How ElevenReader grows

The team ran many experiments smoothing and optimizing the onboarding flow (quiz, goal setting) following popular blog advice. In hindsight Jack would first test the base case: a signup form straight into the zero-state product, e.g. strip onboarding entirely for 10% of users for five days, to size the incremental retention, engagement and monetization the flow unlocks.

ElevenReader removed an entire pricing tier and rewrote plans to be expressed in hours of listening/playback rather than tokens, credits or characters, abstracting away AI cost details for consumers even though real compute costs exist. Separate apps let each app price to its own users (flagship creator app keeps credits).

Instead of onboarding quizzes and goal setting, shorten the path to the aha: user brings their own ebook, EPUB or PDF and quickly finds a voice they want to hear it in. Fewer screens, clear about who the app is for.

Run two tracks at once: earned-media launches (about monthly per team, more than once a quarter) targeted to the right channels (all channels, email, LinkedIn or not), plus a steady paid UA engine. Earned attention feeds ElevenLabs branded search, the first paid target, and makes paid ads convert better because people already know the brand.

From a horizontal platform, pick an ideal persona and form factor and build a dedicated app (creator app vs PDF/ebook reader). Give each app its own brand and look so store searches for 'ElevenLabs' and for 'turn anything into an audiobook' each find the right app, while sharing a design system and tech. More small bets are being incubated.

Monitor mentions across Reddit, Discord and X. Found users share screenshots and screen recordings of a chosen voice reading their content (e.g. a famous-sounding voice reading homework). The team is putting more fuel on that; also redesigning the voice orb to feel more emotive.

Run paid UA on Meta, TikTok, Apple Search Ads, Google Play and Google Ads, reviewing results incrementally each week or month per channel rather than paid in aggregate. Because brand launches build awareness, they don't have to optimize for day-0/6/8 conversion or week-one payback and instead test into the right payback or LTV:CAC while testing new creatives.

The two apps share database, MMP and testing tools. UGC made by the same creators/agencies is boosted on paid or posted organically, and what works for one app is picked up by the other. MMP events that carry the most signal are mapped and defined consistently across apps. Retention, engagement and monetization are built separately per app.

Both app growth leads test their own paywall hypotheses at the same time and compare notes, roughly doubling or quadrupling the surface area for learning.