How Duolingo reignited user growth

The story behind Duolingo's 350% growth acceleration, leaderboards, streaks, notifications, and innovative growth model
Duolingo 350%增长加速背后的故事，排行榜、连胜、通知和创新增长模式

Feb 28, 2023

👋 Hey, Lenny here! Welcome to this month’s ✨ free edition ✨ of Lenny’s Newsletter. Each week I humbly tackle reader questions about product, growth, working with humans, and anything else that’s stressing you out about work.
👋 嘿，这里是 Lenny！欢迎阅读本月的 Lenny 通讯的 ✨ 免费版 ✨。每周我都会谦卑地回答读者关于产品、增长、与人合作以及工作中让你感到压力的任何其他问题。

If you’re not a subscriber, here’s what you missed this month:
如果您不是订阅者，以下是本月您错过的内容：

Subscribe to get access to these posts, and every post.
订阅以获取对这些帖子以及每篇帖子的访问权限。

I was at a small event a few months back where Jorge Mazal (former CPO of Duolingo) shared the story behind Duolingo’s growth reaccelerating. I was captivated. I’ve never seen a growth story like this before—4.5x growth for a mature product, driven by a small handful of product changes, rooted in an innovative growth model, and explained in such actionable detail. I asked Jorge if he’d be willing to share (and expand on) the story with a broader audience, and I’m so happy he agreed. Many products already look to Duolingo for inspiration, and I suspect this story will only increase that trend. Enjoy!
几个月前，我参加了一个小型活动，Jorge Mazal（Duolingo 的前首席产品官）分享了 Duolingo 增长重新加速背后的故事。我被深深吸引。我以前从未见过像这样的增长故事——成熟产品的增长率提高了 4.5 倍，这是由少数产品变化推动的，根植于创新的增长模式，并且以可操作的细节解释清楚。我问 Jorge 是否愿意与更广泛的受众分享（并扩展）这个故事，我很高兴他同意了。许多产品已经寻求 Duolingo 的灵感，我相信这个故事只会加剧这种趋势。享受吧！

Follow Jorge for more on LinkedIn and Twitter.
关注豪尔赫的 LinkedIn 和 Twitter 获取更多信息。

I joined Duolingo as the Head of Product in late 2017. Duolingo was already the most downloaded education app in the world, with hundreds of millions of users, fulfilling its mission to “develop the best education in the world and make it universally available.” However, user growth was slowing down. By mid-2018, daily active users (DAU) were growing at a single-digit rate year-over-year, which was troubling, given the explosive growth the company had seen in the past. This was a problem for a startup with investors anxious to see fast monetization growth.
我于 2017 年底加入了多邻国，担任产品负责人。多邻国已经是全球下载量最大的教育类应用，拥有数亿用户，实现了“开发全球最好的教育并让其普遍可及”的使命。然而，用户增长速度正在放缓。到 2018 年中期，每日活跃用户（DAU）的年增长率仅为个位数，这令人担忧，考虑到公司过去曾经历的爆炸式增长。对于一家渴望快速实现盈利增长的初创公司来说，这是一个问题。

In this post I’ll cover some of our early failures and then our first big wins that helped us turn around growth, including launching leaderboards, refocusing on push notifications, and optimizing the “streak” feature. These, together with several other efforts across Product and Marketing, helped us grow DAU by 4.5x over four years. Robust organic user growth supercharged Duolingo toward its 2021 IPO.
在这篇文章中，我将介绍一些我们早期的失败，以及帮助我们扭转增长局面的首次重大成功，包括推出排行榜、重新关注推送通知以及优化“连胜”功能。这些举措与产品和营销方面的其他努力共同帮助我们在四年内将日活跃用户增长了 4.5 倍。强劲的有机用户增长为 Duolingo 朝着其 2021 年的 IPO 目标提供了强大动力。

This article is an in-depth look into that journey. It is my hope that sharing this work will help others find their own growth breakthroughs faster.
本文深入探讨了这一旅程。我希望通过分享这篇作品，能帮助他人更快地找到自己的成长突破。

Phase 1: Increasing gamification

Our first attempt at reigniting growth was focused on improving retention, i.e. fixing our “leaky bucket” problem. We prioritized working on retention over new-user acquisition because all of our new-user acquisition was organic, and, at the time, we didn’t have an obvious lever to pull to supercharge that. Also, some of us had a suspicion that we could improve retention through gamification. There were two main reasons why this felt like the right approach to me. First, Duolingo had already implemented several gamification mechanics successfully, such as the progression system on the home screen, streaks, and an achievements system. And second, top digital games at the time had much higher retention rates than our product, which I took as evidence that we hadn’t yet reached the ceiling for gamification’s impact.
我们重新点燃增长的第一次尝试是专注于改善留存，即解决我们的“漏斗”问题。我们优先考虑改善留存而不是获取新用户，因为我们所有的新用户获取都是有机的，当时我们没有明显的杠杆来加速这一过程。此外，我们中的一些人怀疑我们可以通过游戏化来提高留存率。这让我觉得这是正确的方法有两个主要原因。首先，Duolingo 已成功实施了几种游戏化机制，如主屏幕上的进度系统、连胜和成就系统。其次，当时的顶级数字游戏的留存率比我们的产品高得多，这让我相信我们还没有达到游戏化影响的上限。

