How Do You Get Started With AI Personalisation for Apps?

When was the last time you opened an app and felt like it truly understood what you needed? Not just the basic stuff like remembering your login details, but actually knowing what content you'd want to see, which features you'd use most, or even predicting what you might be looking for before you've typed a single word? That's AI personalisation in action—and honestly, it's becoming less of a nice-to-have and more of a must-have for mobile apps that want to survive.

I've been building mobile apps for over eight years now, and I can tell you that the landscape has changed completely. Users aren't just comparing your app to your direct competitors anymore; they're comparing it to every single app experience they have. When someone uses Netflix and gets spot-on movie recommendations, or opens Spotify to find a perfectly curated playlist waiting for them, that sets the bar for what they expect from your fitness app, your shopping app, or your productivity tool.

The most successful apps don't just respond to user behaviour—they anticipate it, creating experiences that feel almost magical in their relevance and timing.

But here's the thing that trips up most app owners I speak to: they think AI personalisation is this massive, complex beast that requires a team of data scientists and a budget the size of Google's. Sure, the advanced stuff can get pretty involved, but getting started? That's actually much simpler than you might think. You don't need machine learning PhDs on your payroll or servers that cost thousands per month. You just need to understand your users, start collecting the right data, and take it one step at a time.

Understanding AI Personalisation Basics

Right, let's get straight to it—AI personalisation isn't as scary as it sounds. Basically, it's about making your app smart enough to show different things to different people based on what they like, how they behave, and what they need. Think of it like a shopkeeper who remembers your preferences and suggests things you'll actually want to buy.

The magic happens when your app starts learning from user interactions. Every tap, swipe, and pause tells a story about what someone finds interesting or useful. I mean, if someone always skips past fitness content but spends ages reading recipe articles, that's valuable information your app can use to show them more cooking-related stuff next time.

The Three Core Components

There are three main bits you need to understand. First, data collection—your app needs to gather information about what users do (but don't worry, we'll cover privacy properly later). Second, pattern recognition—this is where AI algorithms spot trends in user behaviour that humans would miss. Third, content delivery—showing the right content to the right person at the right moment.

You know what's brilliant? You don't need a computer science degree to get started. Modern AI tools handle most of the complex stuff for you; they just need you to feed them the right data and tell them what outcomes you want. Its like having a really clever assistant who never gets tired of analysing user behaviour.

The key thing to remember is that personalisation should feel natural to users. They shouldn't notice the AI working—they should just think "wow, this app really gets me." When you nail that feeling, user engagement goes through the roof.

Why Your App Needs Personalisation

Here's the brutal truth about mobile apps today—users are spoiled for choice, and they know it. I've watched apps with millions in funding crash and burn because they treated every user exactly the same way. Meanwhile, smaller apps with smart personalisation features keep growing steadily because they make each user feel understood.

Think about your own phone for a second. How many apps do you actually use regularly? Probably around 9 or 10, right? The rest just sit there taking up space until you finally delete them. The apps that survive on your home screen are the ones that get better the more you use them—Netflix knows what you want to watch, Spotify creates playlists you actually like, and your banking app remembers your preferences.

But here's what really gets me excited about AI personalisation—it's not just about keeping users happy (though that's obviously important). It directly impacts your bottom line in ways that might surprise you. Apps with personalised experiences see retention rates improve by 20-30% on average; users spend more time in the app, they're more likely to make purchases, and—this is the big one—they actually recommend the app to friends.

Start tracking user behaviour from day one, even if you're not ready to implement AI personalisation yet. You'll need this data later, and it takes time to build a meaningful dataset.

The shift towards personalisation isn't just a nice-to-have feature anymore. Users have come to expect it. When they don't get it, they notice—and they leave. Generic experiences feel outdated and frankly a bit lazy to modern users who've been trained by the likes of Amazon and Google to expect apps that adapt to their needs.

What Personalisation Actually Means for Your App

Personalisation goes way beyond just showing someone's name at the top of the screen. It's about creating an app experience that evolves based on how each person actually uses it. This could mean different content recommendations, customised interface layouts, or even adjusting the app's functionality based on user behaviour patterns.

• Content that matches user interests and past behaviour
• Interface elements positioned based on usage patterns
• Notifications sent at optimal times for each user
• Features and options prioritised by individual preferences
• Onboarding flows adapted to user experience levels

The beauty of AI personalisation is that it happens automatically—your app gets smarter about each user without them having to manually configure settings or fill out lengthy preference forms. It learns from their actions, not their stated preferences, which tends to be much more accurate anyway.

Right, so you want to add AI personalisation to your app but you're wondering where on earth to start with collecting user data? I get it—data collection can feel a bit overwhelming at first, but honestly, it's not as complicated as people make it out to be.

