You can’t go online these days without seeing the words big data, artificial intelligence, or machine learning. These concepts are more than just buzzwords. They have become the basis of business decision making, customer research, risk analysis, and more. But it’s not only companies like Google and Facebook that are using big data for these applications. They’re now being used in big businesses and even SMEs with clever tech strategy in place around the world – and the iGaming sector is no different. Online gaming relies on big data for a variety of reasons. Big data is being used by lots of websites that compare no deposit gaming sites with a big table, with each feature given a score from 1 to 5 (and perhaps a behind-the-scenes weighting). Big data plays a role in rating all the different aspects and highlighting recommendations for readers. If the author of such a table gathered and analyzed this data with a machine (instead of coming up with it themselves) then they would be using big data in the most profitable way.
Why is big data a prime business tool for the iGaming industry?
Any business that depends on consumer-business interactive technology is already primed to take advantage of the benefits of big data. They already have data flows circulating between customers and the software they run that can start capturing data, so they can analyze it in the future for a variety of benefits. iGaming is a broad term that encapsulates all of online gaming activities. It’s offered via websites, apps, in-house booths, and even other consumer techs like VR. These are all generally governed by the same rules as in-person betting at a live gaming establishment. Instead of offering these services through traditional channels, they are provided through a frictionless, easy-to-navigate experience through an online channel. As you can imagine, the global online gaming market is huge. There are billions of dollars flowing through the iGaming market, as consumers place bets large and small on a huge amount of different outcomes. Big businesses in iGaming have plenty of funds to invest back into business to increase revenues, enhance the customer experience, and venture into new iGaming avenues. Developing big data use cases is a complex activity that does require an investment – investment into investigating the types of questions that data can answer, then development of solutions to capture that data and analyze it, converging on an answer. One example of such a question might be a company investigating when to advertise new products to customers. They will be able to gather gender, age, and location statistics from their customers, as well as spend. They will be able to determine engagement statistics, times, and whether (and when) they’ve clicked on other ads. From all this data, they will then be able to figure out when customers are most likely to try out a new betting game. They’d also be able to determine which advertising avenues appeal most to their demographics and try to lure new customers that fit these particular demographics at the right place and the right time.
What big data solutions have been tried in the iGaming space already?
A study published in the International Gambling Studies journal titled ‘Predicting online gambling self-exclusion: an analysis of the performance of supervised machine learning models’ from 2016 is a good first example. This study examined the use of machine learning algorithms to determine which gamblers were more likely to use self-exclusion tools, following learning through trends in their online gambling behavior. The study examined 4 different techniques, with the best-performing algorithm being 35% more accurate than baseline estimates. Research like this could allow iGaming houses to put into place efforts to reduce addition before the need for self-exclusion – thus lowering the chances of customers heading into full blown addiction. Bet Buddy conducted similar research into responsible gambling online in 2018 with a 6-month project – this time examining whether they could determine risky behavior from single sessions of customer play. What they found? They could identify that the number of different games per session had an impact on whether a player might be an at-risk person. The City University of London recently partnered with Kindred and Bet Buddy to investigate the application of machine learning in the identification of money laundering behaviour across online gambling transactions. Or there’s even this study, Predicting Customer Churn Rate in the iGaming Industry using Supervised Machine Learning, from the Kth Royal Institute Of Technology School Of Engineering Sciences. This study investigated using “24 hours of initial data on player characteristics and behavior to predict the probability of each customer churning or not.” The results of this machine learning study showed the model achieving 75.94% accuracy in predicting customer churn. As you can see, there are plenty of big data applications in iGaming, both theoretical and already proven in studies. You can bet that plenty of the big gambling houses are already utilizing big data and machine learning behind the scenes to make their offering more lucrative and appealing, within the gambling rules and regulations set out in each jurisdiction, of course.
Big Data is Transforming the Online Gaming Market
The online gaming industry is undergoing massive changes. Big data is the basis for this impressive revolution. What will it bring in the future?
