Dynamic bonuses: using machine learning to predict player preferences in casinos — Retail Technology Innovation Hub

By using ML_driven dynamic bonuses, online casino sites can attract players in unique and very customised ways. Highlighting popular £10 deposit bonuses in the UK, for example, can attract new players looking for low-commitment entry points, while experienced players might benefit from loyalty rewards of exclusive promotions.

How machine learning models in dynamic bonuses work

Machine learning models designed to predict player preferences are typically built on three core components: data collection, pattern recognition and personalised engagement. You can find out more about each one below: 

–        Data collection: Casinos collect data across all player interactions, including games played, frequency, the device used and the time of day for such gaming sessions.

–        Pattern recognition: By analysing this data, ML algorithms detect various behavioural patterns, recognising tendencies that might predict potential future preferences.

–        Personalised engagement: With patterns identified, the model can also recommend specific bonuses or promotions to individual players, increasing the likelihood of them accepting the offer and engaging.

These data-driven insights are designed to address player preferences, from offering tailor-made welcome bonuses to designing loyalty programs that align perfectly with the player’s gaming history.

A player that makes small deposits on a frequent basis might be more attracted to the best PayPal casinos in the UK, where they can benefit from safe and smooth transactions, along with snapping up casino bonuses perfectly designed for small deposits.

How online casinos use dynamic bonuses

The power of dynamic bonuses lies in their ability to match casino bonus offers with individual player profiles.

Some online casinos are now using machine learning to send real-time bonus offers based on the player’s current activity. If the player is on a winning streak, they might receive an extra casino bonus for example in a bid to encourage them to try a different game category.

On the other hand, a player who is on a losing streak might be offered cashback or a low-risk casino bonus to maintain engagement levels.

Another example entails highlighting the most attractive casino bonuses based on deposit methods. For example, casinos can create specific casino bonuses towards players opting for PayPal. Offering unique bonuses for PayPal players is not only about luring them to the site but also reassuring them with fast and secure transactions. 

Here are some of the most popular UK dynamic bonuses created by machine learning: