Unlock Infinite Possibilities: Introducing InfinityAI!

Discover Now

Ultimate Guide: How Gamification, AI Models, and InfinityAI Are Changing iGaming

The right AI models can revolutionise player experience, increase retention, and increase revenue. Just imagine the possibilities they unlock for your iGaming venture – tailored strategies, informed decisions, unparalleled player satisfaction, and more await. With AI, every move is smarter and every win is bigger! 

In this post, we will introduce you to InfinityAI which can be used for modelling in iGaming.

Adaptive Models

Adaptive models aren't your run-of-the-mill, static algorithms that get outdated faster than last season's game meta. Nope. You can take advantage of them, offering the kind of flexibility and precision that makes sure your strategies are sharp. 

The Adaptive AI technology automatically evaluates multiple models to find the best-performing model for your unique data set, use case and goals (1:1 approach). At scale and at pace.

Yes – these models learn from your data, evolve with your challenges, and make sure that you're not just keeping up but setting the pace.

Ready to see how these smart models can transform your approach, from player retention to optimising your next big campaign? Let's level up your game with adaptive models that promise to keep you at the top of your game.

Predict player behaviour using advanced machine learning models:

#1 Time-series forecast model

The time-series forecast model predicts future iGaming trends by analysing historical data. It focuses on understanding past player behaviour, game popularity, and transaction volumes to forecast future events. With its insights, iGaming businesses can plan more effectively, optimising operations and increasing profitability in a competitive market.

Use cases in iGaming
  • Predicting player activity on online gaming platforms is essential for adjusting server capacity and ensuring customer support resources are adequately allocated. Accurate player activity can give operators an edge when planning their campaigns – helping with engagement and retention. 
  • Estimating the future popularity of free-to-play games enables targeted resource allocation and marketing efforts. Understanding which games will attract more players aids in prioritising updates and promotions.
  • Projecting new user sign-ups helps refine marketing strategies and budget allocation. Then, accurate predictions of user acquisition trends guide promotional activities and partnerships.
  • Forecasting revenue from expected bets and transactions supports financial planning and investment decisions. Knowing potential revenue streams allows for more strategic budgeting and resource allocation.

#2 Binary Classification

The binary classification model is a key component of iGaming analytics. It is designed to make predictions on two possible outcomes. It relies on historical data where the outcomes are clearly labelled, making it possible to learn patterns and predict future events.

Such a model is instrumental in identifying player behaviours and outcomes, helping iGaming businesses tailor interventions and enhance player experiences. Through its predictive power, operators can improve engagement strategies to a previously unknown level.

Use cases in iGaming
  • Predicting player churn as the main one. It allows for targeted interventions, such as personalised offers or engagement tactics, to keep players engaged.
  • Estimating the risk of self-exclusion helps in creating preemptive support measures. Identifying players at risk allows for timely support and responsible gaming interventions, fostering a safer gaming environment.
  • Assessing conversion probabilities to different products enables more effective cross-selling strategies. Marketing efforts and promotional allocations are guided by determining which players are likely to engage with new features or games.
  • Determining the likelihood of a player completing a deposit process after starting it can improve payment system efficiencies. Insights into potential drop-off points allow for system optimisations – and this can both reduce friction and increase completion rates.
  • Predicting the success of promotional campaigns in achieving desired player actions informs marketing effectiveness. Analysing campaign data to forecast outcomes helps refine promotional strategies so that resources are invested in the most impactful initiatives.

#3 Multi-class classification model

The multi-class classification model extends the predictive capabilities of iGaming platforms by categorising players into more than two distinct groups. 

This approach uses historical data with predefined labels to identify patterns that assign new or existing players to specific value tiers or segments. On top of that, it facilitates targeted marketing and customised player experiences.

Using this model, iGaming businesses can refine their strategies to better meet the needs and preferences of different player groups.