Duolingo’s gamified Home and Achievements pages
Duolingo 的游戏化首页和成就页面

Armed with a short presentation I co-created with our chief designer, we were able to get just enough buy-in from the rest of the executive team to create a new team, the Gamification Team. The team consisted of an engineering manager, an engineer, a designer, an APM, and me.
携带着与我们的首席设计师共同创作的简短演示文稿，我们得以从执行团队的其他成员那里获得了足够的支持，从而创建了一个新团队，即游戏化团队。该团队由一名工程经理、一名工程师、一名设计师、一名产品经理助理和我组成。

But there was one small issue: we had no idea which incremental gamification mechanics would work for Duolingo.
但是有一个小问题：我们不知道哪种渐进式游戏化机制适用于 Duolingo。

Our team at the time was hooked on a game called Gardenscapes, a mobile, match-3 puzzle game similar to Candy Crush. This mobile game became our first inspiration.
当时我们团队迷上了一个叫做《花园物语》的游戏，这是一款类似《糖果传奇》的移动端连连看益智游戏。这款移动游戏成为了我们的第一个灵感来源。

As we looked at the different game mechanics in Gardenscapes, we didn’t really know what we were looking for—we just knew that Gardenscapes seemed stickier than Duolingo, and we saw several parallels. A three-minute Duolingo lesson felt similar to a Gardenscapes match-3 level, and Duolingo and Gardenscapes both used progress bars to provide visual feedback on how close the user was to completing the session. Gardenscapes, however, paired its progress bar with a moves counter, which Duolingo didn’t do. The moves counter allowed users only a finite number of moves to complete a level, which added a sense of scarcity and urgency to the gameplay. We decided to incorporate the counter mechanic into our product. We gave our users a finite number of chances to answer questions correctly before they had to start the lesson over.
在我们研究《花园物语》中的不同游戏机制时，我们并不真正知道我们在寻找什么——我们只是知道《花园物语》似乎比多邻国更吸引人，我们看到了一些相似之处。三分钟的多邻国课程感觉类似于《花园物语》的配对 3 级别，多邻国和《花园物语》都使用进度条来提供用户完成会话的视觉反馈。然而，《花园物语》将其进度条与移动计数器配对，而多邻国没有这样做。移动计数器只允许用户有限次移动来完成一个级别，这为游戏增添了一种稀缺感和紧迫感。我们决定将这种计数器机制纳入我们的产品。我们给予用户有限次机会在开始课程之前正确回答问题。

It took our team a couple of months of work to add the counter. With the release of the update, I expectantly waited for an unmitigated success. Depressingly, the result of all that effort was completely neutral. No change to our retention. No increase in DAU. We hardly got any user feedback at all. I was deflated. The greatest effect the initiative had was on our team. After the results came out, we quickly fell into dissension. Some wanted to continue iterating on the idea, while others wanted to pivot. The team almost immediately (and dramatically) disbanded, and the idea was abandoned. It was pretty awful. The one silver lining of this failure was that I learned a lot about the company culture and about how to improve my personal leadership style—though that’s for a different article.
我们团队花了几个月的时间来添加这个计数器。更新发布后，我满怀期待地等待着取得巨大成功。令人沮丧的是，所有努力的结果完全是中性的。我们的留存率没有任何变化。日活跃用户也没有增加。我们几乎没有得到任何用户反馈。我感到灰心丧气。这个举措最大的影响是对我们团队的影响。结果一出来，我们迅速陷入了分歧。一些人希望继续对这个想法进行迭代，而另一些人则希望转变方向。团队几乎立即（并且戏剧性地）解散了，这个想法也被抛弃了。这真是太糟糕了。这次失败唯一的一点好处是我对公司文化以及如何改进我的个人领导风格有了很多收获，尽管这是另一篇文章的内容。

The first attempt to reignite growth through more gamification resulted in a dumpster fire.
试图通过更多的游戏化来重新激发增长的第一次尝试导致了一场垃圾箱火灾。

Phase 2: Referrals

Feeling burned after our gamification effort, we completely pivoted away from improving retention and put together a new product team focused on acquiring new users, called the Acquisition Team. At the time, Uber was doing well with user acquisition and had reputedly grown largely because of its referral program. Inspired by this, we created a referral program similar to Uber’s. The reward was a free month of our premium subscription, Super Duolingo (at the time, it was called Duolingo Plus). Seemed like a pretty good offer to us!
在我们的游戏化努力失败后，我们彻底转变了方向，不再致力于提高留存率，而是组建了一个专注于获取新用户的新产品团队，名为“获取团队”。当时，Uber 在用户获取方面做得很好，据说主要是因为其推荐计划。受此启发，我们创建了类似于 Uber 的推荐计划。奖励是我们的高级订阅服务“Super Duolingo”（当时称为 Duolingo Plus）的免费一个月。对我们来说，这似乎是一个相当不错的优惠！

We implemented the feature and hoped our second attempt would be more successful. Instead, new users increased by only 3%. It was positive, but not the type of breakthrough we needed. Still, the team doubled down and pushed through, shipping iterations to the referral program and making some other bets, but no avail.
我们实施了这一功能，并希望我们的第二次尝试会更成功。然而，新用户仅增加了 3%。这是积极的，但并非我们所需要的突破。尽管如此，团队加倍努力，不断推进，对推荐计划进行迭代，并进行其他一些尝试，但都无济于事。

