How Duolingo Approaches Strategy, OKRs, and Discovery


How Duolingo Approaches Strategy, OKRs, and Discovery

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HERBIG.CO

PUBLISHED

Mar 7, 2025

READING TIME

4 min & 24 sec

​Dear Reader,​

Many Product Managers were in awe of the ways of working shared in The Duolingo Handbook a few weeks ago. While it’s an inspiring read, I used this as a reason to revisit some of my all-time favorite reads about how this company operates (or at least used to operate) and extract my takeaways with you.

Duolingo focuses on “Movable” Metrics for OKRs

via Meaningful metrics: How data sharpened the focus of product teams

Duolingo acknowledges the importance of broad strategic directions through lagging Impact metrics like Daily Active Users (DAUs). However, they have also realized this metric is strategically important but out of reach for steering and measuring individual initiatives.

After modeling driver metrics of DAU, they landed on Current User Retention Rate (CURR) as a potential driver. In the next step, they staffed a team who started running A/B tests to see whether 1) CURR is a metric they can move and 2) moving CURR actually moves DAU (remember: correlation does not equal causation!). And they were successful! With a team now focused on optimizing a movable metric, growth in DAU kicked off again. Since then, they have utilized the concept to set the quarterly OKRs of teams.

A Takeaway for your work: Revisit your team goals from a perspective of how movable (or, as I like to say, influenceable and leading) they are.

Duolingo Sometimes Puts Signals over Metrics

via How Duolingo builds product

To separate how the progress of metrics-based and feature-based teams are measured, they try to avoid the tyranny of metrics:

Obviously it’s harder to measure success with feature-based teams, but we’ve learned to work with that. We use a combination of qualitative and quantitative signals to see if we are tracking toward success, which looks different depending on the feature. For example, to measure how we are “making Duolingo more social,” we would use a few signals to decide if we are making progress:
- User research studies on the features we are building.
- Signals from public forums like Reddit and Twitter to see how our users are reacting to our features.
- Usage of features by Duos (Duolingo employees) and buzz around the features we are building. In other words, we look for how much Duos care about our features.

A Takeaway for your work: Don’t try to bend the success measures of teams whose work is hard to measure into obscurity by forcing them into conventional metrics structures. Acknowledge the importance of their work and focus on signals, not just metrics.

Duolingo’s Team Leads lean into their Unfair Advantage to create Space for Discovery

via How Duolingo builds product

Typically, a PM lead and an Engineering lead head up the team, sometimes joined by a Learning and Curriculum lead (experts in learning science, curriculum design, and educational content creation), Biz Ops lead, or Marketing lead, depending on what the team is working on.
Team co-leads are ultimately responsible for their team’s success and deciding its roadmap. They also own the decisions in their domain. For example, the PM co-lead owns more of the product discovery and roadmap decisions, whereas the engineering lead owns more of the product delivery and implementation decisions.

A Takeaway for your work: Even within a Trio, you want to make sure every member can focus on their unfair advantage instead of forcing everyone into the same type of work just for the sake of “equal collaboration.”

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Thank you for Practicing Product,

Tim

PS.: As I prepared for an in-house training day on adopting the Press Release FAQ format in the last weeks, I truly enjoyed Marcelo Calbucci's book on implementing this concept.

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Thinking in Metrics

The most effective metric strategies often combine qualitative insights, quantitative measures, leading indicators, and strong signals. Let’s consider an example scenario: A new e-learning platform wants to improve user engagement and retention. First, the team uses qualitative metrics by interviewing users to understand pain points and unmet needs, discovering that users find the current onboarding process confusing. Next, they implement quantitative metrics by tracking daily active users and completion rates for courses. The team also establishes leading indicators to predict engagement, such as tracking users who create a learning plan and finish at least one lesson. Finally, they evaluate strong indicators by identifying users who complete 50% or more of a course, as this action signifies a deeper commitment to the platform. By combining these metrics, the team gains a well-rounded understanding of user behavior and actionable insights to improve product experience.

A guide to AI prototyping for product managers

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Who is Tim Herbig?

As a Product Management Coach, I guide Product Teams to measure the real progress of their evidence-informed decisions.

I focus on better practices to connect the dots of Product Strategy, Product OKRs, and Product Discovery.

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