{"id":82716,"date":"2024-11-19T10:12:59","date_gmt":"2024-11-19T09:12:59","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=82716"},"modified":"2025-11-13T13:30:09","modified_gmt":"2025-11-13T11:30:09","slug":"personalization-in-igaming-why-ai-is-your-best-bet","status":"publish","type":"blog","link":"https:\/\/intellias.com\/role-of-personalization-in-igaming\/","title":{"rendered":"Personalization in iGaming: Why AI Is Your Best Bet"},"content":{"rendered":"

Digital technologies have magnified the extent of personalization. The combination of big data<\/a> and scalable cloud computing<\/a> has led to rapid advances in machine learning (ML) and artificial intelligence (AI)<\/a>. 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.<\/p>\n

And this knowledge pays off. According to BCG<\/a>, personalization leaders grow revenue ten percentage points faster than laggards \u2013 significant enough that $2 trillion will shift to them over the next five years.<\/p>\n

But iGaming companies may not be in the winning cohort, as they are behind in personalization maturity according to Dynamic Yield<\/a>.<\/p>\n

Personalization maturity in iGaming<\/strong><\/p>\n

\"Personalization<\/p>\n

Source: <\/em>Dynamic Yield<\/em><\/a><\/p>\n

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).<\/p>\n

AI technologies can transform what once was data overload into a sandbox for data-driven innovations, especially for personalization.<\/strong><\/p>\n

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.<\/p>\n

Comparison of traditional programming vs machine learning<\/h4>\n

\"Comparison<\/p>\n

Source: ResearchGate<\/a><\/em><\/p>\n

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.<\/p>\n

By creating your own rules instead of following cookie-cutter ones, iGaming companies can excel in personalization and operational efficiencies.<\/p>\n

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Build a seamless and personalized user experience with iGaming platform development services by Intellias. <\/p>\n

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<\/div>\n <\/div>\n <\/div>\n Learn more<\/span>\n\t\t <\/a><\/div>\n

How AI brings personalization in iGaming to the next level<\/h2>\n

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.<\/p>\n

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.<\/p>\n

Based on our market analysis, we estimate that AI solutions can bring the most value (with the least friction) in the following areas:<\/p>\n

\"Potential<\/p>\n

Multi-tiered cohort modeling<\/h3>\n

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<\/a>, while people in their early 20s are the fastest-growing segment of sports bettors<\/a>.<\/p>\n

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.<\/p>\n

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\u2019 actions. This allows for the construction of multi-tiered models to analyze:<\/p>\n