While the team continued to iterate, it became clear to me that we had to find a different approach to tackle our growth problem.
团队不断迭代的过程中，我意识到我们必须找到不同的方法来解决增长问题。

Time to regroup

The aftermath of these back-to-back failures in only a few months was a period of reflection for me about making better product bets.
这些接连发生的失败仅仅几个月后的后果是我反思如何做出更好的产品抉择的时期。

In hindsight, it became clear why the Gardenscapes moves counter was not a good fit for our product. When you are playing Gardenscapes, each move feels like a strategic decision, because you have to outmaneuver dynamic obstacles to find a path to victory. But strategic decision-making isn’t required to complete a Duolingo lesson—you mostly either know the answer to a question or you don’t. Because there wasn’t any strategy to it, the Duolingo moves counter was simply a boring, tacked-on nuisance. It was the wrong gamification mechanic to adopt into Duolingo. I realized that I had been so focused on the similarities between Gardenscapes and Duolingo that I had failed to account for the importance of the underlying differences.
事后看来，我们的产品为什么不适合 Gardenscapes 的移动计数器变得清晰起来。当你玩 Gardenscapes 时，每一步都感觉像是一个战略决定，因为你必须越过动态障碍物找到通往胜利的道路。但完成 Duolingo 课程并不需要战略决策——你大多数时候要么知道问题的答案，要么不知道。因为它没有任何策略，Duolingo 的移动计数器只是一个无聊的、附加的讨厌东西。这是错误的游戏化机制，不适合应用到 Duolingo 中。我意识到我过于专注于 Gardenscapes 和 Duolingo 之间的相似之处，而忽略了基本差异的重要性。

It also did not take long to understand why our referral program did not produce Uber-like success. Referrals work for Uber because riders are paying for rides on a never-ending pay-as-you-go system. A free ride is a constant incentive. For Duolingo, we were trying to incentivize users by offering a free month of Super Duolingo. However, our best and most active users already had Super Duolingo, and we couldn’t give them a free month when they were already in a plan. This meant that our strategy, which needed to rely on our best users, actually excluded them.
我们很快就明白了为什么我们的推荐计划没有取得像 Uber 那样的成功。推荐对 Uber 有效是因为乘客在一个永无止境的按需付费系统上支付乘车费。免费乘车是一个持续的激励。对于 Duolingo 来说，我们试图通过提供一个月的超级 Duolingo 来激励用户。然而，我们最好和最活跃的用户已经拥有了超级 Duolingo，当他们已经在一个计划中时，我们无法给他们一个免费月。这意味着我们的策略，本应依赖于我们最好的用户，实际上排除了他们。

In both of these situations, we had borrowed successful features from other products, but the wrong way. We had failed to account for how a change in context can impact the success of a feature. I came away from these attempts realizing that I needed a better understanding of how to borrow ideas from other products intelligently. Now when looking to adopt a feature, I ask myself:
在这两种情况下，我们都从其他产品中借鉴了成功的特点，但方法错误。我们未能考虑到环境变化如何影响特点的成功。我从这些尝试中领悟到，我需要更好地理解如何聪明地从其他产品中借鉴想法。现在在寻求采纳某个特点时，我会问自己：

Why is this feature working in that product?
为什么这个功能在那个产品中运作？
Why might this feature succeed or fail in our context, i.e. will it translate well?
为什么这个功能在我们的环境中可能会成功或失败，即它是否会翻译得很好？
What adaptations are necessary to make this feature succeed in our context?
在我们的环境中，需要做哪些调整才能使这个特性取得成功？

In other words, we needed to use better judgment in adapting when adopting. Being more systematic in just this area would have made a big difference in what gamification mechanics we chose to pursue. And we would have probably been dissuaded from focusing on referrals altogether. I was committed to making sure our next attempts would be more methodical. We needed to be better at basing our decisions on data, insights, and foundational principles.
换句话说，我们在采用时需要运用更好的判断力。在这一领域更加系统化可能会对我们选择追求的游戏化机制产生重大影响。而且我们可能会被劝阻不再专注于推荐。我致力于确保我们下一次的尝试会更加有条不紊。我们需要更擅长基于数据、洞察力和基本原则做出决策。

Phase 3: Using data and models

Duolingo has always excelled at collecting data, especially in support of A/B testing. But there hadn’t been much effort put into using the data for insights generation. Having seen from the inside how Zynga and MyFitnessPal used data, I felt we could use Duolingo’s data to find a North Star metric and get the breakthrough we needed.
Duolingo 一直擅长收集数据，尤其是在支持 A/B 测试方面。但在利用数据生成洞察方面并没有投入太多努力。从内部看到 Zynga 和 MyFitnessPal 如何使用数据后，我觉得我们可以利用 Duolingo 的数据找到一个核心指标，并取得我们需要的突破。

My time at Zynga and MyFitnessPal gave us inspiration on how to segment and model our users by engagement level. Zynga separated their users and measured retention based on the following weekly retention metrics:
在 Zynga 和 MyFitnessPal 的经历给了我们灵感，让我们学会如何根据用户参与程度对其进行分组和建模。Zynga 将他们的用户分开，并根据以下每周留存指标来衡量留存率：

