AI, machine learning, and the human touch in iGaming

AI is no longer the future. AI is here. In the iGaming world, machine learning has become the benchmark. Operators and affiliates are expected to use this technology to build efficient player conversion journeys. However, AI has had some interesting side effects on the iGaming industry, moving the emphasis away from data and statistics and back to the personal experience.

AI is the new baseline

Before the emergence of AI, affiliates spent significant time building a successful campaign through data analysis, campaign setup, and optimization. Something like 30% work in a campaign went into this phase. With AI, that can now be done in the blink of an eye.

That hasn’t removed 30% of the work for affiliates because everyone uses these machine learning tools. That 30% has become the new floor, the new standard for advertising. Companies now need to build another 100% from that floor, effectively providing 130% of what was possible before AI.

What makes a campaign stand out now is all the work done after AI. The baseline has changed, and everyone has to build from there.

The AI paradox

One interesting development seen with the emergence of AI in campaigns is that it’s pointed us to provide more personal campaigns rather than the robotic, data-driven campaigns you’d expect.

Affiliates and operators can stop focusing on data-driven approaches as machine learning can handle this, and spend more time adding the human touch to the customer journeys and digital marketing.

While this will only grow as time goes on, several campaigns have already been built around social connections and the human touch, thanks to AI freeing up that space.

Adding human stories to March Madness

During the last March Madness competition, AI was used to collate data, analyze results of each tournament day, and make predictions for the next day. With so many programs coming from across the US competition, making accurate predictions based on data was incredibly time-consuming. Now? Machine learning has it covered.

But something was missing from the campaign, and we could use that free time to add the human and personal stories of March Madness. The tournament is, after all, about college students thrust into the national spotlight, sometimes out of nowhere.

Those stories, backed by machine learning-provided data, are what captured attention. We noticed a huge increase in engagement on our platform from this campaign, and there is evidence that shows AI is adding the human touch back to marketing.

Testing casino sounds

Sounds are often an underappreciated element of game design, but one iGaming brand we worked with put it at the forefront of development. To improve its customer experience, we assisted the company in using AI through acquisition channels to track the engagement and preferences of users for soundtracks played through game sessions.

Using machine learning to track patterns of human preferences helped build a stronger product and created a more personalized product.

Enhancing the social side of fantasy sports

Daily Fantasy Sports (DFS) are all about the community and players competing with each other. However, it is also data and statistics-intensive. Until AI was introduced, many operators we worked with focused on campaigns emphasizing data, risk management, and statistics.

Now, our partners are working closely with us to learn how to add the human and social experience to campaigns for DFS. Rather than enhancing data tools, which machine learning can handle, we’re helping to build better social features and user engagement tools for these platforms. Campaigns are focused on that as well, rather than just the team-building mechanics and the data available to help with that.

Summary