Use cases in iGaming
  • Segmenting players into value tiers helps tailor rewards and bonuses. Recognising the spending patterns and engagement levels of players allows for customised incentives that match their contribution to the platform – optimising retention efforts and maximising revenue.
  • Identifying players most likely to enjoy certain game types guides content personalisation. Analysing gameplay data to segment users based on their preferences ensures that recommendations and game offerings are relevant and engaging.
  • Customising marketing messages according to player segments enhances communication effectiveness. A better understanding of each segment makes it possible to design marketing campaigns that resonate with targeted groups more deeply. Needless to say it can positively affect conversion rates. 
  • Optimising customer support by prioritising high-value player segments ensures efficient resource allocation. By knowing which players are worth the most, support teams can focus their efforts on retaining and satisfying players.
  • Enhancing VIP management programs through precise segmentation improves player loyalty. Accurately categorising players into different levels of VIP status based on their behaviour and value ensures that VIP programs are effective and appreciated.

#4 Regression model

With regression modelling in iGaming analytics, you can predict continuous numerical outcomes based on a variety of variable, such as amount wagered, player age, or previous lifetime value (LTV), the model can forecast key metrics like lifetime value, the total number of bets, transaction volumes, gross gaming revenue (GGR), and bonus utilisation. As a result, iGaming operators are better able to optimise player engagement and financial planning, as well as their overall business strategy – all while making precise, data-driven decisions.

Use cases in iGaming
  • Forecasting a player's lifetime value provides insights into long-term profitability from individual players. Accurate LTV predictions help tailor engagement strategies and allocate marketing resources more effectively, ensuring efforts are focused on players with the highest potential return.

  • Predicting the total number of bets a player will place enables operators to gauge engagement levels and gambling behaviour. This information is crucial for managing risk, tailoring player experiences, and developing strategies to increase betting frequency and volumes.

  • Estimating the number of transactions players will initiate helps in understanding their activity patterns. Insights into transaction frequencies aid in optimising payment processes and identifying opportunities to encourage more frequent deposits or game plays.

  • Calculating expected gross gaming revenue (GGR) from players aids in revenue forecasting and financial planning. Forecasting GGR enables operators to plan for growth, allocate resources, and manage budgets more effectively.

  • Assessing bonus usage patterns among players assists in optimising promotional strategies. Understanding how different players utilise bonuses enables the design of more effective promotions, enhancing player satisfaction and loyalty while ensuring bonus spending is efficient and targeted.

#5 Customer lifetime value model

The customer lifetime value (CLV) model in iGaming is dedicated to quantifying the total value a player brings to the platform over their entire relationship. In addition to predicting whether a customer will remain active, it forecasts their next purchase date and lifetime value. 

Predictive insight drives player experiences, optimises marketing spend, and prioritises customer engagement. The CLV helps iGaming operators nurture the most profitable relationships, enhance customer satisfaction, and, in turn, maximise long-term revenue.

Use cases in iGaming
  • Determining the likelihood of a player remaining active helps in identifying those who are at risk of churning. Operators can deploy targeted retention strategies to re-engage these players, extending their lifecycle and preserving their value to the business.
  • Forecasting when (and if) a player will make their next deposit or purchase enables timely and relevant marketing communications. By predicting purchase dates, operators can create personalised offers that encourage continued engagement and spending at critical moments.
  • Estimating a player's total lifetime value guides resource allocation across marketing, customer service, and loyalty programs. With a clear understanding of a player's potential value, investments can be directed towards those most likely to drive long-term profitability.
  • Tailoring loyalty and reward programs based on predicted CLV ensures that the most valuable players receive the recognition and incentives they deserve. This fosters loyalty among high-value players, encouraging them to continue their engagement and spending.
  • Optimising customer acquisition strategies by focusing on attracting players with high potential lifetime value. Analysis of high CLV players can be used to refine acquisition efforts, improving the overall quality of the customer base and enhancing revenue growth.

Automatically segment your audience based on different attributes with these models:

#6 General segmentation

This model analyses player data, such as days since last bet, days since last deposit, average deposit amount, and average stake, to create distinct player groups. 

These segments can then be targeted with tailored strategies to maximise engagement, retention, and revenue. Players' diverse behaviour and preferences allow iGaming businesses to deliver more personalised experiences, improve customer satisfaction, and promote loyalty.

