iGF RoundTable: Leveraging the Power of Data, AI and Automation

iGaming businesses collect vast amounts of data from, and about, their consumers. But only the most savvy are able to leverage it to deliver analysis, insights and actionable strategies that create competitive advantage.

The use of widely available Artificial Intelligence (AI) has simplified this task. Automated systems can sift through large datasets and highlight the most relevant information for human analysis, allowing companies that follow the data trail to tap into their full potential.

In this iGF RoundTable discussion, we’re looking at how gaming companies can utilise data processing, AI and automation to give themselves that vital edge over the competition.

Joining our Expert Panel are:

  • Etienne Azzopardi, Chief Operating Officer at Swintt
  • Max Sevostianov, Chief Operating Officer at Betbazar
  • Allan Stone, CEO at Intelitics
  • Thomas Kolbabek, CTO of Golden Whale Productions
  • Karl-Jorit Hausdorf, Head of Customer Experience, Data & aaS at Fujitsu;
  • Andrea McGeachin, CEO of Neosurf

Curtis Roach, iGamingFuture’s Head of Content, was anchoring the RoundTable, and he kicked off by asking:

How can businesses read the data they gather and understand what it is saying?

Etienne Azzopardi: “Data visualisation, ‘journeys’ and storytelling all play an incredibly important role in helping businesses analyse the information being collected.

“Through these practices, an organisation can begin to build narratives that enable anyone in the company to understand a particular data point and what it could potentially mean in terms of future optimisation.

“That being said, understanding where the data is actually coming from is just as important as determining what actions a business can, or should, take to achieve a particular KPI, so it’s important not to lose sight of this.

“Before constructing any form of narrative, it’s vital for businesses to first verify that the data is accurate and relevant to the hypothesis being tested. Failing to do this could lead one into a situation where a story is made to fit with a pre-conceived outcome rather than the other way around.”

Max Sevostianov: “This is a really interesting question, and the answer to it has undoubtedly changed in the last few years due to the advancement of AI and machine learning. Previously, a company would have had to painstakingly go over all the relevant data they had on a specific subject and make an educated guess as to how that information could be used to drive improvement going forward, but these days AI can analyse huge data sets that were previously too large or convoluted to break down manually.

“While there’s still definitely room for human interpretation, using AI to predict, for example, future player behaviour may enable you to find patterns and conclusions that you wouldn’t otherwise have thought of. Not only that, but it allows this process to occur much faster too.”

Allan Stone: “The data [we’re talking about] definitely exists somewhere in your ecosystem. But to really act upon it and take advantage of it in a timely manner, you have to unlock that data and make it much more centralised. You should be able to take a much more holistic view of the data within your platform.

“We look at data from an acquisition perspective. We want to be able to see every piece of data in an acquisition campaign all in one place. How many impressions? Who was running the campaign? How many clicks did it receive? How many first-time depositors were there? We then pull that post-acquisition data to measure the success of those campaigns, allowing our partners to see the actual return on their ad spend compared to the net gaming revenue for the player.”

Thomas Kolbabek: “Ensuring that all decision-making processes are made transparent to humans will be a second step. And we are possibly approaching a time when this step is not even the most important one.”

Karl-Jorit Hausdorf: “I think it will be very different for each customer. Generally, reading data is the last step in terms of user experience. At the end of the day, you need to determine who is going to be the end-user and how they need to consume that data.

“Over the last five years, we have seen many companies invest in data visualisation, whether that’s Power BI or Tableau. The next step is going to be working out how they automate that process based on having this data more visualised. For example, do they clean up that data? How do you use that data to automate processes? That is where visualisation is a really great starting point.”

Andrea McGeachin: “The best thing that businesses can do is to employ expert and qualified decision scientists who specialise in the data sets that you are working with. Without this specialised knowledge, identifying crucial insights becomes a significant challenge.”

What role does AI, machine learning, and automation play here?

Etienne Azzopardi: “The power of AI and machine learning can be leveraged in the automation of data analysis and its subsequent translation into worthwhile insights. Often, the sheer volume of data collected by a company can be so vast that it’s incredibly difficult for a human operator to analyse it manually. But using AI can streamline this process and even deliver predictive insights that are actionable in real time.

“Another advantage of such technologies is their knack for discovering unexpected correlations. That is to say those that a human operator likely wouldn’t consider looking for, some of which could hold a surprising amount of value for the business.

“Additionally, AI tools can automate the clean-up and organisation of data, enhancing its quality and ensuring relevant analysis can occur. When using AI for these purposes, however, businesses must be mindful that all projects consider any related data privacy and security risks.”

Max Sevostianov: “As I touched on above, AI and machine learning let you analyse large data sets that you wouldn’t be able to break down manually and automate potential solutions as a result. It can allow you to better predict future player behaviour and make the necessary adjustments far quicker than if you were updating everything by hand.

“In 2023, we saw many new projects that used AI to personalise content for specific markets, age groups and operators, and I think it’s now an invaluable support solution for any business. AI enables you to receive high-quality data 24/7, analyse actions in your back-end and tailor your product for specific audiences. This is undoubtedly the key to providing a more personalised experience to your customers and ultimately improving your bottom line.”

Allan Stone: “We’re just starting to see what these technologies can do to impact growth from a gaming perspective and how they can help drive player acquisition.

“AI and Machine Learning have been helpful in identifying trends and segments of customers that an operator might not have thought about engaging with previously. For example, technology now exists where an operator can segment players down to a postcode level or even by device type. This wealth of data just informs better decision-making as you’re going forward. You can then begin to create your campaigns and tailor your performance based on those insights.

“In the past, you would have needed to hire an entire team, and an analyst, to create a bespoke campaign and understand the impact this has on players. Even then, by the time you got that answer, the campaigns were already finished, and there was no way to reactivate them.

