Digital technologies have magnified the extent of personalization. The combination of big data and scalable cloud computing has led to rapid advances in machine learning (ML) and artificial intelligence (AI). Using big data analytics, digital-first brands can delight their customers as if they have known them for ages. Spotify understands your musical tastes better than your sibling. Amazon knows which detergent you buy, unlike your spouse.
And this knowledge pays off. According to BCG, personalization leaders grow revenue ten percentage points faster than laggards – significant enough that $2 trillion will shift to them over the next five years.
But iGaming companies may not be in the winning cohort, as they are behind in personalization maturity according to Dynamic Yield.
Personalization maturity in iGaming
Source: Dynamic Yield
Effective personalization hinges on data. The better you know your customer, the better you can empower them to reach their goals and delight them with unique experiences. According to Dynamic Yield, 61% of iGaming operators collect and store user data, but acting on it requires time and ad-hoc resources (which are often in short supply).
AI technologies can transform what once was data overload into a sandbox for data-driven innovations, especially for personalization.
Instead of using pre-existing classifiers (inputs) and fitting them into generic software-suggested cohorts (rules), you can use algorithms for more granular classifications and predictions based on demographics, behavior, and transactional data.
Comparison of traditional programming vs machine learning
Source: ResearchGate
What AI systems do is determine the optimal rules for analyzing data (based on the provided data) and use these rules for classification, clustering, predictions, or anomaly detection.
By creating your own rules instead of following cookie-cutter ones, iGaming companies can excel in personalization and operational efficiencies.
How AI brings personalization in iGaming to the next level
iGaming companies are in a tough position. They need to balance high engagement with responsible gaming practices, seamless user onboarding with regulatory requirements, and fast payouts with anti-money laundering (AML) measures.
Striking the right balance is hard without adaptable technology. What AI brings to the table is the ability to securely link more data sources, produce unique insights, and infuse these into a wide range of business processes.
Based on our market analysis, we estimate that AI solutions can bring the most value (with the least friction) in the following areas:
Multi-tiered cohort modeling
What do a recent accounting graduate, a US-based CEO, and a middle-aged nurse have in common? They all fit into the growing demographic of online players. Over 50% of Americans earning $50K or more per year gamble online, while people in their early 20s are the fastest-growing segment of sports bettors.
These people share a hobby, but their gaming preferences and behaviors differ. Player account management (PAM) helps iGaming companies understand their audiences by aggregating gameplay, transaction, and player data, providing holistic insights into player preferences.
AI extends a new pane of analysis over the available data, enabling deeper, real-time data modeling. Unlike traditional statistical systems based on pre-programmed rules, AI models can be trained to identify new classification patterns, detect similar behavior patterns, and draw correlations between gamers’ actions. This allows for the construction of multi-tiered models to analyze:
- Player onboarding behavior. Consider registration method, registration location, and first deposit amount to localize the gaming experience and estimate potential long-term value.
- Engagement frequency. Monitor engagement over time to evaluate retention and predict churn risks at early stages based on periods of inactivity.
- Spending patterns. Evaluate spending habits across player segments (casual gamers, high rollers, VIPs, etc.) to ensure responsible gaming and prevent fraud.
- Game preferences. Deliver personalized game suggestions, loyalty rewards, bonuses, and new content recommendations to maintain high engagement.
- Lifetime value (LTV). Gain accurate LTV data for different segments to prioritize your retention efforts and optimize customer acquisition strategies.
Intellias recently helped EveryMatrix deploy a new user analytics platform. The machine learning recommendation system performs cohort analysis and provides game recommendations, churn predictions, and fraud analysis with 90% accuracy. The high-load cloud architecture allows the platform to process over 1 million messages per second and over 100 TB of data. The new system resulted in a tangible sales increase.
Targeted engagement
Do your marketing campaigns often miss the mark? Do players register but withdraw a few weeks later leaving a $300 to $800 customer acquisition cost (CAC) in their wake? If so, why?
Likely because you’re using engagement strategies in the aggregate, instead of personalizing your pitches for specific player segments. Almost 40% of iGaming companies use limited personalization across channels, mainly due to limited data, lack of system integration, and limited experience.
AI solutions have great scalability potential and can be embedded into various marketing and retention processes when architected correctly. In the case of EveryMatrix, the analytics system is integrated into the company’s business intelligence platform, providing real-time data visibility to different teams. A/B testing tools have been included for benchmarking marketing campaigns and conducting conversion rate optimization (CRO) experiments.
