But how can you apply more personalization to your product to improve engagement<\/a>?<\/p>\n Well, let\u2019s explore product personalization examples from these brands:<\/p>\n Hate those onboarding flows that bombard you with endless questions? Miro gets it. They’ve nailed the art of personalization without the interrogation.<\/p>\n From the moment you sign up, Miro is all about you<\/em>. They ask a few key questions about your role, goals, and projects, then use that info to create an onboarding experience<\/a> that’s actually helpful.<\/p>\n Here’s how they do it<\/strong>:<\/p>\n The best part? It’s all super smooth and frictionless. No endless forms or tedious questionnaires. Just a few quick questions, and boom\u2014you’re off to the races with a personalized Miro experience.<\/p>\n To implement this process, Miro leverages customer data collected through its onboarding survey<\/a>, which asks users key questions about their goals and preferences. Based on responses, Miro\u2019s system identifies the most relevant features and presents tailored recommendations.<\/p>\n \ud83d\udca1Pro tip<\/strong>: You can implement this process with no-code tools like Userpilot. It allows product teams to create onboarding experiences<\/a> with minimal development effort.<\/p>\n Sure, Spotify knows what you like. But what’s really<\/em> cool is how they use AI to introduce you to new music you’ll probably love (with the occasional miss, of course).<\/p>\n Here’s the lowdown on Spotify’s AI personalization:<\/p>\n The result? A personalized listening experience that feels fresh and exciting, not repetitive and stale. And even if the AI gets it wrong sometimes (hey, nobody’s perfect!), it learns from your feedback and adjusts its recommendations over time.<\/p>\n Spotify\u2019s recommendation system leverages data such as listening history, song skips, saved songs, and listening trends to select recommendations.<\/p>\n It also combines collaborative filtering (learning from similar users\u2019 preferences) and content-based filtering (analyzing song metadata), making recommendations more precise. Spotify\u2019s AI model also improves over time as it learns from user interactions<\/a>, continuously refining its recommendations.<\/p>\n Let’s be honest, we’ve all fallen victim to YouTube’s addictive recommendation algorithm.<\/p>\n But how does YouTube know what you want to watch before you<\/em> even know?<\/p>\n It all comes down to their super-smart algorithm. This thing is constantly learning about your content preferences and analyzing your every click, like, and comment.<\/p>\n Here’s how it works:<\/p>\n Personalized content is the name of the game for platforms like YouTube. It\u2019s what allowed it to be widely used, so it\u2019s no surprise that it sets the standard for how a content recommendation system should work.<\/p>\n Now, YouTube\u2019s recommendation system isn\u2019t simple, it has multiple layers in it.<\/p>\n First, it follows a content-based filtering approach<\/strong>, which involves analyzing the category of videos and matching them with a user\u2019s viewing history. The algorithm then learns from behavioral data<\/a>, such as likes, shares, and comments, to refine its understanding of each user’s preferences.<\/p>\n Then, there\u2019s also collaborative filtering<\/strong>, where YouTube\u2019s algorithm puts your profile into one or multiple buckets, and recommends content that people with similar profiles also engage with, even when it\u2019s not related. For instance, if you consume cat grooming videos, the algorithm might somehow recommend easy home-cooking recipes.<\/p>\n Let’s face it, booking travel online can be a pain. Endless scrolling, confusing filters, and the constant fear of missing out on a better deal.<\/p>\n But Agoda, the popular travel booking platform, is doing things differently. They’re using a simple yet powerful tactic to personalize your experience: location, location, location.<\/strong><\/p>\n Here’s the deal:<\/p>\n It is also one of the few travel platforms that, unlike apps like Airbnb, leverage any sort of personalization\u2014and it works!<\/p>\n Agoda\u2019s recommendation engine relies on user signals, which are data points gathered from user interactions and properties.<\/p>\n These signals are analyzed by the system to create a profile of each user\u2019s preferences and locations. And then segment users <\/a>into categories to make tailored recommendations through in-app messaging<\/a>.<\/p>\n Plus, this approach might also involve real-time data processing. This is because it would allow Agoda to adjust its recommendations as users interact with the platform.<\/p>\n But although it seems simple, not all travel platforms follow this approach. For instance, Airbnb lacks the same level of personalized experience, which is a great missed opportunity to create more business from users.<\/p>\n What does Airbnb know about you?<\/p>\n \u231b\ufe0f the history of all your trips<\/p>\n \ud83d\udd2e all your searches (dreams!)<\/p>\n \ud83d\udc6a # of people you travel with<\/p>\n \ud83d\udcb0 how much do you pay on average<\/p>\n \ud83c\udfe0 what type of properties do you book<\/p>\n \ud83d\udccd all your IP locations<\/p>\n What a missed opportunity for personalization! pic.twitter.com\/inmgwFahli<\/a><\/p>\n \u2014 Peter Fabor (@faborio) May 11, 2022<\/a><\/p><\/blockquote>\n<\/div>\n<\/figure>\n Netflix is a standard for personalized content recommendations in the streaming industry, and for a reason.