9 Product Personalization Examples for Better UX
If you’re here for product personalization examples, then you already know personalization is not just nice-to-have.
And you’re not a minority, Twilio’s state of personalization report says 89% of leaders think personalization is required for business success.
But how can you apply more personalization to your product to improve engagement?
Well, let’s explore product personalization examples from these brands:
- Miro
- Spotify
- YouTube
- Agoda
- Netflix
- Duolingo
- Amazon
- Grammarly
- Strava
#1 Miro’s personalized onboarding experience
Hate those onboarding flows that bombard you with endless questions? Miro gets it. They’ve nailed the art of personalization without the interrogation.
From the moment you sign up, Miro is all about you. They ask a few key questions about your role, goals, and projects, then use that info to create an onboarding experience that’s actually helpful.
Here’s how they do it:
- Tailored templates and workflows: No more sifting through irrelevant options. Miro recommends the stuff you’ll use.
- Personalized tutorials: Whether you’re using a mouse or trackpad, Miro shows you exactly how to use their whiteboards, with interactive visuals.
The best part? It’s all super smooth and frictionless. No endless forms or tedious questionnaires. Just a few quick questions, and boom—you’re off to the races with a personalized Miro experience.
How does it work?
To implement this process, Miro leverages customer data collected through its onboarding survey, which asks users key questions about their goals and preferences. Based on responses, Miro’s system identifies the most relevant features and presents tailored recommendations.
💡Pro tip: You can implement this process with no-code tools like Userpilot. It allows product teams to create onboarding experiences with minimal development effort.
#2 Spotify’s AI personalized recommendations
Sure, Spotify knows what you like. But what’s really cool is how they use AI to introduce you to new music you’ll probably love (with the occasional miss, of course).
Here’s the lowdown on Spotify’s AI personalization:
- AI-powered playlists: “Discover Weekly” and “Daily Mixes” aren’t just random songs thrown together. Spotify’s algorithm analyzes your listening habits, favorite tracks, and go-to genres to create curated playlists that feel like they were made just for you.
- AI playlist generator: Want a playlist for your next workout or road trip? Just give Spotify a prompt, and their AI will whip one up in seconds.
- AI DJ: This is where things get interesting. Spotify’s AI DJ plays your favorites, old classics you haven’t heard in a while, and even throws in some new tunes that might just become your next obsession.
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.
How does it work?
Spotify’s recommendation system leverages data such as listening history, song skips, saved songs, and listening trends to select recommendations.
It also combines collaborative filtering (learning from similar users’ preferences) and content-based filtering (analyzing song metadata), making recommendations more precise. Spotify’s AI model also improves over time as it learns from user interactions, continuously refining its recommendations.
#3 YouTube’s content-based personalized experiences
Let’s be honest, we’ve all fallen victim to YouTube’s addictive recommendation algorithm.
But how does YouTube know what you want to watch before you even know?
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.
Here’s how it works:
- Personalized homepage: Your YouTube homepage isn’t random. It’s a carefully curated collection of videos based on your watch history, search queries, and interactions. Love cooking shows? Expect to see Gordon Ramsay staring back at you.
- Exploring your interests: YouTube doesn’t just stick to what you already know. It throws in videos from different topics to see if you bite. Maybe you’re secretly a fan of Korean dramas or DIY tutorials.
- Topic filters: Once YouTube has a good grasp of your tastes, it lets you filter recommendations by topic. So, if you’re in the mood for fashion, just hit the “Skirts” button and voilà—a whole world of stylish content awaits.
Personalized content is the name of the game for platforms like YouTube. It’s what allowed it to be widely used, so it’s no surprise that it sets the standard for how a content recommendation system should work.
How does it work?
Now, YouTube’s recommendation system isn’t simple, it has multiple layers in it.
First, it follows a content-based filtering approach, which involves analyzing the category of videos and matching them with a user’s viewing history. The algorithm then learns from behavioral data, such as likes, shares, and comments, to refine its understanding of each user’s preferences.
Then, there’s also collaborative filtering, where YouTube’s algorithm puts your profile into one or multiple buckets, and recommends content that people with similar profiles also engage with, even when it’s not related. For instance, if you consume cat grooming videos, the algorithm might somehow recommend easy home-cooking recipes.