Current users retention rate (CURR): The chance a user comes back this week if they came to the product each of the past two weeks
当前用户留存率（CURR）：如果用户在过去两周每周都使用产品，那么本周再次使用产品的机会
New users retention rate (NURR): The chance a user comes back this week if they were new to the product last week
新用户留存率（NURR）：如果用户上周是产品的新用户，那么他们本周再次使用产品的机会
Reactivated user retention rates (RURR): The chance a user comes back this week if they reactivated last week
重新激活用户留存率（RURR）：如果用户上周重新激活，本周再次回来的机会

Later, when I worked at MyFitnessPal, I found that they had adopted and expanded Zynga’s retention work. They not only used CURR, NURR, and RURR to measure growth but also to model future scenarios. They also added SURR:
后来，当我在 MyFitnessPal 工作时，我发现他们采纳并扩展了 Zynga 的留存工作。他们不仅使用 CURR、NURR 和 RURR 来衡量增长，还用于模拟未来情景。他们还增加了 SURR：

Resurrected user retention rate (SURR): The chance a user comes back this week if they resurrected (from a longer absence) last week
用户复活留存率（SURR）：如果用户上周复活（从较长的缺席中复活），则本周再次回归的机会

I hypothesized that we could use these metrics at Duolingo as a starting point to create a more sophisticated model, and use that model to identify a North Star metric. Working with the data scientist and the engineer manager in the Acquisition Team, we came up with the model below. We used the same retention rates as Zynga and MyFitnessPal, but we tweaked from a weekly view to a daily view and we added several more metrics.
我假设我们可以利用这些指标作为 Duolingo 的起点，创建一个更复杂的模型，并使用该模型来确定一个核心指标。与获取团队的数据科学家和工程经理合作，我们提出了下面的模型。我们使用了 Zynga 和 MyFitnessPal 相同的留存率，但我们从每周的视角调整为每日的视角，并增加了几个指标。

The blocks, or buckets, represent different user segments with different levels of engagement. And every single user who has ever used the product is in one, and only one, bucket on any given day. That means the buckets in the model are MECE (mutually exclusive, collectively exhaustive) in representing the entire base of users who have ever used Duolingo. The arrows measure the movement of users between the buckets (these include CURR, NURR, RURR, and SURR, but evolved into daily retention rates rather than weekly). Combining the buckets and the arrows, the model creates an almost closed-circuit system, with new users being the only break.
这些区块或桶代表不同参与程度的用户群体。每一个曾经使用过产品的用户在任何一天都只属于一个桶。这意味着模型中的桶是相互排斥且完全穷尽的，代表了曾经使用过 Duolingo 的所有用户群。箭头表示用户在不同桶之间的流动（包括 CURR、NURR、RURR 和 SURR，但已演变为日留存率而非周留存率）。结合桶和箭头，该模型构建了一个几乎闭环的系统，只有新用户是唯一的突破口。

Conveniently, the top four buckets of the model add up to DAU. Those buckets are defined as:
方便地，模型的前四个桶加起来就是 DAU。这些桶被定义为：

New users: first day of engagement ever in the app
新用户：在应用中的首次参与日
Current users: engaged today and at least one other time in the prior 6 days
当前用户：今天参与，并且在过去 6 天内至少有一次参与
Reactivated users: first day of engagement after being away for 7-29 days
重新激活的用户：离开 7-29 天后的首日参与
Resurrected users: first day of engagement after being away for 30 days or longer
复活的用户：离开 30 天或更长时间后的首日参与

The remaining three buckets represent users who were not active today and have different degrees of inactivity.
剩下的三个桶代表着今天没有活跃的用户，他们有不同程度的不活跃。

At-risk WAU: inactive today, but active in at least one of the prior 6 days
WAU 风险：今天不活跃，但在过去 6 天中至少有一天活跃
- At-risk WAU + DAU = WAU
At-risk MAU: inactive in the past seven days, but active in at least one of the prior 23 days
处于风险之中的月活跃用户：在过去的七天内处于不活跃状态，但在之前的至少 23 天内至少活跃一次
- At-risk MAU + WAU = MAU
Dormant users: inactive in the past 31 days or longer
休眠用户：在过去 31 天或更长时间内处于非活跃状态
- MAU + dormant users = Total user base

The fact that DAU, WAU, and MAU can easily be calculated from these buckets made it easy to model them over time. This is a key feature of the model. Additionally, by manipulating the rates represented by the arrows, we can model the compounding and cumulative impact of moving these rates over time; in other words, the rates are the levers product teams can pull to grow DAU.
DAU、WAU 和 MAU 可以很容易地从这些桶中计算出来，这使得随着时间的推移对它们进行建模变得容易。这是该模型的一个关键特性。此外，通过操纵箭头所代表的比率，我们可以对随着时间推移移动这些比率的复利和累积影响进行建模；换句话说，这些比率是产品团队可以拉动以增长 DAU 的杠杆。

With the model created, we started taking daily snapshots of data to create a history of how all of these user buckets and retention rates had evolved on a day-by-day basis over the past several years. With this data, we could create a forward-looking model and then perform a sensitivity analysis to predict which levers would have the biggest impact on DAU growth. We ran a simulation for each rate, where we moved a single rate 2% every quarter for three years, holding all the other rates constant.
有了创建的模型，我们开始每天拍摄数据快照，以创建这些用户存储桶和留存率如何在过去几年里逐日演变的历史。有了这些数据，我们可以创建一个前瞻模型，然后进行敏感性分析，以预测哪些杠杆会对 DAU 增长产生最大影响。我们对每个比率进行了模拟，每季度将一个比率提高 2%，持续三年，保持其他所有比率不变。

