Creating an app that feels personal to the users is a goal for many – if not all – app developers. Users are more sensitive to the user experience (UX) that you deliver, and they actively seek apps that can deliver a personalised UX at every turn. Users want to be pampered and the way to do that is by offering advanced personalisation.
One of the ways you can do that is by integrating machine learning into your app. Machine learning and a massive amount of user data will help you understand users in a granular way. Instead of placing your focus on the big picture, machine learning helps you focus on individual users without having to do manual work.
Integrating machine learning, however, isn’t without its challenges. Technical challenges aside, there are a few key things that you need to know about adding machine learning to apps.
Consistency Is Still Important
Just because you want to deliver a personalised user experience, it doesn’t mean you should change everything. Some degree of consistency is still needed; for instance, changing the menu isn’t always a good idea, especially when the users are already accustomed to the old menu layout.
Adding consistency gives your app a grounded feel. You want to keep certain elements consistent in order to introduce that familiar feel every time users open the app. While some degree of personalisation is great, a familiar layout, set of colours, and other elements are still needed.
The real challenge is finding a balance between the two sides of the equation. To deliver the perfect yet highly personalised user experience, you need to listen to users, pay attention to how they use the app, and personalise elements that aren’t critical.
Here’s another important point about utilising machine learning for personalisation: it takes time to really satisfy users. You can either use mock data to allow the AI core to develop or run an open beta and have the AI learn directly from existing users. Both have consequences.
There is a third option of allowing machine learning to develop in the background without doing any real personalisation, but this too isn’t easy to integrate, especially when you have a complex app. It takes a considerable amount of development before the AI core can start interacting with other parts of your app.
That’s where third-party developers come in. instead of trying to figure everything out yourself, it is often more effective to work with local app developers with sufficient machine learning prowess.
Appetiser, a Melbourne-based app developer, is one of the leading names in this area.
Working with Appetiser lets you leverage the company’s experience and still deliver personalised user experience within a short period of time. You are cutting both the time required to integrate machine learning and the cost of implementing ML.
As long as you keep these key points in mind, machine learning can help you take your app to new heights. You will not only be improving the app but also pampering your users at the same time.
Listen to the Users
That actually brings us to our second point: listen to the users. Machine learning can process a large amount of user data but relying on machine learning alone isn’t the way to go. Remember when Instagram introduced its new stream algorithm? Remember how disastrous that was?
For Instagram, the major change was worth keeping for the sake of their bottom line. Instagram is also a big platform that everyone relies on, which means they have a much higher bargaining power during difficult times. A new app, on the other hand, may not survive such controversy.
As mentioned before, you need to pay attention to how users are utilising your app features. The ones you want to personalise are elements that users use often, but not in a tactile way. Customising the home greeting is safer than altering how categories are adjusted using machine learning.
Know the Limits
The next thing to understand about adding machine learning to your app is the fact that it has its limits. Machine learning isn’t a magical technology that can transform your app into a personalised one. You still need to feed in good user data and use a suitable learning model for it to be effective, especially when using drawing tablets.
Machine learning requires a good set of metrics and data to learn about your users optimally. It is up to you to determine the metrics to track – and the ones to ignore. Once the metrics are selected, you also have to determine how metrics are processed into insights. AI models and algorithms designed to analyse users must be adjusted to the data sets they process.
The last element of the equation is how insights are utilised for personalisation. You have to go beyond strings and predetermined values. Instead, you need to integrate insights generated by your machine learning framework as part of the native UI.