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#4 Agoda’s user signal recommendations
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.
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.
Here’s the deal:
- Near me, now: Agoda analyzes your location to show you accommodations and destinations that are close to home. No more sifting through irrelevant options halfway across the world.
- Always-on recommendations: Even when you’re not actively searching, Agoda keeps those location-based suggestions coming.
It is also one of the few travel platforms that, unlike apps like Airbnb, leverage any sort of personalization—and it works!
How does it work?
Agoda’s recommendation engine relies on user signals, which are data points gathered from user interactions and properties.
These signals are analyzed by the system to create a profile of each user’s preferences and locations. And then segment users into categories to make tailored recommendations through in-app messaging.
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.
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.
#5 Netflix’s collaborative filtering for personalized content
Netflix is a standard for personalized content recommendations in the streaming industry, and for a reason.
Here’s how your Netflix experience is influenced by personalization:
- Your personal watchlist whisperer: Netflix analyzes your watch history, ratings, and genre preferences to create a recommendation system that’s tailored to you. Start bingeing sci-fi shows? Expect to see more spaceships and aliens on your homepage.
- The “people like you” effect: Netflix also looks at the viewing habits of similar users to suggest shows and movies you might enjoy. It’s like having a friend with impeccable taste curating your watchlist.
On Netflix, every user’s homepage looks unique. It doesn’t share the same depth as YouTube, sure, but it doesn’t matter because their goals are fundamentally different.
Netflix wants you to binge-watch shows, while YouTube only needs you to stay on the platform for the ad space.
How does it work?
Netflix’s recommendation engine has many levels of personalization.
The first is user signals, which leverage user actions 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.
There’s also content-based filtering, which, like YouTube, categorizes the content and aligns it with your watching history to learn your most common preferences.
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.
With all of these, Netflix can predict what each user is likely to enjoy.
#6 Duolingo’s personalized gamification
Duolingo’s gamified experience is the reason why it became one of the most addictive and effective language-learning apps on the market.
Its goal is to transform language learning into a fun and dynamic journey, making it accessible and engaging for users of all ages.
Here are some of the key gamification elements Duolingo uses:
- Daily quests: Encourage daily practice by rewarding users for completing specific tasks each day.
- Friend quests: Allow users to collaborate or compete with friends, adding a social layer to learning.
- Monthly challenges: Provide a series of challenges based on user streaks and progress, offering extra rewards for sustained learning.
- Streak count and rewards: Track consecutive days of practice, rewarding users for maintaining consistency.
- XP and leaderboards: Motivate users by allowing them to earn points and compete in leaderboards, enhancing their sense of progress.
- Personalized elements based on each user’s progress: Users with longer streaks are often given more challenging quests in monthly challenges. Also, daily and friend quests are customized according to the user’s language course and level, making the experience feel a bit unique for each learner.
Although Duolingo is definitely a masterclass in gamification, it doesn’t mean it’s 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’t even change the content of the exercises.
How does it work?
Duolingo’s gamification engine is based on a mix of standard game mechanics and personalization algorithms.
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’s learning streak, language course, and overall progress. This means that beginners may have simpler daily tasks, while advanced users face more complex challenges.
Plus, the quests are also different, it can go from doing a listening exercise to completing a hard challenge.
This results in a balanced experience that keeps users engaged without overwhelming them.
#7 Amazon’s customized and personalized products
Ever wonder how Amazon seems to know exactly what you want before you do? You browse for a new coffee maker, and suddenly, you’re bombarded with recommendations for filters, mugs, and artisanal coffee beans.
That’s Amazon’s personalization engine at work, and here’s how they do it:
- It curates product suggestions tailored to each shopper’s preferences based on search history, past purchases, and browsing patterns.
- Shows products that other customers also view while navigating the same page.
- Updates recommendations based on real-time user interactions. You can see them across various sections of the site, from the homepage to individual product pages.
Amazon’s goal is to make it easier for users to discover products they’re likely to purchase—improving both the user experience and Amazon’s online store conversion rates.
So considering the vast amount of data they have, the scale of Amazon’s personalization is definitely a feat not every company can accomplish.
How does it work?
Amazon’s recommendation engine relies on a combination of collaborative filtering and content-based filtering techniques.