Below are the results of our first simulation. It shows how those small 2% movements on each lever impacted forecasted MAU and DAU.
以下是我们第一次模拟的结果。它展示了每个杠杆上的小幅 2%移动如何影响了预测的 MAU 和 DAU。

We immediately saw that CURR had a gigantic impact on DAU—5 times the impact of the second-best metric. In hindsight, the CURR finding made sense, because the Current User bucket has an interesting characteristic: current users who stay active return to the same bucket.
我们立即发现，CURR 对 DAU 产生了巨大影响——是第二佳指标影响的 5 倍。事后看来，CURR 的发现是有道理的，因为当前用户桶具有一个有趣的特征：保持活跃的当前用户会返回到相同的桶中。

This produces a compounding effect, which means that CURR is much harder to move, but when it does, it will have a greater impact. Based on this analysis, we knew that CURR was the metric we had to move in order to get that strategic breakthrough we wanted. We decided to create a new team, the Retention Team, with CURR as its North Star metric.
这产生了一种复合效应，这意味着 CURR 要难得多才能移动，但一旦移动，它将产生更大的影响。基于这一分析，我们知道我们必须移动 CURR 才能实现我们想要的战略突破。我们决定创建一个新团队，留存团队，以 CURR 作为其北极星指标。

One of the biggest benefits of focusing on CURR was deciding not to work on things that seemed paramount before, especially new-user retention. This was a huge mindset shift for a company that had tremendous success spending years running the bulk of its growth experiments on new users first.
专注于 CURR 的最大好处之一是决定不再致力于之前似乎至关重要的事情，特别是新用户留存。对于一家多年来主要在新用户身上进行增长实验并取得巨大成功的公司来说，这是一次巨大的心态转变。

Another big lesson was seeing the massive gap between how a metric could impact DAU vs. MAU; for example, CURR’s impact on DAU was 6 times its impact on MAU. iWAURR (inactive WAU reactivation rate) was the second-best lever for moving DAU but a distant fourth for moving MAU, behind increasing new and resurrected users. This meant that, at some point, we would still need to figure out new growth vectors for new-user acquisition if we wanted to see substantial MAU improvements. But for the time being, our focus was only on moving DAU, so we prioritized CURR over all other growth levers. And it turned out to be the right choice.
另一个重要的教训是看到一个指标对 DAU 和 MAU 的影响之间存在巨大差距；例如，CURR 对 DAU 的影响是其对 MAU 影响的 6 倍。iWAURR（非活跃 WAU 重新激活率）是推动 DAU 增长的第二大杠杆，但对于推动 MAU 增长来说，排名远远落后于增加新用户和复活用户。这意味着，我们在某个时候仍需要找出新的增长途径来获取新用户，如果我们想要看到实质性的 MAU 改善。但目前，我们的重点仅在于提高 DAU，因此我们将 CURR 置于其他增长杠杆之上。结果证明这是正确的选择。

Leaderboards vector

With this clear directive, we looked at our historical model data and at our A/B tests going back a few years to see if we had inadvertently done anything that had moved CURR in the past. Surprisingly, we hadn’t. In fact, CURR had not moved in years. We had to figure out our first steps to move CURR based on first principles.
有了这个明确的指示，我们查看了我们的历史模型数据和几年来进行的 A/B 测试，以查看我们是否无意中做了任何曾经影响过 CURR 的事情。令人惊讶的是，我们并没有。事实上，多年来 CURR 都没有发生变化。我们必须根据第一原则来确定我们的第一步，以推动 CURR。

I still thought gamification was a good place to start when trying to improve retention. Our failure with the Gardenscapes-style moves counter hadn’t actually disproved any of the original reasons why we believed gamification still had upside for Duolingo—we had only learned that the moves counter was a clumsy attempt at it. This time, we would be more methodical and intelligent about features we added or borrowed. We made sure to apply the lessons from our prior efforts with gamification.
我仍然认为游戏化是在努力提高留存率时的一个好起点。我们在《花园物语》风格的移动计数器上的失败实际上并没有证明我们最初相信游戏化对 Duolingo 仍然有潜力的任何原因是错误的——我们只是得知移动计数器是一个笨拙的尝试。这一次，我们将更加有条理和智能地添加或借鉴功能。我们确保应用了我们在游戏化方面之前努力的教训。

After some consideration, we decided to bet on leaderboards. Here’s why and how. Duolingo already had a leaderboard for users to compete with their friends and family, but it wasn’t particularly effective. Based on my experience at Zynga, I felt that there was a better way. When I started working on Zynga’s FarmVille 2 game, it included a leaderboard similar to Duolingo’s existing leaderboard, where users competed with their friends. I had hypothesized based on my personal experience as a player that the closeness of the competitor’s engagement would be more important than the closeness of personal relationships. I thought this would be especially true in a mature product where many users’ friends weren’t active anymore. From our testing at Zynga, that idea turned out to be true. Based on this, I felt a leaderboard system, similar to what I had helped design at Zynga, would succeed in the context of our product.
经过一些考虑，我们决定押注排行榜。以下是原因和方式。Duolingo 已经为用户提供了一个排行榜，让他们与朋友和家人竞争，但效果并不特别好。基于我在 Zynga 的经验，我觉得有更好的方法。当我开始在 Zynga 的 FarmVille 2 游戏上工作时，它包括了一个类似于 Duolingo 现有排行榜的排行榜，用户可以与朋友竞争。我基于我作为玩家的个人经验假设，认为竞争对手的参与度比个人关系的亲近更重要。我认为在一个成熟的产品中，许多用户的朋友不再活跃，这一点尤其正确。根据我们在 Zynga 的测试，这个想法被证明是正确的。基于此，我觉得一个类似于我在 Zynga 帮助设计的排行榜系统，在我们产品的背景下会取得成功。