Let’s consider another example. Lottomart used an AI-powered CRM from Fast Track to create automated lifecycle engagements personalized to a gamer’s journey stage. For example, new players receive an automated sequence and an offer to return to play after a period of inactivity. Other players get personalized communications based on their preferences and activity. With the new system, Lottomart automated a range of engagement activities, doubling its active player base in four months.
Beyond engagement, AI can also ensure responsible gaming through analysis of account-based player data. Pretrained algorithms can detect problematic behaviors and adjust controls: reduce deposit limits, suggest self-exclusion, or connect the player with a support agent for gambling addiction assistance.
Responsible players are loyal. According to research published in the International Journal of Mental Health and Addiction, gamblers who set voluntary spending limits had a greater chance of remaining active and loyal than those who didn’t. OpenBet recently acquired Neccton, a gaming analytics platform developed by Dr. Michael Auer and Andreas Schneeberger, experts in responsible gaming. Integrated into OpenBet’s platform, the AI-powered solution monitors player behaviors against customizable responsible gaming thresholds.
Investing in a new responsible gaming system is cheaper than paying hefty fines for responsible gaming and AML failures, which were up by 42.3% last year, costing iGaming businesses over $442.6 million. The Intellias iGaming product development team would be delighted to help you find the right balance between engagement and compliance using advanced analytics solutions.
AI-driven gameplay experiences
A gaming engine is the core element of every iGaming platform, bringing exhilaration to every player. Infusing AI into gaming dynamics can create even more entertaining gaming experiences.
ESPN Fantasy Football uses neural networks and natural language processing (NLP) to enhance gameplay. Algorithms suggest personalized ratings for each player based on real-time data. For example, the platform will alert you if the waiver grade for a replacement quarterback isn’t as high as when you have an excellent quarterback on a bye week. The model provides insights when trading athletes, including grades for each to ensure a fair deal. AI tools can also analyze the pros and cons of starting a player and assess the impact of an injury on the team.
AI assistants can effectively coach players through different risk-and-reward scenarios with high precision. Machine learning models predicted match outcomes correctly 70% of the time, compared to the bookies’ 65%.
NetBet Sport, for example, recently launched a generative AI assistant that provides instant information on betting rules, event details, and current odds. Using a retrieval augmented generation (RAG) approach, the app delivers relevant real-time insights and helps users place the best bets. At Intellias, we have also successfully used AI for compiling odds and customizing RTP percentages, helping operators differentiate themselves through a more attractive player experience.
Google recently presented a Scalable Instructable Multiworld Agent (SIMA) that can follow natural language instructions to carry out tasks in 3D video game settings. Unlike other models, SIMA uses keyboard and mouse outputs to control a game’s character, making it adaptable to various games. These AI advancements open an even wider vista of opportunities for enhancing gameplay and user experiences.
Data-driven product development
Every product manager hits a plateau at some point. The product is doing relatively well, but new features and improvements don’t quite tilt the scales, and no one knows why. Sound familiar?
In that case, you might need to improve your approach to product thinking. In short, product thinking is all about asking “why”: Why do users need this feature? Why improve this process?
Answers to these questions should be rooted in data, which AI analytics systems supply rapidly and in ample quantity. By applying a data-driven product thinking framework, our team helped a fantasy football company develop collaborative gaming experiences. Personalized Leagues became a premium feature that drove over 20,000 new players within the first year. Newly acquired users were highly engaged and had a higher LTV than existing players.
AI models can help product teams use a wider range of structured data (e.g., player data from PAM) and unstructured data (e.g., public reviews, complaints in support tickets) in product development, avoiding the delays always present in a manual feedback loop.
Our engineering team helped another client build a GenAI application for monitoring brand and marketing campaign performance. The tool relies on an LLM to pre-process and summarize incoming unstructured data from all over the internet. The NLP model then conducts clustering and sentiment analysis to evaluate each brand mention.
Sample architecture for a GenAI brand tracking app
Instead of manually chasing mentions, users can review aggregated brand reputation as a smart score and investigate individual mentions. The app also automatically identifies the best media resources for favorable media coverage, streamlining employees’ efficiencies.
Enhanced fraud prevention
Mobile casinos and betting platforms lost over $1.2 billion to fraud between 2022 and 2023 according to Sumsub’s 2024 iGaming Fraud Report. Even more worryingly, Sumsub found that fraud volumes grew 64% year over year.
