Presenting the right training video that fits the users’ needs and activating all content on the website – not just the newest uploads.
Based on Raptors algoritms, Nordic Hiit is able to present the most relevant training video to their users that matches training level and interest.
When we talk about personalization, it usually refers to a commerce site, product recommendations, or upsales. But data-based recommendations can cover so much more. Nordic Hiit is a great example of that.
Nordic Hiit is an online training concept with online video workouts on demand.
The mission for Nordic Hiit has been clear from the beginning; it should be easy for everyone to live a healthy lifestyle – even during a busy schedule and without having to hit the gym.
Nordic Hiit has developed a platform which gives their users access to 1000+ workout videos within the categories HIIT, yoga, running, office workouts, pregnancy workouts, postpartum exercises, and pilates. The videos can be accessed on their website and through their app.
Every user on Nordic Hiit’s platforms has chosen a specific training level, training goal, training days, and what equipment they have available in their home, on their profile.
This means there are a lot of different preferences that have to be taken into consideration when creating recommendations for the users.
Whenever a user clicks on “next video”, a signal is sent to Raptor’s recommendation engine. The Raptor recommendation engine then starts the process of finding the most relevant video for the individual user on Nordic Hiit’s platforms. This takes place in 3 steps:
Step 1: The algorithm starts by separating the three different training levels that the individual user has chosen in their profile preference settings. This means that there are three different algorithm paths which are either beginner, intermediate, or advanced.
Step 2: The recommendation engine then starts a data filtering process by looking at the individual user’s profile. Based on the chosen equipment, training goal, and training days it will include and exclude videos from the video catalog containing +1000 videos. Likewise, the algorithm will make sure that the videos fit the current season, so the workout videos that fit into a winter environment do not get recommended in the summer months.
Step 3: In the last step, the algorithm takes into consideration which workout videos you have watched recently. If you have just done a workout that trained your legs and abs, then the next video should not contain the same exercises.
When the user clicks the “play” button, the algorithm immediately finds the most relevant video.
The algorithm starts this process every time a user clicks the “next video” button, and every time the algorithm finds the correct order of videos to show. All these steps in the algorithms happen within milliseconds, and therefore there won’t be any delay for the user.
We are proud of this case since it shows how custom our personalization engine can be. The results of the Nordic Hiit solution are a testament to the agility and customization of our solution. We emulate a personal trainer to find the most relevant workout videos based on the user’s preferences. This will give the users the best training results based on their training level, goal, equipment, and previous workouts.
Nordic Hiit’s journey towards creating the most personalized online training experience in the market doesn’t end here.
Recently a ”favorite” button has been added to the videos, which users can use to save a workout they enjoyed.
But the button does more than just save a video for future use!
The favorite button can be used to create lists of the user’s favorite workouts, but we can also use the data to recommend related videos that other users who have liked the same videos have seen.
We all know it from Spotify, which creates playlists based on the music we listen to. We can do the same for video workouts. If a user has watched a lot of workout videos about yoga or stretching, then we can create a playlist with more videos like the ones they have watched.
Trigger messaging is also a part of Nordic Hiit’s future plans.
If a user has been at the same training level for a certain amount of time, Raptor can send messages like “Is it time to take your training to the next level?” to get users to watch new videos, which hopefully means that they will remain a member.
Based on all Nordic Hiit’s user’s behavior such as clicks, favorites, and profile settings, the plan is to create a public playlist that is tailored towards the individual, which can create even more value for the user.
Raptor has helped Nordic Hiit take their home workout platform to the next level, offering a personalized and individual workout experience for their users. This case proves that the right personalization engine can be customized and tailored to any business and its goals.
- Nordic Hiit went from a static one-size-fits-all model to a dynamic 1:1 personalized workout experience
- The right personalization engine can be used to personalize any businesses customer journey
- Tailored recommendations ensure a better customer experience overall, and for Nordic Hiit it has boosted Customer Lifetime Value (CLV), Trial-to-Paid conversion rate, and prevented churn.
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