FarmVille 2’s leaderboard also included a “league” system. Beyond getting to the top of a weekly leaderboard, users had the opportunity to move through a series of league levels (e.g. from the Bronze league to the Silver league to the Gold league). Leagues provided users with a greater sense of progress and reward, an integral element in game design. They also increased engagement over time, since engaged users move up to more competitive leagues week after week. We felt this feature would translate well to Duolingo’s existing product because it tapped directly into the common human motivators of competitiveness and progression.
FarmVille 2 的排行榜还包括了一个“联赛”系统。除了登上每周排行榜的榜首，用户还有机会在一系列联赛级别中晋级（例如从青铜联赛晋升到白银联赛再到黄金联赛）。联赛为用户提供了更强烈的进步和奖励感，这是游戏设计中不可或缺的元素。随着用户每周晋级到更具竞争力的联赛，他们的参与度也会随之增加。我们认为这一特性能够很好地应用到 Duolingo 现有的产品中，因为它直接触及了竞争和进步这两个普遍的人类动机。

Users are matched with other users who had a similar level of engagement in the prior week. The top players at the end of this week move up to a higher league the following week.
用户将与上周参与程度相似的其他用户匹配。本周末排名靠前的玩家将在下周晋升到更高的联赛。

Not all aspects of the FarmVille 2 leaderboards would translate well to Duolingo, though. We had to use our judgment to adapt this gaming mechanic to Duolingo’s context. In FarmVille 2, competing in the leaderboard required completing additional kinds of tasks on top of the core gameplay. That was something that we purposefully left out. In the Duolingo context, more tasks would only add unnecessary complexity to language learning. We deliberately made our leaderboard as casual and frictionless as possible; users were automatically opted in and could progress to the top of the first league by merely engaging consistently in their regular language study. By keeping the game mechanic exciting, but making it simpler than in FarmVille 2, we felt like we had struck the right balance of adopting and adapting.
农场物语 2 排行榜的所有方面并不都适合转移到 Duolingo。我们不得不凭借自己的判断力，将这种游戏机制调整到 Duolingo 的背景下。在农场物语 2 中，参与排行榜需要在核心游戏玩法之外完成额外的任务。这是我们特意省略的内容。在 Duolingo 的背景下，增加更多任务只会给语言学习增加不必要的复杂性。我们刻意让我们的排行榜尽可能轻松和无障碍；用户会自动参与，并且只需在日常语言学习中保持一贯的参与就能进入第一联赛的榜首。通过保持游戏机制的刺激性，但使其比农场物语 2 简化，我们觉得我们已经找到了采纳和调整的正确平衡。

The leaderboards feature had a huge and almost immediate impact on our metrics. Overall learning time increased by 17%, and the number of highly engaged learners (users who spend at least 1 hour a day for 5 days a week) tripled. At this time, we hadn’t yet figured out how to calculate statistical significance for CURR, but we saw that our traditional retention metrics (D1, D7, etc.) improved materially and with statistical significance. Going forward, the leaderboards feature became a vector for improving metrics, and teams continue to optimize the feature to this day. Also importantly, the leaderboard was the Retention Team’s first breakthrough!
排行榜功能对我们的指标产生了巨大而几乎是立竿见影的影响。整体学习时间增加了 17%，高度参与的学习者数量（每周至少连续 5 天每天至少 1 小时学习的用户）增加了三倍。当时，我们还没有弄清楚如何计算 CURR 的统计显著性，但我们发现我们传统的留存指标（D1、D7 等）在实质上和统计上都有所改善。未来，排行榜功能成为改善指标的一个途径，团队继续优化这一功能至今。同样重要的是，排行榜是留存团队的第一个突破！

Push notifications vector

The Retention Team was completely energized to find more mechanics to keep current users engaged and motivated to practice every day. One area they started to look into was push notifications. Based on substantial A/B testing in prior years, Duolingo had established that notifications can be a big vector for growth, but that impact had plateaued for us over the years. With a re-energized team full of new ideas, we felt it was the right time to revisit this vector.
留存团队充满活力，致力于寻找更多机制，以保持当前用户的参与度和每天练习的动力。他们开始研究的一个领域是推送通知。根据之前几年的大量 A/B 测试，Duolingo 已经确定通知可以成为增长的重要途径，但多年来对我们的影响已经趋于平稳。有了充满新想法的团队，我们觉得现在是重新审视这一途径的合适时机。

