Briefs

The Learner Home is Preply's highest-traffic surface and one of its most contested — multiple teams run experiments on it simultaneously, each optimising for their own metric, with no shared rubric for what a "good" Learner Home actually looks like. The result is a page that has grown through accretion rather than intention: banners, widgets and nudges added over time without a coherent logic connecting them. We have limited institutional memory of what's been tried before — which experiments ran, what they measured, whether they succeeded or failed and why — making it easy to repeat mistakes or abandon directions that were actually promising. Underneath all of this is a more fundamental gap: the Learner Home currently treats all users the same, regardless of where they are in their learning journey. A learner with a lesson starting in ten minutes has completely different needs from someone booking their first trial or someone in the gap between sessions, yet the page doesn't meaningfully distinguish between them. This project is an attempt to establish a clearer foundation — a rubric that defines what the Learner Home should do and for whom, and a lifecycle-aware design that gives each user a single, unambiguous next step rather than a competing set of calls to action.

Learners who have completed 3+ lessons show significantly higher satisfaction signals but we're not capitalising on that moment to drive referrals. The current refer-a-friend mechanic exists but is buried in account settings — it's not surfaced at moments of peak motivation. We're leaving organic acquisition on the table. Objectives: Surface the referral prompt at the right moment in the learner journey (post-lesson, post-milestone) Understand what incentive structure actually motivates sharing vs. passive awareness Test whether timing the referral ask to lesson completion improves referral conversion

Project name: Expansion — Non-Language Subjects on the Homepage Squad: Expansion Quarter: Q2 2026 Problem statement: Preply has a growing catalogue of non-language subjects but the homepage experience is built entirely around language learning. Learners with interest in maths, music, coding, or other subjects have no clear entry point from the homepage, making NLS effectively invisible to a large portion of potential learners. This suppresses NLS discoverability and limits NTGMV share. Objectives: Introduce NLS as a credible, discoverable category on the homepage without undermining the core language value proposition Understand how learners currently discover NLS tutors and where the journey breaks down Identify the lightest intervention that drives meaningful NLS entry and conversion Primary metric: NLS NTGMV share / NLS session starts from homepage entry Related squads: Matching (tutor card and search), AI Marketplace (personalised subject ranking), Tutor Content (NLS profile data quality) In scope: Homepage subject entry points, NLS category landing experience, subject browsing and filtering Out of scope: Tutor onboarding for NLS subjects, B2B NLS, young learners (separate brief) Current state: NLS is accessible via search and direct URL but is not surfaced in the homepage information architecture. No current experiment running on homepage NLS exposure.