Security perimeters need to be tightened, but some companies hesitate due to potential negative effects on gaming experiences. Fortunately, you can keep high sign-on bonuses and rewards for genuine users, not fraudsters, with tech upgrades to your security systems.
Machine learning has strong credentials for reducing false positive rates in banking transactions. It can also combat chargeback, bonus, and affiliate fraud in iGaming. Underdog Fantasy, for example, went with Sift’s machine learning to identify high-risk transactions in real time. Trained on over 1 trillion annual events, the system detects stolen payment methods and fraudulent chargebacks with an ultra-low rate of false positives.
Malicious bots are a menace for iGaming companies, giving fraudsters an unfair advantage and reducing the winning odds for legitimate players. Traditional rule-based systems fail to detect them, but AI-powered player behavior analytics can.
Maltese Gaming Innovation Group has showcased a real-time prescriptive solution for fraud detection. The model predicts fraudulent player behavior with 84.2% accuracy and automatically blocks users. An explainable AI framework provides the reasoning for each block to a human agent who can review blocks and lift inappropriately assigned limits.
EvenBet Gaming is one company that has invested in new technology for bot detection. Powered by Fingerprint JS Pro technology, anti-fraud verification processes, and behavioral analytics, the tool runs in-play inspections of poker player moves, evaluating factors like hand strength, knowledge of opponents’ cards, session duration, location, and jackpot hunting to distinguish between normal player behaviors and bot fraud.
If you too are looking to implement comprehensive AML procedures and real-time fraud detection capabilities, the Intellias team can help you infuse best practices from the financial services sector.
Seamless customer support
They say Las Vegas never sleeps, and AI agents can be available around the clock to provide seamless player support. In iGaming, players expect fast, effective, and personalized issue resolution. The ability to excel in customer support translates to higher satisfaction and loyalty: 40% of players name “excellent customer support” as one of the most important factors in their loyalty to an iGaming platform
AI can increase automation and deflection, helping optimize both agent-led and self-service processes. Unlike the first chatbots, the new generation of AI agents can provide highly contextualized replies, look up information in corporate databases and around the web, and automate some basic transactional tasks.
With RAG-powered search and analytics capabilities on the back end, GenAI agents empower players to complete self-service tasks such as:
- Reviewing platform policies
- Looking up and adjusting limits
- Making deposits and withdrawals
- Mastering gaming rules
- Entering self-exclusion programs
With AI handling trivial cases, human agents can prioritize work that requires a human touch, such as talking players through technical issues or explaining responsible gaming rules.
AI in customer support has already proven beneficial to gaming companies:
Source: Zendesk.
Regulatory compliance
The iGaming sector faces tight and constantly changing regulations. Staying compliant across all jurisdictions can be as difficult as typing an email without using the letter E.
About 40% of iGaming operators need 6 to 12 months to fulfill compliance requirements before entering a new territory, while 30% need 3 to 6 months according to an ID Now survey. Source of fund checks are a key compliance challenge globally. US operators cite challenges in dealing with differing jurisdiction requirements, which is understandable given the relatively new state of such legislation.
AI solutions can assist compliance teams by providing automated checks, the latest change summaries, and localized regulatory insights. Intellias recently launched Compl-AI, an AI system for monitoring regulatory changes and performing business impact assessments.
Customizable for various domains, Compl-AI can help iGaming companies:
- Validate operating procedures against current regulations to better understand compliance levels or prepare for market expansion.
- Receive a digest of the latest regulatory developments in the industry with benchmark comparisons to stay ahead of changes.
Delegating compliance to an AI agent that tracks regulatory changes around the clock could enable your product team to get to market faster with fewer risks. Quickly interpreting changes is a key step to ensuring responsible gaming.
Betting on AI is betting on the future
The iGaming market is ultra-competitive. You can bet on your brand or bigger bonuses, but neither will hold you atop the scoreboard without more targeted investments in personalizing the player journey. After all, that is what 80% of iGaming players actively seek out.
AI can help iGaming companies optimize players’ experiences at every virtual touchpoint, from account registration and game selection to bet optimization and payout processing. At every stage of the funnel, AI algorithms can add value, improving customer retention, engagement, and lifetime value (and keeping their gambling habits in check).
The Intellias team will be delighted to help you identify the most promising customer-driven use cases for AI and create the right architecture for delivering personalization at scale. Contact us for a consultation.