“But now with machine learning and AI, all these campaigns can be changed much more quickly. It accelerates the decision-making process.

“AI and machine learning have also helped the industry automate many more processes. And this is all about creating more relevant content.

“There are two ways that we can look at automation. The first is workflow management. Take HTML5 for example, we can build creatives on the fly, based on user responses. A use-case for this might be if we notice that a bettor is using iOS, they have their browser language set to German, why would we then show them an advert in English?

“Previously, we’d have had to create those creative assets manually. But with HTML5 technology, you can build creatives much more easily and tailor them to individual users. Things that might have taken days or weeks can now be done in seconds; which is great for everyone.”

Thomas Kolbabek: “ML and automation are at the core of these developments. Automation bridges the gaps between marketing, product and operation, whereas ML must be strategically positioned to solve the most complex optimisation problems at the points where it matters most.

“The tools for solving this very recent fragmentation in the whole process have to be developed from the ground up. Having a new generation of infrastructure is the key factor here.”

Andrea McGeachin: “Artificial intelligence, machine learning, and automation are fundamental to speed and resource management. But, without the expertise of decision scientists and astute data analysts, there’s a risk of misinterpreting trends, potentially affecting future strategic decisions.”

How does all this come together to give businesses an advantage over their rivals?

Etienne Azzopardi: “At the end of the day all data held by an organisation is an asset and should be leveraged accordingly. How well this is done with respect to all the points I’ve mentioned above–along with the company’s general attitude towards using the insights they’ve gained–will ultimately determine the long-term success of any data initiative.

“Intelligently identifying data insights and promptly acting on them can certainly enable businesses to get ahead of the competition, be that by delivering a better product, improving an internal process, or reducing their operational costs.

“All these things–alone or combined–can be enough to differentiate an organisation from its main rivals, which is often the difference between success and failure in a competitive market. And that’s exactly what we are striving to achieve at Swintt.”

Max Sevostianov: “By effectively using data insights and solutions that are backed up by AI and machine learning, iGaming businesses can gain a competitive advantage in the market by providing better personalised and more rewarding user experiences.

“They can all be used to improve game design, enhance security measures and even gain a better understanding of what’s going on inside your business quickly and efficiently. This helps you stay ahead of market trends and better meet both the needs of the customer and the needs of your business.

“These days, if you want to be the best business you can be, it’s vitally important that you analyse all the data you have available to you. Without this analysis, I don’t believe there’s any chance you can continue keeping pace with industry demands.”

Allan Stone: “We say this all the time, but the best brands are going to be those that can personalise their messaging and user experience.

“The only way that you can do this is by using data and automation. Right?

“You must have the right data to inform your decision-making processes, but also the right tools to incorporate that information into automated workflows. You can’t really have one without the other. By getting this right, you will definitely set yourself apart from the crowd. You will be able to make decisions much quicker than your competitors.”

Thomas Kolbabek: “Once the fragmentation issues are solved and the necessary processes are in place, the results speak for themselves.

“We see uplifts of three-digit percentages in bottom-line results, and the corresponding growth advantages. And there still is no end in sight when it comes to improving and speeding up the process. Every leader in the industry has to let that sink in.

“There are businesses already using these methods that have in-sourced the learning process while having out-sourced the tool-making process. These are the fastest growth accelerations you will find in the market!”

Karl-Jorit Hausdorf: “Actually, that was one of the outcomes of our survey. We can say that the companies that achieve what I have mentioned above are data-driven. The more academic term here is data literacy, which means that you haven’t just given the data analysis tools to a small handful of data scientists. Rather, it means that you have given the majority of your company access to that data, and they are both enabled and empowered to use it.

“On the one hand, not many companies in Europe can be considered data-driven. On the other hand, for those who are data-driven, you can see a very clear, strong correlation that they perform above average, by default.

“The benefit here is relatively clear; once you are in that space, you become data-driven and very factually driven.

“At Fujitsu, we have a data maturity tool where you can clearly see whether you are on the path to becoming more data-driven. So, there is directly a competitive advantage for your company to move in that direction and invest in robust data practices.”

Andrea McGeachin: “Sharing appropriate data is the key. At Neosurf, data collaboration with operators empowers smarter decision-making and collective growth. For instance, through our KYC Handshake product and API, we establish shared IDs and due diligence components, enabling self-limiting usage and cross-operator spending.

“By leveraging payment trends, operators can devise tailored acquisition and retention strategies, easily measure their effectiveness, and continuously enhance them.

“In conclusion, investing in skilled data scientists is paramount for deciphering data insights and facilitating informed choices.”

Editor’s Note:

Utilising data strategically leads to an enhanced customer experience, a safer gambling journey, and more streamlined internal operations.

But our RoundTable experts also caution that effective data use requires the right technology and a skilled team of data analysts to transform complex data sets into real-time and actionable insights. This ultimately gives businesses a competitive advantage.

After all, as Etienne Azzopardi underlines “data is an asset and should be leveraged accordingly.”

In addition to the more obvious uses, AI and machine learning can efficiently find patterns that humans often miss (due to its extensive data processing capabilities). As such, it is better suited for quickly and efficiently handling large amounts of data and capturing the latest trends.

Humans, however, still play a crucial role in analysis, and skilled data analysts and scientists are essential in making informed decisions.

According to Max Sevostianov: “Without this analysis, I don’t believe there’s any chance you can continue keeping pace with industry demands.”

Whatever the data’s application–whether it’s to “improve game design, enhance security measures or gain a better understanding of what’s going on inside your business quickly and efficiently”–its correct application can help companies “stay ahead of market trends and better meet both the needs of the customer and the needs of your business.”

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