Use cases in iGaming
  • Creating tailored marketing campaigns for players based on their activity levels. Using segmentation to identify players who haven't bet in a while, operators can develop re-engagement campaigns specifically designed to draw them back into the game.
  • Designing personalised loyalty programs for different segments. Understanding the average deposit and stake levels of various segments allows for the creation of loyalty rewards that resonate with each group's spending habits, enhancing the perceived value of the rewards and loyalty program participation rates.
  • Optimising the customer support experience by prioritising segments based on their value or risk level. Players identified as high-value through their deposit and betting patterns can receive prioritised support, ensuring their issues are resolved quickly, which helps maintain their loyalty and satisfaction.
  • Enhancing product offerings by identifying and focusing on the preferences of different segments. Analysis of segments based on betting patterns and preferences can inform the development of new games or features tailored to the interests of specific player groups, driving higher engagement and satisfaction.
  • Refining risk management and responsible gaming strategies by segmenting players based on risk indicators, such as frequency and amount of bets. This enables operators to proactively address responsible gaming concerns by providing targeted support and interventions to those who may exhibit risky gambling behaviours, promoting a safer gaming environment.

#7 RFM segmentation model

RFM segmentation in the iGaming industry involves categorising players based on three key metrics: 

  • Recency (how recently a player has engaged with the platform), 
  • Frequency (how often a player engages), 
  • Monetary value (the financial worth of a player to the business). 

Using this model, iGaming operators can tailor their strategies across different segments to effectively engage and value players. Businesses can enhance customer satisfaction, retention, and revenue, all through differentiating players based on these dimensions.

Use cases in iGaming
  • Personalising promotional offers to match the engagement level and value of different player segments. Players who score high on all three RFM metrics can be targeted with exclusive rewards, encouraging further engagement and loyalty.
  • Identifying and re-engaging lapsed players through targeted campaigns. For example, by focusing on players with high Monetary and Frequency scores but low Recency scores, operators can craft specific re-engagement strategies to encourage their return.
  • Optimising loyalty programs by segmenting players according to their RFM scores. This allows for the development of tiered loyalty schemes that reward players in a way that correlates with their engagement and contribution to the platform.
  • Enhancing customer service by prioritising support based on RFM segmentation. High-value players, indicated by high scores across all three metrics, can be given priority in customer service queues, ensuring their issues are addressed promptly and efficiently.
  • Improving product and game development strategies by analysing the preferences and behaviours of top RFM segments. Understanding the games and features favoured by the most valuable and engaged players can guide the development of new offerings that are more likely to succeed.

#8 New vs Returning model

New vs. Returning is an important model for understanding player dynamics in iGaming analytics. This segmentation lets operators measure growth, loyalty, and engagement trends over different periods, such as daily, weekly, monthly, or yearly. The analysis of new versus returning players can help iGaming businesses improve their acquisition efforts and increase overall lifetime value of players.

Use cases in iGaming
  • Tailoring welcome offers and onboarding experiences for new players. Operators can create customised introductory offers and guidance to enhance initial experience and long-term loyalty among new players. 
  • Developing targeted retention strategies for returning players. Understanding the patterns and preferences of returning customers enables the creation of personalised re-engagement campaigns and offers that resonate with their specific interests.
  • Measuring the effectiveness of marketing campaigns and acquisition channels. Comparing the volume and value of new versus returning players over specific periods, operators can assess the performance of their marketing efforts and adjust them.
  • Optimising the customer experience based on player lifecycle stage. Differentiating between new and returning players allows for the delivery of customised content, promotions, and support, ensuring that each player's experience is tailored to their stage in the customer journey.
  • Analysing player loyalty and churn rates. Tracking the proportion and performance of new versus returning players over time provides valuable insights into loyalty trends and potential churn risks, enabling proactive measures to improve player retention.

#9 Cohort model

The Cohort model in iGaming analytics segments players based on their first purchase date, giving a clear view of customer behaviour over time within defined groups. 

In this way, engagement, retention, and value can be compared across different acquisition cohorts. These cohorts help iGaming operators gain deep insights into customer lifecycle management, marketing strategies, and onboarding processes.

Use cases in iGaming
  • Assessing the impact of onboarding experiences across different cohorts. Operators can evaluate how initial engagement strategies affect long-term player retention and adjust onboarding processes to maximise player loyalty from the start.
  • Evaluating the long-term value of players acquired through specific marketing campaigns. In terms of player lifetime value, the effectiveness of different acquisition strategies can be measured by segmenting players by their first purchase date.
  • Identifying trends in player churn over time. Cohort analysis provides insights into when players from specific acquisition periods are most likely to leave. And this analysis allows operators to intervene with targeted retention strategies at critical points in the customer lifecycle.
  • Enhancing loyalty programs based on cohort behaviour. Understanding how different cohorts engage with loyalty incentives over time helps in tailoring these programs better to meet the evolving preferences and needs of players.
  • Optimising product and game offerings for specific cohorts. Analysis of how different acquisition cohorts respond to various games and features enables operators to refine their product development and marketing strategies.

