they would basically pull in your data in the onboarding and you'd immediately get some insights about yourself in the onboarding rather than tell you we can give you insights.”
Welltory hack: pull live user data into onboarding so the aha is delivered, not promised
Stop writing copy that claims your app delivers insights or transformation. Manufacture a real demonstration during onboarding — pull live data, run a quick analysis, surface a personal insight. Welltory imports health data on first launch and serves up personalized insights inside the flow itself. The aha becomes proof, not marketing.
“Headspace, for example, they're very focused on sleep, but if you say you're struggling with anxiety and stress, you're going to see very different content in app than if you click on sleep… they're probably going to be showing a different form of social proof, they're probably going to be talking about different content, they're probably going to be using different language.”
Branch onboarding content by stated JTBD — different testimonials, copy, examples
One quiz answer should fork the entire onboarding: testimonials, copy, illustrations, and example content all shift to match the chosen job. JTBD-based personas outperform demographic personas because they tell you exactly which social proof and copy to surface for each user — Headspace runs this playbook at scale.
“Why don't we do a 3-day challenge potentially in the onboarding already and then actually have them already do that first gratitude practice so that they feel like I've already achieved something I've made progress already... What is the tiniest step you can get them to do to make them feel like they're making progress from A to B even if it's a false sense of progression.”
Manufacture a micro-win inside onboarding, even if it feels symbolic
For apps where the real outcome takes weeks (gratitude, fitness, language), engineer a micro-completion inside onboarding itself — first gratitude entry, first plan generated, first lesson done. Even a 'false sense of progress' beats dead air between sign-up and the user's first self-driven session. For tax apps, even checking off 'documents identified' works.
“I see brands shaping their pricing around the specific jobs to be done of users and having different packages based on what are you trying to achieve, what job to be done do you have. Mimo — there's people who want to learn to code just for fun and there's people who really want to become a developer.”
Tier pricing by job-to-be-done — not by feature checklist
Mimo splits pricing by intent (casual learner vs aspiring dev), not by feature count, and uses different support depths per tier. The host applies this to his weather app: a $20 Apple-data tier for casual users alongside a $40 premium-data tier — same product, two jobs, two price points.
“When you're solving new problems, that's a great time to think about that as well. If you create a really big new feature that solves a new job to be done or solves an existing job to be done in a much better way, that's a great opportunity to introduce that higher price tier… it's a lot easier than just raising the price across the board.”
Launch a new tier alongside a new feature — not as a standalone price hike
Bundling a price increase with a meaningful new capability reframes the change as added value rather than a take. Existing subscribers self-select up to the higher tier because they're paying for the new thing, not absorbing a hike. Time price changes to coincide with substantive feature launches.
“Tallow and Ash, it's an eco-friendly laundry detergent in the UK. They sponsored a post saying 'We're sorry about our previous packaging. It wasn't good enough. We've got new packaging.'… It was just an organic post probably on their social that they've just boosted. Hey sorry, we weren't fulfilling your job to be done… now we are.”
The "sorry, we got it wrong" boosted post for winning back churned users
A direct, public apology paired with paid distribution turns a known weakness into a reactivation lever — it signals to former users that the specific reason they left has been fixed. Pairs naturally with targeted email or push to anyone who churned during the broken period. The transparency itself becomes the campaign.
“Sweat is a really good example… I'd not been very consistent in it and I really wanted to see a difference, and they had all these programs that each time I could go from beginner intermediate advanced and really feel like I was getting to a next level… What is it that's going to lose them, and that's where your churned customers are so important to speak to.”
Watch for evolving jobs — boredom is the signal a JTBD has been outgrown
Long-term subscribers' jobs evolve — a recipe app that felt inspiring becomes repetitive once cooking skill grows. Build explicit ladders (beginner → intermediate → advanced) like Sweat does, and interview churned power users specifically about what stopped feeling fresh. Boredom = JTBD outgrown, not feature gap.
“I basically went into my dog park and just asked a load of people there who were walking their dogs... I said I'll give them a year free of it as a thank you for it and I just asked them to go through it sign up... I just put my phone record on. It was very lowkey nothing fancy.”
Find your research subjects in their natural habitat — dog park beats Zoom
Solo founders skip user testing because it feels expensive and scary. Daphne's hack: find your target user in their natural habitat (dog park for a pet app, cafe for a productivity app), trade an annual subscription or a coffee for 20 minutes, record on your phone. An hour or two yields enough friction findings to reshape your onboarding.