Artificial intelligence (AI) tools are revolutionizing the way entire industries operate. The potential scope of AI is firmly in our public consciousness, whether through scare stories about the impending death of human civilization or playful experimentation with AI-generated songs. People appreciate the technology's power, even if we don't fully know its ultimate direction of travel.
In the world of online sports betting and casinos, I've noticed a spreading awareness that AI can benefit customer engagement and player protection; however, there are more than a dozen AI models that may benefit different objectives. Let's break down some of the ways businesses can introduce flexible AI tool sets to achieve their goals.
AI can have a major impact on how companies handle customer relationship management (CRM). In iGaming, AI can foster a transformative and efficient approach by allowing brands to optimize client interactions, predict player churn, target marketing campaigns and automate repetitive tasks.
Increasingly, adaptive AI offers a higher level of flexibility, speed and precision in CRM by automatically evaluating multiple models. These models can help elevate an operator's revenue, retention or player experience for a variety of use cases.
I have found that technology that combines adaptive AI with plug-and-play models, allowing immediate deployment and smart segmentation, is often ideal for operators. Those who require unique datasets and want to adjust models to meet their needs, as well as those with little technological knowledge who need ease of use and the ability to address common use cases, can benefit from software that allows bespoke adaptive solutions.
Predictive modeling uses historical data to predict future behavior or trends. It can also be used to segment players into groups for targeted marketing.
To determine the applicable prediction models for your business, you must first know the desired outcome -- is it to increase player retention, maximize lifetime value or identify problem play? Next, assess the quantity and quality of the player data available to feed into your prediction model, and ensure your team has the skills to interpret the model effectively.
If you choose to build your AI models in-house, this information can help you tailor them to individual business needs and give clients ownership of their data. However, make sure you have strong data science skill sets, data storage and processing abilities to develop this technology internally. You also have the option to use a pretrained model, which typically requires less expertise.
Time-series forecast models can predict levels of player activity on online gaming platforms to ensure scalability and 24/7 customer service availability. They can estimate the future popularity of free-to-play (F2P) games and project new user signs-up by assessing short-term trends and historical data. The models can also forecast revenue from expected bets and transactions, supporting financial planning and allowing for resource allocation.
These models predict the outcome of two or more possibilities based on player, game and marketing campaign data. In iGaming, the binary classification model utilizes clearly labeled historical data to help operators strengthen responsible gaming capabilities. The data shows if a player went on to self-exclude or display problem behaviors, with the model learning from this to predict future risks among others. This can allow you to put measures in place that encourage cool-off periods or stop problems before they occur.
You can use multi-class classification to segment players into categories of concern, such as low, medium or high risk. This lets you identify potentially risky behaviors and implement measures such as deposit limits or targeted communications. You can also classify players into categories, such as VIP, bonus seeker or casual/new player. The key to getting the best from this model is clearly defining categories relevant to your business, using high-quality data and having some level of explainability.
The regression model takes this one step further, allowing users to forecast a player's lifetime value (LTV) or their total number of bets or transactions. Use this to calculate expected gross gaming revenue (GGR) from players to aid forecasting. You can also use the customer LTV model to predict the player's value to the platform by looking at the likelihood of them remaining active and when or if they are going to make a deposit, as well as helping tailor loyalty and rewards programs.
Segmentation models group data into distinct categories to automatically classify it based on shared characteristics.
Customer segmentation can boost operator efficiency by enabling your operators to allocate resources and target players in a more focused way. Use general segmentation to analyze player data, such as the time since their last bet, their average deposit amount and their average stake; then create player groups that can be targeted to maximize engagement, retention and revenue. This data can help you personalize loyalty programs for different types of players, optimize customer support and refine risk management more proactively.
The RFM segmentation model categorizes players based on recency, frequency and monetary value, to identify lapsed customers for targeted campaigns. The new vs. returning model, on the other hand, measures growth, loyalty and engagement trends over specified periods of time. In my experience, both of these can optimize the player experience to improve loyalty and reduce churn rates.
Based on their first purchase date, the cohort model can segment players in a way that gives a clear overview of their behavior and helps gain deep insights into customer life cycle management, identify trends in churns over time and enhance loyalty through targeted player journeys.
The final segmentation model, ABC analysis, categorizes products and services based on their importance to commercial and financial success. This divides products into most valuable (A), moderately valuable (B) and least valuable (C) by streamlining product offerings, informing innovation and optimizing inventories.
When implemented effectively, AI solutions can have a significant impact on customer engagement by providing a deeper understanding of players. Operators in the iGaming industry can benefit from combining plug-and-play models, which allow immediate deployment and smart segmentation, with adaptive AI. The evaluation of multiple models within adaptive AI allows for creating a flexible and specialized approach most appropriate for any given operator. In my experience, the speed and precision offered by this combination of approaches can provide a competitive advantage. By acquiring more customers, keeping them engaged and boosting profitability, you can maximize loyalty and enhance your long-term commercial sustainability.
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