#10 ABC analysis model

ABC analysis in iGaming is a strategic approach that categorises products or services based on their importance to the financial success of the business. 

This model divides offerings into three categories: 

  • A (most valuable), 
  • B (moderately valuable), 
  • C (least valuable). 

In order to maximise revenue and profit, iGaming operators need to identify which sports, markets, bet types, and casino games contribute most to revenue and profit. ABC analysis helps with that, ensuring that the most impactful elements receive the highest priority.

Use cases in iGaming
  • Prioritising marketing and promotional efforts towards 'A' category games and products. Operators can maximise return on investment by focusing on the offerings that generate the most revenue.
  • Streamlining product offerings by identifying and possibly phasing out 'C' category services that contribute minimally to financial success. This allows for a more focused and efficient product portfolio that better serves the core audience.
  • Enhancing user experience and engagement on high-value 'A' category offerings. In order to boost satisfaction and loyalty among their most profitable customers, operators should improve the quality and access of top-performing games and betting options.
  • Informing product development and innovation strategies. ABC analysis reveals which types of offerings are most valued by customers, guiding the development of new products and services that align with these preferences.
  • Optimising inventory and resource management based on the categorisation of offerings. For physical products or limited resources like VIP support, focusing on 'A' category items ensures that the most critical aspects of the business are always well-supported and available to customers.

Plug & Play Models

Through Plug & Play Predictions, operators can maximise campaign success and improve personalisation levels. Easily deploy ready-made models to identify high-value players, prevent player churn, and more to increase player retention and lifetime value. 

#1 Churn prediction

Churn prediction models in iGaming leverage machine learning to identify players at risk of leaving the platform. 

Through analysing player behaviour, transaction history, and engagement patterns, these models can predict which players will churn. Based on this insight, operators can intervene proactively with targeted strategies aimed at keeping the players. 

Use cases in iGaming

  • Player churn prevention. Early identification of potential churners enables the deployment of personalised messages offering bonuses, free spins, or other incentives. Tailoring these offers to individual player preferences maximises their effectiveness in preventing churn.
  • Preventing early life player churn. Focusing on players who show signs of disengagement within their first 30 days is really important in iGaming. Operators can improve early engagement and long-term retention by targeting these players with customised campaigns highlighting the platform's value and entertainment potential.
  • Preventing high-value players churn. High-value players contribute disproportionately to revenue, making their retention a priority for operators. Combining churn prediction with lifetime value (LTV) prediction models identifies these valuable players before they disengage. Then, offering them exclusive rewards and personalised attention ensures they feel valued.
  • Custom engagement strategies. Beyond standard retention tactics, understanding the specific reasons behind each player's risk of churn enables the creation of highly customised engagement strategies. 
  • Dynamic retention offers. Machine learning models can continuously update churn predictions based on new data so that the operators can refine and adjust their retention offers in real-time. 

#2 LTV prediction model

LTV (Lifetime Value) prediction models in iGaming utilise advanced analytics to forecast the future value of players to the business. 

The models provide important insights for strategic decision-making by estimating how long players will remain active, the number of bets they will place, and the total amount they will wager. This information can be used by operators to tailor bonuses, messages, and rewards to each player's predicted value. 

Use cases in iGaming

  • Tailoring bonus amounts. Operators can assign bonuses proportional to the LTV of each player by predicting their LTV. As a result, the high-value players receive rewards that reflect their importance, which encourages them to continue to engage and stay loyal to the operator.
  • Forecasting player activity. LTV models can predict the likelihood of player activity over various time frames, such as the next 7, 30, 60, 90, or 365 days. Operators can use this insight to identify inactive players and target them with re-engagement strategies.
  • Predicting betting behaviour. Understanding the expected number of bets from players within specific time frames allows operators to plan for game availability, customer support, and liquidity management. 
  • Revenue forecasting. LTV prediction models enable more accurate revenue forecasting by estimating the total spending of customers over different periods. 
  • Customised player engagement. Insights from LTV predictions allow for highly personalised communication strategies. Operators can craft messages and offers that resonate with individual player's behaviours and preferences.