As we started diving into this, there was one principle that became paramount. It came from a cautionary tale from Groupon’s CEO. He explained to Luis von Ahn, our CEO, that for a long time, Groupon stuck to one email notification per day. But their team started wondering whether sending more emails would improve metrics. The CEO eventually gave in and allowed his team to test sending one more email to each user each day. This test resulted in a big increase to their target metrics. Encouraged, Groupon kept experimenting, sending more emails, even as many as five a day. Then, in what felt like a change from one day to the next, their email channel lost most of its effectiveness. Over time, the accumulation of Groupon’s aggressive email tests had basically destroyed their channel. One often underappreciated risk with aggressively A/B testing emails and push notifications is that it results in users opting out of the channel; and even if you kill the test, those users remain opted out forever. Do this many times, and you’ve destroyed your channel. This was the outcome to avoid. For our push notifications, we established one foundational rule: protect the channel.
当我们开始深入研究时，有一个原则变得至关重要。这来自于 Groupon 的首席执行官的一个警示故事。他向我们的首席执行官 Luis von Ahn 解释说，很长一段时间，Groupon 坚持每天发送一封电子邮件通知。但他们的团队开始怀疑是否发送更多的电子邮件会改善指标。首席执行官最终屈服并允许他的团队测试每天向每个用户发送一封额外的电子邮件。这项测试导致他们的目标指标大幅增加。受此鼓舞，Groupon 继续进行实验，发送更多的电子邮件，甚至一天多达五封。然后，在一夜之间似乎发生了变化，他们的电子邮件渠道失去了大部分效力。随着时间的推移，Groupon 激进的电子邮件测试积累基本上摧毁了他们的渠道。对于激进进行 A/B 测试电子邮件和推送通知的一个常被低估的风险是，这会导致用户选择退出该渠道；即使你终止测试，这些用户也会永远选择退出。如果这样做多次，你就摧毁了你的渠道。这是要避免的结果。对于我们的推送通知，我们确立了一个基本规则：保护渠道。

With this constraint in mind, we decided to give the team a lot of freedom to optimize on dimensions like timing, templates, images, copy, localization, etc., but they could not increase the quantity of notifications without strong justification and CEO approval. Over time, through countless iterations, A/B testing, and a bandit algorithm, the team was able to generate dozens of small- and medium-size wins that have amounted to substantial gains in DAU year after year.
考虑到这一限制，我们决定给团队很大的自由度，以便在时间、模板、图片、文案、本地化等方面进行优化，但他们不能在没有充分理由和 CEO 批准的情况下增加通知的数量。随着时间的推移，通过无数次迭代、A/B 测试和赌徒算法，团队成功取得了数十次小规模和中等规模的胜利，这些胜利在 DAU 方面带来了实质性的增长。

A meme about Duolingo’s “pushiness” that went viral in 2019
2019 年走红的有关多邻国“唠叨”的网络迷因

The streak vector

In the search for even more growth vectors, the APM on the Retention Team started exploring whether there was a strong correlation between retention and usage of particular Duolingo features. He discovered that if a user reached a 10-day streak, their chances of dropping off were reduced substantially. Clearly, a lot of this was simply correlation and selection bias, but we felt the insight was interesting enough to start investing in improving this feature again.
在寻找更多增长向量的过程中，保留团队的 APM 开始探索保留和特定 Duolingo 功能使用之间是否存在强烈相关性。他发现，如果用户连续学习 10 天，他们流失的几率会大大降低。显然，这很大程度上只是相关性和选择偏差，但我们觉得这一洞察足够有趣，值得再次投资改进这一功能。

The concept of a streak is really quite simple: show users the number of consecutive days they’ve done any activity on the app. But it turns out that there is a surprisingly large number of optimization opportunities around streaks.
连续记录的概念实际上非常简单：向用户展示他们在应用上连续进行某项活动的天数。但事实证明，在连续记录周围存在着令人惊讶的大量优化机会。

We got our first big win with the streak-saver notification—a notification that alerts users with streaks if they are about to lose their streak. This late-night notification proved that indeed there was considerable upside to doubling down on streak optimizations. After this, several improvements followed: calendar views, animations, changes to streak freezes, and streak rewards, among others. Each helped improve upon the original streak idea and generated substantial improvements to retention.
我们取得了第一个重大胜利，即“连胜保护通知”——一种通知，它会在用户即将失去连胜时提醒他们。这个深夜通知证明了在连胜优化上加倍投入确实有相当大的好处。在此之后，进行了几项改进：日历视图、动画、连胜冻结的更改以及连胜奖励等。每一项改进都有助于改进原始的连胜理念，并对留存率产生了实质性的改善。

To date, the streak feature is one of Duolingo’s most powerful engagement mechanics. When people talk about their Duolingo experience, they often bring up their streak. I recently met one user who told me, “I have a 1,435-day streak!” and added, “with no streak freezes!” His bragging rights were well-earned, as he had been studying his chosen language daily for almost four years.
迄今为止，连胜功能是 Duolingo 最强大的参与机制之一。当人们谈论他们的 Duolingo 体验时，他们经常提到他们的连胜。我最近遇到一个用户告诉我，“我已经连续学习了 1,435 天！”并补充说，“没有任何连胜冻结！”他的自夸是实至名归的，因为他几乎已经连续四年每天学习他选择的语言。