<\/p>\n Here\u2019s how your Netflix experience is influenced by personalization:<\/p>\n On Netflix, every user\u2019s homepage looks unique. It doesn\u2019t share the same depth as YouTube, sure, but it doesn\u2019t matter because their goals are fundamentally different.<\/p>\n Netflix wants you to binge-watch shows, while YouTube only needs you to stay on the platform for the ad space.<\/p>\n Netflix\u2019s recommendation engine has many levels of personalization.<\/p>\n The first is user signals, which leverage user actions <\/a>and location data to determine the best recommendations (think of viewing duration, feedback, and active hours). This is also why your Netflix catalog might change drastically when you travel to another country.<\/p>\n There\u2019s also content-based filtering, which, like YouTube, categorizes the content and aligns it with your watching history to learn your most common preferences.<\/p>\n Finally, there is collaborative filtering. It creates clusters of users with similar viewing patterns and recommends content that others in the same cluster have enjoyed.<\/p>\n With all of these, Netflix can predict what each user is likely to enjoy.<\/p>\n Duolingo\u2019s gamified experience is the reason why it became one of the most addictive and effective language-learning apps on the market.<\/p>\n Its goal is to transform language learning into a fun and dynamic journey, making it accessible and engaging for users of all ages.<\/p>\n Here are some of the key gamification elements <\/a>Duolingo uses:<\/p>\n Although Duolingo is definitely a masterclass in gamification, it doesn\u2019t mean it\u2019s particularly strong at personalization. Every user follows more or less the same path (with few practices you can do on the side), and the only personalized bits don\u2019t even change the content of the exercises.<\/p>\n Duolingo\u2019s gamification engine is based on a mix of standard game mechanics and personalization algorithms.<\/p>\n First, the structure of quests and challenges remains consistent for all users, but the specific requirements for quests and badges are tailored based on the user\u2019s learning streak, language course, and overall progress. This means that beginners may have simpler daily tasks, while advanced users face more complex challenges.<\/p>\n Plus, the quests are also different, it can go from doing a listening exercise to completing a hard challenge.<\/p>\n This results in a balanced experience that keeps users engaged without overwhelming them.<\/p>\n Ever wonder how Amazon seems to know exactly what you want before you<\/em> do? You browse for a new coffee maker, and suddenly, you’re bombarded with recommendations for filters, mugs, and artisanal coffee beans.<\/p>\n That’s Amazon’s personalization engine at work, and here\u2019s how they do it:<\/p>\n Amazon\u2019s goal is to make it easier for users to discover products they\u2019re likely to purchase\u2014improving both the user experience and Amazon\u2019s online store conversion rates<\/a>.<\/p>\n So considering the vast amount of data they have, the scale of Amazon\u2019s personalization is definitely a feat not every company can accomplish.<\/p>\n Amazon\u2019s recommendation engine relies on a combination of collaborative filtering and content-based filtering techniques.<\/p>\n Collaborative filtering identifies patterns from similar users to suggest products that others in the same user group have found valuable. Meanwhile, content-based filtering analyzes product attributes and aligns them with a user\u2019s individual preferences, making recommendations even more accurate.<\/p>\n As a result, Amazon created a personalization engine that adapts to user preferences and provides a shopping journey that becomes increasingly relevant with each interaction\u2014enhancing customer satisfaction<\/a> and driving more sales.<\/p>\n Grammarly keeps its users engaged and motivated through personalized email notifications.<\/p>\n With behavioral data, Grammarly crafts weekly email updates that provide insights into the user\u2019s writing habits, productivity, and language mastery. And include:<\/p>\n This personalization approach works wonders because it serves two purposes: To reinforce positive behaviors (encouraging users to write more and improve their skills), and to keep Grammarly top-of-mind.<\/p>\n As a result, each user feels like they are on a journey of continuous improvement (rather than being sold to).<\/p>\n Grammarly\u2019s personalized email marketing campaigns are powered by the data collected from user interactions within the platform.<\/p>\n It tracks metrics like word count, alert frequency, and vocabulary diversity to automatically generate personalized reports. These emails are then sent to the user to motivate the user to keep engaging with the platform.<\/p>\n These reports also serve as an upselling opportunity<\/a> for Grammarly premium. It shows the number of suggestions and advanced grammar mistakes that the user might have solved with premium. As a result, it compels users to not only upgrade but to improve their writing.<\/p>\n Strava, a popular app for runners and cyclists, takes personalization to a new level by offering tailored route recommendations based on the customer\u2019s location, activity preferences, and previous workouts.<\/p>\n This feature allows users to:<\/p>\n Strava\u2019s personalized recommendations work great because it saves time while offering new challenges, making it a great fit for users who enjoy exploring but prefer not to plan their routes manually. This way, Strava not only promotes consistent workouts but also enhances user satisfaction<\/a> by making training sessions feel fresh and exciting.