Why Your Free Users Are Your Real Growth Engine – Cem Kansu, Duolingo CPO

Duolingo's original model (~2013): the website was free; learners' translation exercises were crowdsourced into full article translations sold to outlets like CNN and BuzzFeed. Quality work pulled resources from teaching toward running a translation business.

Duolingo's first monetization experiment (2016): users could buy a repair of a broken streak for about $1-3.

Introduced ads in 2016 with a single placement: after a lesson is completed (not app open, not mid-lesson), still the only placement today. Team dogfooded the app daily to judge the feel, and A/B tested each ad change against retention, app closes and reviews.

Before deciding to launch ads in 2016, quantified complaints and negative reviews about ads per million DAU and compared them to complaints about other topics (e.g. a missing language course), alongside neutral retention in A/B tests.

Started with AdMob, then added Facebook, Flurry and later Unity to broaden demand. Blocked every category not brand- or young-audience-safe (gambling, sketchy games) and worked with networks on custom blocks (e.g. ads with three X buttons).

US-only direct ad deals: one of Duolingo's eight characters (e.g. Lily) introduces the brand's ad with a short custom-written joke script, then the ad plays. Formats are Duolingo-built. Goal: cut programmatic volume substantially in 2026.

At each lesson end an ML model picks between a programmatic ad and an internal ad (Super, Max, Duolingo English Test), maximizing LTV with purchase likelihood as the objective. If a user ignores the Super ad about three times it backs off. Internal ads are character-driven videos that explain the subscription features.

Ran a 1-2 year ads holdout, but no longer recommends year-long holdouts (they get forgotten and polluted by bugs). Now runs 3-6 month holdouts or long experiments on things like leaderboards (10 weeks to climb from lowest to top league) and push notifications, where novelty lifts fade.

After reading reviews where users asked to pay to remove ads, tested a one-time purchase of 30 ad-free days at $3, $5 and $10 instead of a subscription.

Duolingo Plus (2017): no ads plus fully offline use; because the purchase page looked thin with two features, added a third line 'Support Duolingo's mission', echoing what users said in reviews.

Added free trials to the subscription even though the core product was free and paid features (no ads, offline) did not need to be experienced. The trial removes payment hesitation.

Purchase pages explain the mechanics: 'this is a seven day free trial; on day five we'll send you a notification to see if you still want your subscription.'

Iterated purchase button copy in A/B tests: 'Subscribe' (baseline) -> 'Start my free trial' (big uplift) -> 'Try for free' (better) -> 'Try for $0.00' (best; believed live). Part of ~400 concurrent A/B tests, many on the purchase flow.

Hearts (now Energy) limit new lessons after a number of mistakes; free users regenerate by practicing old content, watching ads, or (then) an IAP. Made unlimited Hearts a subscription feature. Also added a Practice Hub tab to the subscription (later made free).

A/B tested giving Explain My Answer (an LLM-based Max feature explaining your specific mistake) free vs keeping it in Max. Revenue impact was small, and LLM costs had fallen with better caching, so it moved to free. Practice Hub was also moved from Super to free. Rationale: free users are the word-of-mouth engine; big trade-offs are signed off by senior leaders including the CEO.

Launched math as a separate app, planning one app per subject, then moved math, music and chess into the main app's course switcher with light promos. Picked subjects that are fun to teach on mobile, have existing demand, fit bite-sized skills and have a senior internal champion (skipped coding and running). Each took 6-12 months of prototyping and dogfooding.

Built a higher-tier subscription where users pressed a button to connect instantly with a human Spanish tutor, no booking. Users had requested it for years. Iterated to ease users in, then shut it down. Replaced by an AI video call with the character Lily.