#3 Dynamic RFM segmentation

RFM segmentation uses real-time data about recent purchase behaviour, transaction frequency, and overall spending to automatically divide the customer base. In this manner, operators can understand their players on a deeper level, categorising them based on their engagement and value. This dynamic insight facilitates stronger, more personalised relationships with customers and increases their loyalty.

Use cases in iGaming

  • Personalised marketing campaigns. Leverage dynamic RFM segmentation to deliver marketing messages that resonate with each player segment's unique characteristics. Tailoring offers based on recent behaviour and spending ensures higher engagement and conversion rates.
  • Optimised loyalty programs. Design loyalty programs that reward players based on their RFM segment. Players with higher frequency and monetary value can receive more substantial rewards, incentivising continued engagement and spending.
  • Enhanced player experience. Use RFM insights to customise the gaming experience for different segments. For example, high-value players might get access to exclusive games or early releases, while frequent players could receive bonuses for their loyalty.
  • Strategic resource allocation. Operators can allocate resources more efficiently by understanding which segments contribute most to revenue. Focus development efforts on features that appeal to the most lucrative segments or prioritise customer support.
  • Real-time segmentation for real-time engagement. The dynamic nature of RFM segmentation allows operators to adjust their strategies in real time, responding to shifts in player behaviour with agility. As a result, marketing efforts are always aligned with the current state of play.

InfinityAI – Unified Solution for Boosting Gamification with AI

InfinityAI is the premier solution for iGaming operators who aim to leverage the power of artificial intelligence to predict and influence player behaviour, value, and preferences with precision. 

It uses Adaptive AI technology, which automatically selects the most effective model tailored to an operator's unique data and use case – allowing for real-time customisation and optimisation of AI models for unmatched results.

Key Features of InfinityAI

Tailored Intelligence with Adaptive AI

InfinityAI's Adaptive AI provides operators the flexibility to adapt AI models specifically to their data. Such capability allows operators to address niche business needs and swiftly adapt to evolving player behaviours. 

Speed Meets Accuracy with Plug & Play AI

For operators seeking immediate results, InfinityAI’s Plug & Play models offer a quick and efficient solution. These models are pre-configured for instant deployment and rapid implementation – without the need for deep technical knowledge.

Transparency with Explainable AI (XAI):

With Explainable AI, InfinityAI demystifies the AI decision-making process. With this transparency, operators can understand the reasoning behind predictions, all with confidence and trust in the AI. This feature is particularly valuable in managing and adjusting the most complex models.

Actionable AI: Insights into Impact:

InfinityAI’s capability extends beyond simple data analysis to facilitate real-world applications. It empowers operators to launch sophisticated, predictive, omnichannel marketing campaigns and gamification strategies. With its Single Player View, operators can initiate personalised player journeys for better retention.

Final thoughts

In the iGaming world, AI models make tackling complexity not just manageable, but truly exciting. With these smart solutions, you can improve your retention strategies, increase player engagement, and drive even more growth. 

It's no longer just about keeping up with an industry that's constantly evolving, but instead, setting the benchmark.

So, as you move forward, carry with you the insights and inspiration from these advanced models to truly revolutionise the iGaming experience you offer.

Our Latest Blogs

Ridiculus Parturient Nibh Fermentum Pellentesque

feature-img
Leveraging Free-to-Play to Enhance Cross-Selling in Online Gambling with John Smith
The online gambling market is highly competitive, and operators need to constantly come up with new ways to stay relevant. Free-to-Play (F2P) experiences are a smart strategy in this environment. F2P
Read more
feature-img
Ultimate Guide: How Gamification, AI Models, and InfinityAI Are Changing iGaming
The right AI models can revolutionise player experience, increase retention, and increase revenue. Just imagine the possibilities they unlock for your iGaming venture – tailored strategies, informed
Read more
feature-img
The Xtremepush Use Case Series: Cart Recovery for the Sports Betting & Gaming industry
For the past two weeks, Xtremepush have been bringing you videos from their new four-part business use case series. This weeks video showcases how online cart recovery can be achieved in the Sports
Read more

Get the latest Updates