Fans United supports a set of prediction games:

• Тhe Predictor feature allows your users to predict various results in different matches.
  These results are then validated against the real-life match and the user is rewarded if their predictions are correct. Users also have success rates for their performance. For example, 67% overall success, 43% for AC Milan predictions, 84% for Bundesliga predictions and so on.
• Top X is a game where users must predict a specified number of correct scores. You can create different editions of the game and specify rewards for each edition.
• Match Quizzes allow users to predict different aspects of a match. How many goals or corners? Who will be the first goal scorer? Will there be a penalty or a red card? These and much more will hep you engage your users. Of course, you can create multiple match quizzes and specify rewards for each of them.

The Actitity Service is a feature created for micro-engagments. Likes, dislikes, comments written, pageviews made, content consumed, conversions and much more. Each “activity” the user makes comes with context (e.g. Manchester United, Premier League, Cristiano Ronaldo). We then record this data and calculate engagement levels for each entity.

Allow your users to specify how they want to be notified and for what. For example, Real Madrid news to be received via email, while kick-off notifications for Arsenal to be received with a push notification.

Fans United works with your favourite notification providers.

The loyalty system rewards users for their interactions with the platform by assigning points. Besides having a points tally, users are assigned into tiers, depending on their performance. They also earn badges for different accomplishments and compete in various rankings/leaderboards. Of course, all of this can be configured by the client.

Engagement coefficients are based on the marketing RFM model and allow you to split your users into different segments based on their recency, frequency or attention spent. This is done based on their overall interactions or activity connected to a particular entity (e.g. a team or a competition).

In turn, you can then say: “Give me all the users who used to frequently engage with Manchester United and spent a lot of attention, but have not engaged for the past 2 weeks.”