The gaming industry is more competitive than ever, and staying ahead means understanding what players want. That’s why many online platforms are turning to advanced technologies to help them better attract new users and retain their loyal ones. At the heart of this shift is one key strategy — predicting player behaviour.
For years, bonuses have been a popular method for improving engagement. And they still play a big role today. You can see many worthwhile promos intended to draw users and encourage them to keep playing. These benefits are customised for diverse games, depending on players’ preferences. For those fond of slot machines, for example, free $100 pokies, are a perfect match, allowing them to test the gameplay before adding real cash to their balance.
But while bonuses are still effective, they’re no longer enough on their own. Harnessing the power of big data to provide a personalised service that matches player behaviour can be the real solution. So, what technologies make this possible? And how do they impact modern gaming? Let’s find out.
Contents
Key Big Data Technologies in Online Casinos
First, let’s share an overview of the big data technologies used by online casinos. Then, we’ll take a closer look at what each of them means and what they are used for.
| Technology | What Does It Do | Purpose of Use |
| Machine Learning Algorithms | Analyses both past and current data to detect patterns in player actions. | Used to personalise services and can detect issues like problem gambling in advance. |
| Real-Time Big Data Processing | Analyses large amounts of data in real time to adjust balance in games and detect fraud. | Can detect payment fraud, balances games for more satisfying gameplay. |
| AI-Powered Behavioural Analytics | Analyses even complex player behaviours with deep learning to understand preferences. | Used to offer deeper personalisation than machine learning. |
| Fraud Detection & Risk Management | Detects cheating in games and determines risk levels for each player. | Used to prevent cheating and setting player-specific restrictions. |
| Customer Segmentation & Personalisation Engines | Segment players to offer personalised bonuses and services. | Used for marketing optimisation and increases player retention. |
| Predictive Analytics | Analyses betting habits using statistical models. | Used for targeted marketing and increases player retention. |
Machine Learning Algorithms
Machine learning is the main technology used to predict player behaviour. It also analyses a player’s past and real-time gaming data without having to be programmed for this purpose, and makes a consistent prediction about the actions they will take in the future. For example, ML algorithms can:
- Detect which game types the player spends more money on and highlight the games that are suitable for their betting range.
- Analyse the distinctive features of the bonuses claimed by the player and the completion time of the wagering process, determine which promotions will attract their attention and create bonuses of that type.
- Offer customised content according to the platform (mobile or PC) the player uses to try their luck. For example, it can list games suitable for touch controls on mobile.
In addition to all these, machine learning can also be used to detect problem gambling early. If players’ betting patterns indicate an issue, it can ensure that the necessary precautions are taken in advance.
Real-Time Big Data Processing
A huge amount of data is created in an online casino every day. This data includes information about player interactions, payments, support requests, win/loss ratios, and when a game was abandoned. AI can analyse this data almost instantaneously, extracting extremely valuable insights from it. The most important of these is game balancing. AI can change the difficulty of a game in real time, keeping players engaged and continuing to play. In addition, this technology can easily detect and flag fraudulent payments (i.e. transactions that violate AML policies).
AI-Powered Behavioural Analytics
Machine learning can sometimes be insufficient in analysing complex player behaviour. This is where artificial intelligence systems using deep learning models and neural networks come into play. These systems can analyse dozens of different data points for each player, creating a highly detailed model of their personal preferences and habits. These data points consist of information such as betting frequency, money spent per game, player attitudes towards bonuses and playing time. The model created is specific to each player and can be used for much deeper personalisation. Online casinos often perform this for VIP and high roller players – AI-powered behavioural analysis can provide extremely valuable information on what to do to retain them.
Fraud Detection and Risk Management
The only fraud that online casinos need to detect is not in terms of payouts. Players may sometimes try to cheat in games, use strategies that give them an edge, or open different accounts to claim the same bonus multiple times. Big data technologies can detect and prevent these types of fraud as well. By constantly working in the background and tracking player actions and gameplay anomalies, they can detect who is cheating with surprising accuracy. They can also set real-time limits for players they deem risky. For example, if they suspect a player is cheating, they can lower the maximum bet limit to 1 unit until a real human can evaluate the situation.
Customer Segmentation and Personalisation Engines
Big data analysis helps online casinos segment their players into different categories. This is also part of classic marketing techniques, but AI allows it to be done much more efficiently and at a deeper level. Thanks to segmentation, players can be divided into the following categories, for example:
- High rollers. VIP customers who spend the most money in the casino.
- Casual players. The segment that most casino players fall into. They can be divided into sub-segments according to their budget and playing time.
- Problem gamers. Players who cheat, abuse bonuses or are at risk of developing an addiction. These can also be divided into sub-segments.
Such segmentation gives online casinos additional insights into offering customised bonuses and gaming recommendations. They can also determine which players to restrict. This allows them to focus on truly valuable players and increase their retention rates.
Predictive Analytics
This essentially means combining other data technologies to provide deeper analysis of betting habits and player behaviour. Using a combination of statistical models and ML algorithms, highly accurate predictions can be made about when a player will stop playing or change their betting pattern. This data is used in targeted marketing campaigns, resulting in increased retention rates.
Remember that not all of these technologies are used at the same time. An online casino can use only one (or all) of the techniques listed here. In this regard, each operator makes their own decision. It is possible to say that machine learning is the most common technology, while technologies such as predictive analysis have costs that only the largest operators can afford.
In any case, online casinos can use big data technologies to enhance the player experience, improve risk management, increase player retention rates, make marketing campaigns much more effective and make response times almost real-time. All of these are crucial for them to stay competitive and one step ahead of their competitors. The future of the iGaming industry, like many other industries, lies in personalisation: the era of offering the same bonuses and games to every player is over. Any operator that can offer personalised recommendations and services to players will be more successful than their competitors.
Ross, an exam specialist with a passion for education, writes comprehensive articles on exam results and admit cards. His expertise ensures students receive reliable information and useful tips to excel in their exams.
