The digital gaming industry has undergone jolting changes over the past decade, as more organizations are looking towards data driven solutions. Gaming organizations have started to use big data to develop a deeper understanding of target customers. They have refined their data decision-making approaches to include new predictive analytics models to forecast trends and adapt to evolving customer behavior.
SAS is one of the organizations that has worked closely with leading gaming companies. They have developed analytics models to address looming changes in the dynamic industry.
SAS admits that the predictive analytics technology they have worked on has progressed more slowly than they originally anticipated. They said that the predictive analytics tools that they developed for their digital gaming clients took a lot longer than they thought, because they didn’t have all the data they needed to create a robust set of algorithms. However, they were able to develop the general framework to make future predictive analytics tools possible for gaming companies. They are being used in gaming companies all over the world. A number of companies offering online gambling in New Zealand are using the same types of predictive analytics models that SAS has worked on. They have found that the technology is revolutionizing their industry, as they offer their services in various markets.
What types of approaches do gaming companies use to develop predictive analytics models?
As Andrew Pearson wrote in his article “Predictive Analytics in the Gaming Industry,” the gaming industry has used some form of predictive analytics for decades. However, newer predictive analytics models are far more intricate and based on more sophisticated digital technology than they used to be.
Modern predictive analytics algorithms for gaming companies use hundreds of different variables. Older statistical modeling methodologies only used three or four variables, so gaming companies can make much more nuanced insights these days.
There are a number of different predictive analytics models that gaming companies have used in recent years. These include the following:
- Regression models that represent a wide range of possible interactions through mathematical equations.
- Linear regression models that use complex analytics to understand the relationship between both dependent and independent variables.
- Neural networks that try to understand relationships between variables when certain independent variables are not well understood. Neural networks could even be used to identify unknown variables and later incorporate them into the model.
- Logistical regression models that make determinations about binary dependent variables.
- Time series models that attempt to forecast future variable behavior.
- Rule induction models that use machine learning to extract rules from a wide range of observations.
Digital gaming companies of all sizes are finding a variety of ways to incorporate predictive analytics into their business models. As predictive analytics technology becomes more sophisticated, they will find that this technology will be even more valuable.
Is predictive analytics the key to sustainable growth in the gaming industry?
Towards Data Science wrote a very useful article on the evolution of analytics in the gaming industry. They made a great argument that predictive analytics models are going to be essential to maintaining industry growth.
Industry growth has averaged about 5% a year. Experts forecast that that figure could accelerate in the years to come.
However, the industry is going to have to overcome certain challenges to meet future revenue projections. They need to make sure that customer Expectations are continually met. Gaming establishments in some jurisdictions are struggling to do this, due to unanticipated bottlenecks and challenges. Gaming websites in newly legalized markets, such as New Jersey and Nevada have struggled to provide the service that customers are demanding.
Advances in digital data collection and predictive analytics should help them. Some of the ways that they can use predictive analytics to meet customer needs include:
- Identifying changes in the market and responding with minimal lag times
- Developing better security models to prevent data breaches
- Anticipating regulatory changes and developing compliance models to avoid fines
- Creating the best possible products for customers to enjoy
The applications of predictive analytics in the digital gaming sphere are virtually endless. Companies need to be smart about how they implement them.