Streaks work for a number of reasons. One of those is that a streak increases user motivation over time; the longer the streak is, the greater the impetus to keep the streak going. When it comes to user retention, this is the exact behavior we want in our users. Each day that a learner comes to Duolingo, they care a bit more about coming back the next day than they did the day before, hence increasing retention and DAU. As a meta-lesson, our success with the streak mechanic further showed us that we could squeeze major wins from existing features. We could see the value in both big breakthroughs and in fast optimizations. And an A+ team often has a mix of both.
连胜的原因有很多。其中之一是连胜会随着时间增加用户的动力；连胜越长，保持连胜的动力就越大。在用户留存方面，这正是我们希望用户表现出的行为。每天学习者来到 Duolingo，他们对第二天再次回来的渴望会比前一天更强烈，从而增加留存和 DAU。作为元教训，我们在连胜机制上的成功进一步表明我们可以从现有功能中获得重大收益。我们看到了大突破和快速优化的价值。而一流的团队往往两者兼具。

Growth beyond CURR

We didn’t stop at CURR; there was a very healthy paranoia that at some point CURR would hit a ceiling, so sooner or later we would have to figure out growth vectors for new user acquisition. The Retention Team stayed focused on increasing CURR, but as a company, we consistently increased our investment in growth by creating more and more Product and Marketing teams to find new vectors (for both retention and acquisition). Luckily, several of these bets worked, including expanding internationally, building social features (this is what the Acquisition eventually team pivoted to, with great success), accelerating course content creation, working with influencers, increasing our presence in schools, investing (a little bit) in paid UA, and going crazy viral on TikTok. Each of these merits its own case study.
我们并没有止步于 CURR; 我们非常健康地担心，CURR 迟早会达到一个上限，所以迟早我们将不得不找出新用户获取增长向量。保留团队专注于增加 CURR，但作为公司，我们不断增加了对增长的投资，通过创建越来越多的产品和营销团队来寻找新的向量（用于保留和获取）。幸运的是，其中几个赌注成功了，包括国际扩张，构建社交功能（这最终是获取团队转变的方向，并取得了巨大成功），加速课程内容创作，与意见领袖合作，在学校增加我们的存在，投资（少量）付费用户获取，并在 TikTok 上疯狂传播。每个都值得进行单独的案例研究。

Overall results

Through our efforts over four years, we were able to increase CURR by 21%, which represents a reduction in the daily churn of our best users by over 40% and, together with our other successful bets, led to an increase in our DAU of 4.5x. Last year was one of the fastest growth rates in Duolingo’s history. The quality of the user base also improved; the share of our DAU with a streak of 7 days or longer increased almost 3 times to more than half of our DAU. This means that not only does Duolingo have a much higher number of active users now, but also that those users are much more likely to keep coming back, refer their friends, and subscribe to Super Duolingo. This growth was key to Duolingo’s successful IPO.
通过我们四年来的努力，我们成功将 CURR 提高了 21％，这意味着我们最佳用户的日均流失率减少了 40％以上，并且与我们的其他成功举措一起，导致我们的 DAU 增长了 4.5 倍。去年是 Duolingo 历史上增长速度最快的一年。用户群体的质量也得到了改善；拥有连续 7 天或更长连续学习记录的 DAU 所占比例增加了近 3 倍，达到了 DAU 总数的一半以上。这意味着 Duolingo 不仅拥有更多活跃用户，而且这些用户更有可能持续使用、推荐给朋友，并订阅 Super Duolingo。这种增长对 Duolingo 成功上市起到了关键作用。

Parting thoughts

I hope that this article gives you the inspiration you need to find new vectors of growth for your product. If you adopt anything from my experience at Duolingo, I hope you adapt it to your own context using your best judgment. Don’t blindly trust what Duolingo or any other company did. Certainly that didn’t work for me. Happy experimenting!

Acknowledgements

Gamification Team: You know who you are. Thank you for teaching me so much!

Acquisition Team: Vanessa Jameson (Engineer Director), Cem Kansu and Liz Nagler (PMs on the team, now VP of Product and Product Area Lead for Growth, respectively), and the rest of the team, who worked super-hard and eventually made a smart and successful pivot to work on social features. Shoutout to Nico Sacheri (Principal PM) and Hideki Shima (Eng Director), who have been crushing it leading the Connections team for the past couple of years.

Growth Model: Erin Gustafson (Staff Data Scientist) and Vanessa Jameson, who collaborated with me in the creation of the growth model. Learn more about how Erin is working to evolve the way Duolingo thinks about growth in her recent post: https://blog.duolingo.com/growth-model-duolingo/

Retention Team: Sean Colombo (OG Engineer Manager for the team, and now Eng Area Lead for Growth), Daniel Falabella (OG PM for the team, now GM for Duolingo ABC), John Trivelli (Designer on leaderboards), Anton Yu (PM who “re-discovered” streaks and so much more), Jackson Shuttleworth and Osman Mansur (Sr. PM and PM on the team today, still crushing it), Antonia Scheidel (Engineering Manager, also crushing it), and all the wonderful engineers and designers who have worked and continue to work on this team.

Gina Gotthilf, who was a total growth rock star in Duolingo’s early years.

Luis von Ahn (CEO) and Tyler Murphy (Chief Designer), with whom I reviewed every single product change for almost five years.

Thank you, Jorge! You can follow Jorge for more on LinkedIn and Twitter.

Have a fulfilling and productive week 🙏

📣 Join Lenny’s Talent Collective 📣

If you’re hiring, join Lenny’s Talent Collective to start getting bi-monthly drops of world-class hand-curated product and growth people who are open to new opportunities.

If you’re looking for a new gig, join the collective to get personalized opportunities from hand-selected companies. You can join publicly or anonymously, and leave anytime.

Apply to join