The key thing to remember is this: start small and build up gradually. You don't need to collect everything about your users from day one. Actually, trying to do that will probably scare them off! I've seen apps ask for way too much information upfront and their conversion rates just tank.

Basic Data You Should Collect First

Here's what I recommend starting with—these are the basics that pretty much every app can collect without being too intrusive:

• Device type and operating system (iOS vs Android)
• App usage patterns—when they open it, how long they stay
• Which features they actually use (and which ones they ignore)
• Basic location data if its relevant to your app's purpose
• User preferences they voluntarily share during onboarding
• Simple feedback like ratings or thumbs up/down actions

The beauty of this approach is that most of this data gets collected automatically. Users aren't filling out endless forms or answering dozens of questions. They're just using your app normally, and you're quietly learning about their behaviour.

Making Data Collection Feel Natural

One mistake I see constantly? Apps that feel like they're interrogating users. "What's your age? What's your income? What are your hobbies?" It's like being at a doctor's appointment when you just wanted to order coffee!

Instead, weave data collection into the user experience. If you're building a fitness app, don't ask users to list all their favourite exercises upfront. Let them naturally explore different workouts, then track which ones they actually complete. That tells you way more than any survey ever could.

Remember, the goal isn't to collect as much data as possible—its to collect the right data that will actually help you personalise the experience. Quality beats quantity every time.

Simple Personalisation Features to Begin With

Right, so you've got your data collection sorted and you're ready to start building some actual personalisation into your app. But here's the thing—you don't need to go mad with complex AI algorithms straight away. I've seen too many teams try to build the next Netflix recommendation engine on their first attempt. It's bloody exhausting and usually ends in tears.

Let's start simple. The easiest win? Personalised greetings and user preferences. I mean, it sounds basic but you'd be surprised how much users appreciate seeing their name when they open your app. Beyond that, let people choose their own themes, notification preferences, and default settings. Its not rocket science, but it makes people feel like the app is theirs.

Content and Feature Recommendations

Once you've got the basics down, move into simple content filtering. If you're running an e-commerce app, show recently viewed items or suggest products based on what they've bought before. For content apps, try "more like this" recommendations—just match a few basic tags or categories and you're golden.

Actually, one of my favourite starter features is location-based personalisation. If someone always orders coffee from the same area, show them nearby cafes first. If they're browsing events, prioritise whats happening near them. The data's already there in most cases; you just need to use it smartly.

The best personalisation feels invisible to users—they don't notice the AI working, they just notice that everything feels right for them

Push notifications are another goldmine for beginners. Send messages at times when users are most active, or tailor the content based on their behaviour patterns. Someone who shops on weekends? Don't spam them with sale alerts on Tuesday morning. These small touches make a massive difference to engagement without requiring complex machine learning models.

Choosing the Right AI Tools and Platforms

Right, lets talk about the tools that'll actually make this personalisation thing happen. I mean, you could try building everything from scratch, but honestly? That's like building your own car engine when you just want to drive to the shops. There are some brilliant platforms out there that do the heavy lifting for you.

For beginners, I always recommend starting with something like Firebase ML or AWS Personalize. Firebase is particularly good if you're already using Google's ecosystem—it plays nicely with your existing setup and doesn't require a PhD in data science to get going. AWS Personalize is a bit more powerful but can feel overwhelming at first; the documentation is thorough but bloody hell, there's a lot of it!

Platform Options Based on Your Needs

• Small apps with basic needs: Firebase ML or Mixpanel
• E-commerce focused: Dynamic Yield or Yotpo
• Enterprise level: AWS Personalize or Microsoft Azure Cognitive Services
• Budget-conscious startups: Segment with simple rule-based personalisation

Here's the thing though—don't get caught up in feature lists and fancy marketing. I've seen clients choose platforms based on what sounds impressive rather than what they actually need. Start with something simple that integrates well with your current tech stack. You can always migrate later when your needs grow.

The key is matching the platform to your team's technical skills and your app's specific requirements. If your developers are comfortable with Python, look for platforms with good Python SDKs. If you're working with React Native, make sure the platform has solid cross-platform support. It's about finding the right fit, not the most advanced option.

Building Your First Personalisation Algorithm

Right, let's get our hands dirty with actually building something. I know the word "algorithm" can sound a bit scary—it conjures up images of complex mathematical formulas that only computer scientists understand. But honestly? Your first personalisation algorithm can be surprisingly simple.

Start with what I call the "if this, then that" approach. If a user opens your fitness app three mornings in a row, show them morning workout suggestions first. If someone always skips past your video content but reads articles, serve up more text-based content. It's basically teaching your app to remember what each user likes and give them more of it.