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’s individual preferences, making recommendations even more accurate.
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—enhancing customer satisfaction and driving more sales.
#8 Grammarly’s personalized push notifications via email
Grammarly keeps its users engaged and motivated through personalized email notifications.
With behavioral data, Grammarly crafts weekly email updates that provide insights into the user’s writing habits, productivity, and language mastery. And include:
- Personalized stats such as the number of words checked, writing mistakes, and vocabulary usage compared to other users.
- The most common tones that were detected in your writing.
- The total number of words Grammarly has detected since the beginning.
- Most common mistakes, and the advanced suggestions you could get with Grammarly Pro.
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.
As a result, each user feels like they are on a journey of continuous improvement (rather than being sold to).
How does it work?
Grammarly’s personalized email marketing campaigns are powered by the data collected from user interactions within the platform.
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.
These reports also serve as an upselling opportunity 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.
#9 Strava’s personalized route recommendations
Strava, a popular app for runners and cyclists, takes personalization to a new level by offering tailored route recommendations based on the customer’s location, activity preferences, and previous workouts.
This feature allows users to:
- Explore new routes in their area that align with their training needs, making their exercise routine more enjoyable and varied.
- Get recommendations based on route difficulty, distance, elevation, and popularity within the Strava community.
Strava’s 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 by making training sessions feel fresh and exciting.
How does it work?
Strava’s recommendation engine relies on a combination of user signals and machine learning algorithms.
The app tracks data from user activities—such as distance run, pace, and preferred routes—to build a profile of each user’s fitness level and preferences. Strava then applies machine learning models to suggest routes that align with a user’s previous activities and the popular routes among other users in the same area.
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’s current fitness goals.
As a result, Strava’s app has become known for being a platform that makes exercise engaging and fulfilling.
Conclusion
As we learned from these product personalization examples, good personalization not only improves satisfaction but also fosters long-term customer loyalty.
That said, implementing similar strategies in your product can help you deliver a relevant, user-centered experience that provides the same results, too.
Want to learn how Userpilot can personalize the product experience based on user behavior? Book a demo to see how you can get a more sophisticated personalization process without coding skills.
Product personalization FAQs
What is product customization vs personalization?
Product customization allows users to actively modify and adjust a product to their preferences, such as changing color schemes or adding specific features. On the other hand, personalization uses data-driven insights to automatically adapt the product experience to each user without manual input.
In a nutshell, while customized products offer users more control, personalization creates an experience that feels more seamless and intuitive.
Why is product customization important?
Product personalization makes the product experience feel relevant, convenient, and aligned with individual preferences. Plus, personalized experiences are shown to increase user engagement, brand loyalty, and retention. As they save time for users and provide content or features that match their specific needs.
What are the 5 levels of product personalization?
The five levels of product personalization represent a progression from basic user segmentation to advanced, predictive technique.
Each level builds on the previous to deliver more accurate and relevant experiences. They include:
- Creative direction: Work closely with your copywriters and designers to create the most relevant content for your different types of users.
- User signals: Applies specific signals to deliver personalized experiences. For instance, using location-based or time-specific rules to tailor what users see on your website.
- Content-based filtering: The system triggers personalized content based on the user’s engagement with specific content “clusters”. These can be product categories, music genres, JTBDs, etc.
- Collaborative filtering: At this stage, personalization is driven by the behavior of similar users, matching individual preferences with patterns observed among other users with similar interests. This method is popular in recommendation systems like Netflix and Amazon, where users are shown content that others in their “cluster” have enjoyed.
- Predictive Personalization: Uses machine learning algorithms to predict specific changes in users’ preferences based on context or time. For instance, if the algorithm notices you tend to watch different types of movies during summer, it might try to recommend different movies to learn about what triggers engagement from you.
How to personalize your product?
To personalize a product effectively, consider leveraging the following techniques throughout the entire customer journey:
- UI writing: Use adaptable language that resonates with different user personas.
- User signals: Analyze behavioral and geographic data to segment users and deliver relevant content.
- Filtering: Use content-based or collaborative filtering to provide relevant recommendations.
- Machine Learning (ML) and AI: Employ predictive models that adapt experiences in real-time, refining personalization as users interact with the product.
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