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Five great examples of personalisation that are getting us excited

7th November 2019

Personalisation is all the rage in the world of digital design.

Having already shared some heuristics we thought we’d delve into a few examples of personalised experiences that we like – i.e. those which are all about providing genuine value to users.

Spotify –  the algorithms are helping you

Spotify intelligently use information based on your listening behaviours and patterns to make suggestions about new music. The new music which is offered in playlists have particular names and explanations to help users understand why this has been displayed to them. These lists are also easy to navigate into and out of meaning you don’t lose sight of the journey you were on previously (your own playlists and preferences), whilst allowing for exploration and serendipitous experience.

It’s well publicised that they have an AI engine which supports how it recommends music to users:

“[Spotify’s] home screen is governed by an A.I. system called BaRT (“Bandits for Recommendations as Treatments”). The system’s task is to organise each home screen in a personalised way for each user.”

It does this by using information about you; basic account data, location, your viewing habits and then combines it with the information other users like you have given to make personalised recommendations to you about future listening.

What’s more, the algorithm analyses the music which people listen to understand the elements of the composition that people like and the similarities and differences inherent within them. The algorithm also makes better recommendations over time based on the information it gathers about the listening behaviour users exhibit on the playlists it has recommended. 

Duolingo – tailored learning

Not only is Duolingo an app with great onboarding they also use AI to power personalised language learning. As a user trying a new language on the platform you go through a proficiency assessment. As you go through this process each subsequent question you have to answer is determined by the answer you’ve given previously as part of the process. This builds your own language profile which helps tailor questions to you.

Here, personalisation is powered by deep learning, an AI which mimics neural pathways. The AI makes intelligent predictions based on a user and their previous response history using natural language processing. Your response to any given question (right or wrong) is then fed back in. This personalises future learning experiences – whether you recognised or failed to recognise a word may increase or decrease the amount of times it comes up again, and the context in which it does.  

Nike Run Club – personalisation for disengaged users

Sports applications are the frontrunners (forgive the pun) on the personalisation front – whether that’s Strava or My Fitness Pal, they just collect a lot of data and use it well. We’ve chosen one example - Nike Run Club. It has tailored messaging, remembers previous routes taken, replays activity and makes coaching suggestions for improvement. There are also some other features which approach personalising low usage messages differently.

If you don’t keep to your training plan – i.e. failing to run - it offers you different content the next time you open the app. Rather than treating someone as a dead user, or using marketing messages emails/texts/push notifications to guilt them into using the app they use content like this:

This is followed by messaging about reasons for not running with options like, "stress", "injury”, "time poor" and "it wasn't for me". By choosing any given option the content is modified further.

Note the inclusion of names in the messaging and an augmentation of the experience you’d typically have – here, powered by content with a little bit of humanity based on information provided by the user. Clearly, running isn’t for everyone. But Nike Run Club shows what you can do if you take simple recent data sets, in this case inactivity, and combine it with information supplied in the moment by the user.

Headspace – personalised content when you need it

Headspace is an application which helps alleviate stress with sounds and visuals. It orders the content a user has listened to first and demarks that by positioning it highly on their playlist. Important given the context of use, users visiting the application may need quick access support or comfort.

As it’s specific to certain user groups it makes the experience more straightforward – particularly if the experience is happening under emotional stress. This removes the need to scroll and search by accurately remembering and replaying journeys and experiences users had previously.

Headspace also makes smart recommendations based on previous listening behaviours. It highlights content people otherwise wouldn’t have looked for or known about given what they’ve listened to before.

Clue – personalisation for greater health autonomy

Clue allows women to track their menstrual cycles. You can tag different aspects of your cycle to see patterns relating to your hormones or even predict when you are most fertile. With this data it surfaces content based on what you’ve tagged as relevant to you. Also, depending on your recent experiences, the app surfaces content differently to best meet individual user needs. 

It’s up to you what you track, and your decisions allow you to further personalise your experience. If you’re not interested in something you won’t see it. 

The premium version responds even further to user needs by making smarter recommendations and creating a more complete picture of a user’s month to month cycles and the nuances of each. The more data you record, the more personalised and intelligent response the platform gives.

This alleviates the strain on healthcare professionals and provides a great user experience which gives people autonomy and confidence over this aspect of their health.

So what?

You’ll notice that a lot of these examples are standalone apps or product/service success stories. Why is this?

With personalisation the best results will be delivered if it’s scoped in as part of your technical landscape from the start. This is because implementing personalisation is inherently difficult.

However, you can retroactively scale up for personalisation if you carefully consider the following conditions.

  • If it is solid technically and works with your infrastructure.
  • If you’ve carefully assessed - qualitatively and quantitively - the value it can deliver to your users.
  • If you’ve validated the value against user feedback when you implement.

As ever, we’d advocate for personalisation to inform part of your experience strategy from the get-go. If that’s not possible don’t be put off with getting started just be mindful of the things you need to consider.

Here are the company blogs of the examples we’ve listed out above – they’re a wealth of information:

  1. Making DuoLingo
  2. SpotifyLabs
  3. Hello Clue
  4. Headspace Blog
  5. Nike+ Run Club Blog

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