The Three-Step Algorithm Framework

Here's how I structure every personalisation algorithm, whether its for a startup or a massive company. First, collect the data—what did the user just do? Second, compare it to patterns—have they done this before? Third, predict what they want next—what should we show them?

Let's say you're building a news app. When someone opens your app, your algorithm checks: what articles did they read yesterday? How long did they spend on each one? Did they share anything? Then it looks for patterns—do they always read sports news first? Are they into tech stories? Finally, it makes a decision about what to show them today.

Keep It Simple at First

The biggest mistake I see developers make is trying to build something too clever from day one. Start with basic rules-based personalisation. Once that's working well and you've got enough data flowing through your system, then you can add machine learning models and more sophisticated prediction methods.

When you're ready to move beyond basic rules, choosing the right AI algorithms for your specific app personalisation needs becomes crucial for building more sophisticated prediction systems.

Build your algorithm in stages—start with simple rules like "show recently viewed categories first" before moving to complex machine learning models. You need data flowing through your system before the clever stuff will work properly.

Right then, you've built your personalisation features and they're live in your app. But here's the thing—how do you actually know if they're working? I mean, really working, not just looking pretty in your analytics dashboard.

Testing AI personalisation isn't like testing a button colour change. You can't just run a simple A/B test and call it a day. The results take time to show up because machine learning needs data to get smarter, and users need time to interact with your personalised features.

Key Metrics That Actually Matter

Forget vanity metrics for a moment. What you really want to track is user engagement over time—are people spending more time in your app? Are they completing more actions? Session length is a big one; if your personalisation is working, users should be sticking around longer because the content feels more relevant to them.

Retention rates tell the real story though. Check your Day 7 and Day 30 retention before and after implementing personalisation. It's a bit mad how much difference good personalisation can make to these numbers, but don't expect overnight miracles.

Setting Up Proper Tests

Here's what I do: run your personalised experience against a control group getting the standard, non-personalised version. Give it at least 2-3 weeks to gather meaningful data—AI personalisation gets better as it learns from user behaviour, so early results can be misleading.

Track conversion rates too, whether that's purchases, sign-ups, or whatever action matters most to your business. And don't forget to monitor user feedback; sometimes the data looks good but users are actually frustrated with the experience. Real user feedback beats fancy algorithms every time when it comes to understanding what's actually happening in your app.

Common Mistakes to Avoid

Right, let's talk about the mistakes I see businesses make when they're getting started with AI personalisation. And honestly? There are some proper clangers that can cost you time, money, and users.

The biggest mistake—and I mean the absolute worst one—is trying to personalise everything from day one. I've had clients come to me wanting Netflix-level recommendations before they've even collected basic user preferences. You know what happens? Their users get overwhelmed, the system performs poorly, and everyone ends up disappointed. Start small. Really small.

Data Collection Gone Wrong

Another classic mistake is asking for too much information upfront. I've seen apps that demand users fill out massive questionnaires before they can even see the main interface. That's a sure way to lose half your users before they've properly started. Begin with the basics and build up gradually; let users see value before asking for more data.

Then there's the privacy nightmare. Some businesses collect every bit of data they can without thinking about user trust or legal requirements. GDPR isn't just a suggestion, and users are becoming more privacy-conscious every day.

The best personalisation feels invisible to users—they just know the app understands them better over time

Finally, don't ignore the testing phase. I've seen teams launch personalisation features without proper A/B testing, then wonder why engagement drops. Your AI might think its doing a brilliant job while actually making the user experience worse. Always measure the impact of your personalisation efforts—sometimes less really is more.

Conclusion

Right, so we've covered a lot of ground here—from collecting user data to building your first personalisation algorithm. It's actually quite mad how much AI personalisation has become part of the mobile app world these days. When I started building apps, personalisation meant maybe showing users their name on the home screen; now we're talking about machine learning models that predict what users want before they even know it themselves.

Here's the thing though: you don't need to become an AI expert overnight. Start small, genuinely. Pick one simple personalisation feature—maybe personalised content recommendations or customised onboarding—and get that working properly first. I've seen too many teams try to build the next Netflix recommendation engine on their first attempt and end up with something that doesn't work at all.

The most important bit? Keep your users at the centre of everything you do. AI personalisation isn't about showing off how clever your algorithms are; its about making peoples lives easier and more enjoyable when they use your app. If your personalisation features don't make the user experience better, then frankly, what's the point?

And remember, this is just the beginning really. AI personalisation technology keeps evolving, user expectations keep rising, and new opportunities keep appearing. But if you follow the basics we've talked about—start with good data, choose the right tools for your needs, test everything properly, and actually listen to what your users are telling you—you'll be in a strong position to grow and improve your personalisation features over time.

The apps that succeed with AI personalisation are the ones that treat it as an ongoing journey, not a one-time project.