Embedding AI, ML, and Predictive Analytics in iGaming
PwC predicts that AI could add $15.7 trillion to the global economy by 2030. With AI transforming virtually every industry, this prediction is easy to believe. The iGaming industry, which deals with poker, online casinos, sports betting, and other online games, is no exception. AI solutions can improve user experience, recommend games based on user behavior and similar users’ preferences, detect fraud and bots. Let’s discuss in more detail some of possible AI applications in iGaming.
Online Gaming Chatbots for Customer Support and Small Talk
Either when encountering a problem or having a few seconds to wait, online game players will need a chatbot. In terms of customer support, AI chatbots can handle up to 75% of issues, providing quicker responses and saving operators costs. As to small talk, the level of conversation an AI chatbot is capable of sustaining can surprise you.
Big Data and Predictive Analytics for Estimating Customer Behavior
Understanding what players need and what they are going to do gives operators of online casinos and sports betting platforms chances to provide better service in many ways.
Some applications of Big Data and Predictive Analytics in iGaming:
- Improving UI - analyzing user actions and behavior in relation to a game UI furnishes information that users rarely care to give you: what they like or don’t like, where they miss the button, where they are bored, what to change in the UI. This information allows you to eliminate all hiccups, so that your game delivers only good vibes to the players.
- Predicting user activity increase and decrease - what inflow of users an online betting platform should expect before an important football match or on Christmas Eve? How many players you will have on a Friday night? When you know what to expect, you can make sure that you are ready to work at heavy hours and at idle times.
- Recommending games – combining data of a user profile and preferences of look-alike users, you can generate smart game recommendations for each user and get better results in up- and cross-sales, next product offerings, in-play game offerings, etc.
- Reducing customer churn – customer loyalty bases on their personal experience with the game and your platform. Tailoring personalized experience for each player is only possible with the help of artificial intelligence. Customized in-game rewards, unique offers, and other incentives are compiled according to what is predicted efficient for the user and provided when the user loses interest to your service.
Sentiment Analysis for Opinion Collection
Launching a new game or redesigning an old one is a risky business. What if your players don’t like it and you will lose your investments? Sentiment analysis opens up possibilities for early response to users’ dissatisfaction. Knowing what users think, you can act and style out before it’s too late.
The benefits of Sentiment Analysis lies in the fact that it gives you subjective data (the design is too boring), misperceptions, reach the users that didn’t buy the game, get feedbacks posted in different languages.
Types of questions for Sentiment Analysis in iGaming
- Why users don’t buy our game?
- What does the spike in the game popularity means?
- What do players expect from the game to be released?
- What do players love the most about a popular game?
Opinions of your users let you be on the same page with them and apply a proactive approach in your business. Show the players that you listen to them and care about their needs.
Read what we can do with AI.
Fraud and Bot Detection
Fraud threatens the reputation of your brand and deters players. Account takeovers, game hacks, fake licensing, credential ripoffs, spam, and bots are difficult to combat using conservative methods, but AI grants you the tools to change the equation. Machine Learning systems that track all the tiniest game events and detect fraud patterns in real time can put an end to malicious activity at your gaming or sports betting platform.
The advantage of Machine Learning solutions centers on their ability to learn and improve their algorithms. In comparison to static fraud detection systems, this ability keeps them afloat and impedes their bypassing by fraudsters.