Operators are having to try harder than ever to acquire and retain customers. With so much competition, adopting the right business strategy is key and A/B testing is part of this mix.

Casinos and sportsbooks have access to more customer data than ever, while AI technology can now be utilized to consolidate this information and spot patterns. This means that operators now have clearer insights as to what works and what doesn’t, while there are new opportunities to assess strategies in line with KPIs.

A/B testing is integral to GamePark as it allows our clients to experiment with marketing strategies with minimal risk. By comparing two scenarios in real-time, operators can get greater insight into their customers, helping them to make stronger business decisions.

What Is A/B Testing?

A/B testing is where you compare two different versions of something – for example a casino lobby or marketing strategy – to see which one performs best with a specific target audience. This, of course, varies from audience to audience so the ability to perform multiple A/B tests is very valuable.

The aim is to work out which option is best for increasing engagement and reducing churn. This is usually done with small samples and can then be expanded out as part of a company’s overall marketing strategy. By doing this, you will be able to increase personalization based on raw real-time data from actual customers.

Personalization and Casinos

With so much competition, one of the main KPIs for marketing managers is to increase engagement and reduce churn. This not only ensures players keep playing at your casino, but it also means they are less likely to be tempted by your rivals. GamePark allows you to test various strategies with minimal risk.

With data-driven insights, AI can be used to segment audiences. These can then be targeted with personalized marketing, both in the lobby and across all marketing touchpoints.

Most casinos only segment audiences based on core demographics like age or territory. This means that most casino lobbies end up being rather samey. GamePark is different. You can break down your audience in a variety of ways including ‘average time spent playing’ on a game or ‘average deposit amount placed’ then make personalized suggestions based on a user’s previous activity.

An A/B test allows you to assess the average player journey per segment then infer the behavior of similar customers. This then increases the chances of providing personalized recommendations of interest.

An A/B Testing Example – Two Game Studios

Say you have games from two different studios. From Studio A, you retain 30% of all profits. From Studio B, you retain 40% of all profits. However, Studio A is more well-known and thus is more likely to drive higher engagement.

Through A/B testing, you can see what happens when you promote Studio B more heavily than Studio A, using a control group as your base line. You can then see in real-time how this could affect your profits. This reduces business risk as you can use this real-time real-world data to work out whether you should promote Studio B more to a specific customer segment or across your entire customer base.

Conclusion

A/B testing is an integral part of the GamePark ecosystem. You can drive growth through data-driven decision making by experimenting with prompts, messaging, and the recommenders themselves. You can see what works and what doesn’t when compared to a default group, allowing you to optimize player experience in line with your KPIs.

If you would like to find out more about our A/B testing and GamePark as a whole, please get in touch and ask for a demo.