<\/p>\n Strava\u2019s recommendation engine relies on a combination of user signals and machine learning algorithms.<\/p>\n The app tracks data from user activities\u2014such as distance run, pace, and preferred routes\u2014to build a profile of each user\u2019s fitness level and preferences. Strava then applies machine learning models to suggest routes that align with a user\u2019s previous activities and the popular routes among other users in the same area.<\/p>\n Additionally, Strava also incorporates geographic data and community-based insights, creating a network of user-approved routes. The system continues to refine its recommendations as users interact with different routes, ensuring that each suggestion is relevant and suited to the user\u2019s current fitness goals.<\/p>\n As a result, Strava\u2019s app has become known for being a platform that makes exercise engaging and fulfilling.<\/p>\n As we learned from these product personalization examples, good personalization not only improves satisfaction but also fosters long-term customer loyalty.<\/p>\n That said, implementing similar strategies in your product can help you deliver a relevant, user-centered experience that provides the same results, too.<\/p>\n Want to learn how Userpilot can personalize the product experience based on user behavior? Book a demo<\/a> to see how you can get a more sophisticated personalization process without coding skills.<\/p>\n","protected":false},"excerpt":{"rendered":"\n
#1 Miro’s personalized onboarding experience<\/strong><\/h2>\n
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![\"product](\"https:\/\/userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/miro-flow-example_c5722858960f1b82f0c6b5abc3bfeef5.gif)
<\/picture>How does it work?<\/strong><\/h3>\n
![\"product](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/welcome-survey_79c2d1e65a6ed862b6479cfbc26a049c_800.png)
<\/picture>#2 Spotify’s AI personalized recommendations<\/strong><\/h2>\n
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![\"product](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/spotify_ce78b881d03a32b7892db4ab20a1aa27_800.png)
<\/picture>How does it work?<\/strong><\/h3>\n
#3 YouTube’s content-based personalized experiences<\/strong><\/h2>\n
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![\"product](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/youtube-1_113b98713ab7c8aa9e297ff65623797c_800.png)
<\/picture><\/div>\n![\"product](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/youtube-2_113b98713ab7c8aa9e297ff65623797c_800.png)
<\/picture><\/div>\n<\/div>\nHow does it work?<\/strong><\/h3>\n
#4 Agoda’s user signal recommendations<\/strong><\/h2>\n
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![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/agoda-1_0534a469ac271543994197824ec85df9_800.png)
<\/picture><\/div>\n![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/agoda-2_0534a469ac271543994197824ec85df9_800.png)
<\/picture><\/div>\n<\/div>\nHow does it work?<\/strong><\/h3>\n
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#5 Netflix’s collaborative filtering for personalized content<\/strong><\/h2>\n
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![\"product](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/netflix_16b7dac2ebc3239e06a90f5353bf4b42_800.png)
<\/picture>How does it work?<\/strong><\/h3>\n
#6 Duolingo’s personalized gamification<\/strong><\/h2>\n
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![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/duolingo-1_5ea44a5944075a6d767a276a81da9671_800.png)
<\/picture><\/div>\n![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/duolingo-3_5ea44a5944075a6d767a276a81da9671_800.png)
<\/picture><\/div>\n![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/duolingo-2_5ea44a5944075a6d767a276a81da9671_800.png)
<\/picture><\/div>\n<\/div>\nHow does it work?<\/strong><\/h3>\n
#7 Amazon’s customized and personalized products<\/strong><\/h2>\n
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![\"Amazon\u2019s](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/amazon_8ea55d826972bc7567767e392694af33_800.png)
<\/picture>How does it work?<\/strong><\/h3>\n
#8 Grammarly’s personalized push notifications via email<\/strong><\/h2>\n
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![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/grammarly-1_b305f2bb669e7f41426ac4614fe12d44_800.png)
<\/picture><\/div>\n![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/grammarly-2_b305f2bb669e7f41426ac4614fe12d44_800.png)
<\/picture><\/div>\n<\/div>\nHow does it work?<\/strong><\/h3>\n
#9 Strava’s personalized route recommendations<\/strong><\/h2>\n
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![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/strava-1_d35517d42e3cb23a66ab457fb7f651a3_800.png)
<\/picture><\/div>\n![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/strava-2_d35517d42e3cb23a66ab457fb7f651a3_800.png)
<\/picture><\/div>\n![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/strava-3_d35517d42e3cb23a66ab457fb7f651a3_800.png)
<\/picture><\/div>\n![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/strava-4_d35517d42e3cb23a66ab457fb7f651a3_800.png)
<\/picture><\/div>\n<\/div>\nHow does it work?<\/strong><\/h3>\n
Conclusion<\/strong><\/h2>\n