In the digital and data-driven gambling environment, identifying high-value players and potential high-risk gamblers has become more important than ever. With artificial intelligence (AI) analytics technology, operators can now gain deep insights into player behavior patterns, capture early signs of gambling harm, and optimize customer lifetime value (CLV). Emma Philip, Chief Solutions Engineer for Fullstory in Europe, the Middle East, and Africa (EMEA), shared seven types of player behaviors that every operator should focus on, as well as how to use behavioral data for real-time intervention and sustainable growth.
For operators focused on long-term profitability, achieving sustainable growth involves not only acquiring and retaining high-value players but also managing the risks associated with potential vulnerable or problem gamblers. Data is a core tool, but relying solely on transaction data does not reveal the full picture. Transaction records can show deposits, withdrawals, and betting situations, but the real behavior and potential risks of players need to be captured through richer behavioral datasets, which can reveal the characteristics and patterns of high-value or high-risk players.
The Importance of a Data-Driven Environment
Emma Philip noted, "The key is to expand the data perspective, not just when users deposit or bet, but also to observe their overall interaction with the platform." She emphasized that merely seeing large deposits or bets could be misleading—it could either indicate a high-value player or mean that the user is at risk under a responsible gambling environment. Therefore, collecting behavioral data, analyzing the actions and interactions of players between deposits and bets, online duration, and how they use platform features, is crucial for assessing user status.
Philip further pointed out, "Are the responsible gambling features promoted by the brand truly effective? Through behavioral data, we can identify abnormal patterns and determine if intervention is needed."
Based on this concept, Fullstory categorizes player behavior into seven key patterns for identifying loyalty or potential risks.
Seven Key Player Behaviors
1. Gradually Increasing Deposits
The trend of deposits not only reflects the total amount of transactions but also reveals player behavior patterns. Philip explained, "Steadily increasing deposits usually mean high-value players, while irregular fluctuations could be a risk signal." High-value players tend to follow stable patterns, and their deposits do not show drastic fluctuations.
2. High Engagement in Promotions and Bonus Activities
High-value players usually participate in customized promotions, personalized game recommendations, and loyalty programs. Philip said, "Players immersed in the platform experience are more likely to try all available content. High engagement typically means the user has high value."
3. Smooth Completion of Registration and Deposit Processes
Smoothly completing registration and deposit processes is often a sign of stable player behavior. Philip noted, "High-value players follow expected patterns during registration and deposit processes, while risky or problematic players may exhibit frustrated or aggressive behavior."
4. 'Angry Clicks' in Deposit, Bonus, or Withdrawal Processes
"Angry clicks" refer to players repeatedly and rapidly clicking the screen or mouse due to system or process issues. Fullstory reports show a 40% year-over-year increase in mobile angry clicks. Philip stated, "This may reflect player emotional fluctuations, indicating their risk of gambling problems, and can also serve as an intervention signal for high-value players."
5. Repeated Verification Failures
Multiple failures in KYC or payment verification may indicate potential risks. Philip added, "Repeated error messages are an important indicator for identifying risk players, also showing that operators have taken preventive measures."
6. Switching Pages Before Completing Key Actions
Frequently switching pages before completing important actions may indicate low engagement or unstable behavior. Philip explained, "Users switching tabs or refreshing pages too quickly might mean they are not fully engaged or are having a poor experience, indicating their value and risk status."
7. Abandoning After Receiving a Bonus
Unexpectedly exiting after completing a process and receiving a reward may reflect engagement issues or abnormal psychological states. Philip pointed out, "This behavior might occur among new users, those in poor mental states, or players with insufficient funds."
The Importance of Timely Intervention
Identifying the above seven types of behavior is significant for both operators and players. Philip emphasized that traditional transaction data is usually delayed by 24 hours, while Fullstory behavioral data is generated in real-time. Early intelligent intervention not only helps reduce risks but also enhances profitability. For example, a client saved over 300,000 pounds and significantly improved the experience for high-value players by early identification of problem users through Fullstory.
Real Cases and Industry Practices
At the iGB webinar in June, Fullstory, together with Game Lounge and Casumo, shared the application of behavioral data in enhancing player experience and identifying high-risk gamblers. Casumo increased problem detection speed by 50% and reduced resolution time by 87% through session replay and watched elements tools, providing a smoother experience for high-value players. Game Lounge used Fullstory to validate hypotheses, track behavior, and optimize user experience, reducing ineffective clicks by 70% and error clicks by 50%.
AI-Driven Behavior Capture
Fullstory utilizes AI technology to achieve comprehensive user behavior capture, collecting interaction data from web and mobile platforms without manual tagging, including actions like play, pause, zoom in/out, and copy-paste. StoryAI further accelerates pattern tracking and behavior summarization, providing operators with fast, accurate player insights.
Additionally, Fullstory offers automated templates for responsible gambling and high-value customers, triggering interventions through real-time alerts. For example, if a high-risk player is detected, the relevant team can receive immediate notifications and take action.
Key Intervention Signals
Behavioral data can also reveal various risk signs, including online activity during antisocial hours, attempts with multiple payment methods, creation of multiple accounts, rapid increase in bets, and extended gaming time. Player interactions with responsible gambling features (such as adjusting deposit limits or modifying self-exclusion rules) are also important indicators.
Philip stressed the importance of data centralization and breaking information silos: "Centralizing data ensures that teams access as much valuable information as possible, track behavioral changes, and quickly identify potential high-risk or high-value players."
Personalized Strategies and Sustainable Growth
Personalization helps protect high-value players while strengthening responsible gambling interventions. Philip said, "You can send personalized messages for abnormal behaviors while ensuring a smooth experience for high-value players."
As regulatory pressures increase and player competition intensifies, operators embracing real-time behavioral insights are more likely to achieve sustainable growth. These data-driven intervention measures have become key tools for maintaining competitive advantages and sustainable business development.
By integrating transaction data with behavioral data, artificial intelligence analysis, and real-time intervention, operators can not only protect players from potential harm but also maximize customer value, creating a healthier, more sustainable ecosystem for the gambling industry.
