At its core, personalised app marketing is all about relevance, making users feel like your app understands and responds to their needs. It’s not about overpromising or flashy gimmicks, but about delivering the right message at the right time in a way that feels intentional.
And it matters. Recent data shows that 63% of smartphone users are more likely to purchase from companies that provide relevant recommendations. In today’s mobile landscape, personalised app marketing has become less of a differentiator and more of an expectation. For app marketers, it’s a key lever for improving engagement, retention, and, ultimately, growth.
What is app personalisation?
App personalisation is the practice of tailoring a user’s in-app experience based on a combination of their preferences, behaviours, and demographic data. It can influence everything from how your product is designed to how users are onboarded, to the content to the content and messages they receive throughout their journey.
Users have grown accustomed to receiving personalised experiences, whether that’s recommendations based on what they’ve viewed, timely nudges to complete an action or content that feels contextually relevant.
Why personalise your app marketing?
Research shows that 71% of people now expect personalised experiences, while 76% feel frustrated when they don’t receive it.
Mobile apps are uniquely positioned to meet this demand, thanks to the wealth of first-party data they generate. From user preferences and purchases behaviours to usage patterns and interaction history, this information can, and should, be used to shape how and when users are engaged.
The three levels of app personalisation
- Reactive personalisation (basic)
This is the entry point, where most apps begin. It involves using basic user attributes to tailor, such as:
- Addressing users by name in an email
- Sending a welcome message after sign-up
- Displaying recently viewed items in an app
These small elements can improve user experience without requiring significant resources or infrastructure.
- Proactive personalisation (intermediate)
Here you’re layering behavioural data on top of user attributes. This creates more relevant, timely interactions based on what users have done in the app. Examples include:
- Recommending relevant products based on browsing history
- Serving targeted content, such as a blog post related to a user’s interests
- Highlighting active offers or discounts based on previous interactions
This approach bridges the gap between reactive messaging and deeper user understanding.
- Individualised personalisation (advanced)
This is the gold standard of app personalisation; it’s where it becomes truly dynamic – responding to user behaviour in real time and tailoring the experience accordingly. It’s what separates a good experience from a great one. Examples include:
- A streaming app showing a personalised email banner featuring a show the user watched in the past week
- A fitness app suggesting workout routines based on recent activity patterns
- A shopping app that curates product suggestions based on past searches or purchases
This level of personalisation requires a stronger data infrastructure and automation but pays off in deeper engagement and loyalty.
What is dynamic content and why does it matter?
Dynamic content is any digital content (images, text, videos) that automatically adapts based on a user’s in-app behaviour, interests, or preferences.Instead of manually creating multiple marketing automation campaigns for different user segments, marketers can create a single campaign where relevant content is pulled in dynamically.
This might look like:
- Swapping out images based on cart contents
- Customising copy and messaging based on recent searches, e.g., “Still searching for the perfect shoes?”
- Personalising subject lines based on location or weather, e.g., “Five snow-friendly shoes you’ll need in Manchester this week.”
What are the challengers
As with any meaningful approach, personalisation has its challenges. One of the biggest challenges is scale, especially for apps with broad user bases.
Let’s say you run a job search app focused on creative industries. Your user base might include graphic designers, copywriters, strategists, or art directors. Creating highly specific content for each group would be ideal, but it would be resource-heavy and difficult to maintain.
You also need to consider how often your data is refreshed, how it’s stored and segmented, and which data points are actually useful.
How to personalise at scale
The key to doing this well is to identify patterns across users and build campaigns around broader, high-impact segments.
Instead of creating one campaign for each job title, you could group users. Using a job search app as an example, you could group:
- Full-time job seekers
- Part-time job seekers
- Freelancers
Then craft messaging that’s specific enough to feel relevant but broad enough to manage, e.g. “Four Must-Have Resources for Creative Freelancers”.
This approach lets you retain the benefits of personalisation without dozens of workflows and campaigns.
Why data structure matters
For this approach to work, your data must be clean and well-structured. If you want to deliver relevant experience at scale, you need to know who your users are, what they’re doing and where they are in their journey.
The more attributes you collect, the more flexible and effective your campaign options become.
How to set up personalised app marketing campaigns
- Centralise your data
To create effective personalised app marketing campaigns, your data needs to be centralised and accessible. Bring together data from your CRM, product analytics tools, and external sources to get a complete view of your users. This could include:
- User attributes (job type, location, preferences)
- Event date (app activity, purchases)
- Product data (top listings, abandoned carts)
- External data (weather, local trends)
This integration makes it easier to build smart segments and identify what really influences user behaviour.
- Keep your data fresh
How often you refresh user data depends on the type of campaign. For example:
- Cart abandoned reminders might only be relevant for a few hours.
- Content recommendations might last a few days or weeks
Working with stale data will only make your campaigns feel irrelevant.
- Map the use journey
Think of your app’s user lifecycle as a funnel:
Activated → Engaged → Monetised → Retained
Each stage requires different messaging. For example:
- Encouraging activation: If an engaged user views three products in 14 days, dynamically featuring those items in an email can nudge them toward conversion.
- Balancing frequency: Monetised users might be more active than newly activated users. If you over-message new users, they may feel overwhelmed instead of engaged.
By defining each stage of your app’s lifecycle, you can craft targeted, data-driven campaigns that guide users seamlessly through the funnel.
- Test and iterate
Once your segments and dynamic data are in place, focus on testing your assumptions.
Start with a simple hypothesis:
“Love Island fans are more likely to subscribe to an ad-free plan when a new season launches.”
Run a test, analyse the results, and refine based on what you learn. That’s where the real value lies, as it allows you to evolve your personalisation over time.
Final thoughts
Personalised app marketing isn’t about one-off wins or flashy tricks. It’s about creating relevance in a way that scales and combines data, technology and thoughtful content strategies which deliver better outcomes for both users and businesses.
Whilst it will take time to lay the foundations, once you have a clear data structure and manageable process, personalisation becomes a powerful, sustainable part of your app marketing strategy.
To discuss how personalisation could improve the journey for your users and how to go about implementing a strategy, get in touch.
