Machines are here to personalise

In the 1985 sci-fi novel Ender’s Game a group of children are trained via a virtual game to become defenders of humanity.

In the book, a so-called “Mind Game” gathers information on each child’s abilities and psychological states to tailor its challenges to their unique experience.

Strangely, this future doesn’t sound so unrealistic in today’s technological climate.

Two main elements of creating a game like that – virtual reality and machine learning – are already on the rise.

People will soon be able to spend a lovely evening at the poker table wearing a VR headset in the comfort of their cosy bed. And when it comes to machine learning, there are almost endless possibilities to creating custom user experiences.

Machine learning is a kind of artificial intelligence that can learn from vast volumes of data and provide the hidden insights and predictions.

Egaming companies obtain a massive amount of data, but in most cases, it is not used to drive competitive advantage or improve customer engagement.

However, with just a small amount of information on a player’s preferences, you can substantially impact how they interact with the game, in a way that’s beneficial for both sides.

Depending on the game, the time needed for a machine to capture a player’s style may vary (for a simple game like Tetris, it would take about an hour).

This information will allow the system to predict a player’s next move and challenge them to experiment with tactics.

The success of Fantasy Sports Betting speaks for itself. In a game where participants create virtual teams with real life athletes, a complicated algorithm determines the results of the game based on a huge amount of frequently updating statistics.

Whether you’re playing with a group of friends or in an open competition format, no game will be the same and the results are almost as unpredictable as in real sports.

But machine learning opportunities in user experience personalisation don’t end with the actual games.

In the same way that bricks and mortar casinos use information on customers’ wealth and food preferences, online brands are able to analyse data on each player’s interaction in order to market the most appealing offerings. Gone are the days of categorising them by age and gender.

Players now expect to be faced with relevant content on their specific interests.

The use cases for machine learning in online gambling personalisation are vast and sometimes remarkable.

For instance, the Finnish national betting agency, Veikkaus, does a meaningful job by applying both on and offline data to spot potential cases of gambling addiction among users and stop marketing towards them as a remedy.

Another interesting example is SaaS start-up Dojo Madness, which harnesses analytics to coach and help players gain online gaming skills for a variety of titles.

In the long run, gamblers will always prefer rich and challenging experiences, which is why brands should focus on providing that in the most technologically advanced way.

Machine learning and predictive data analytics will undoubtedly play a vital role in innovating operators as they improve customer journeys.