Data duels: can AI overtake manual methods in iGaming’s competitive wars?

In iGaming, data is the lifeblood of success. Yet the real challenge lies not in amassing data but in transforming it into actionable insights. With the advent of artificial intelligence (AI), the industry stands at a crossroads: should it fully embrace automation, or is there still room for manual data collection in this ever-evolving landscape?

The roots of data collection in iGaming

Before AI revolutionized data analytics, iGaming operators relied heavily on manual data collection methods. Early strategies included basic user registrations and rudimentary surveys, giving operators just enough insight into player demographics and preferences.

According to industry experts, “The gambling industry has always relied on data, but the process was labor-intensive and prone to errors.”

Operators would gather data on metrics like daily active users or session durations, often manually cross-referencing spreadsheets. While these methods laid the foundation for today’s analytics, they were slow, error-prone, and limited in scope.

As iGaming evolved, so did its data requirements.

The industry’s exponential growth brought a surge in platforms, players, and transactions. This created a need for faster, more accurate tools to process data — a need that AI has been uniquely positioned to fulfill.

The rise of AI in data collection

AI’s integration into iGaming has redefined what’s possible. From predictive analytics to real-time trend detection, AI has shifted the focus from collecting data to interpreting it.

Max Tesla, co-founder and CEO of Blask, captures the essence of this shift:

"The challenge isn’t just about collecting data—it’s about making sense of it. Operators are often overwhelmed by mountains of data but lack the tools to extract meaningful insights. That’s where AI comes in.”

Tesla’s insights resonate in an industry where the stakes are high and decision-making speed is critical. Blask’s AI models, for example, analyze player behavior in different markets, understand trends and patterns in iGaming markets and show users competitive table with 1-hour granularity. According to Tesla, tasks that once took weeks to analyze manually can now be executed in seconds.

AI’s competitive edge: the pros

1. Unmatched efficiency

AI’s capacity to process large datasets at lightning speed offers a decisive advantage. Blask’s AI-powered tools have reduced fraud detection time by 60%, and predictive analytics can anticipate player churn with 85% accuracy.

As Max Tesla puts it:

"AI doesn’t just look at yesterday’s data — it forecasts tomorrow’s trends, giving operators a critical edge."

2. Enhanced personalization

AI algorithms enable operators to deliver tailored gaming experiences. From personalized game recommendations to targeted promotions, AI can increase player retention by up to 39%.

For instance, platforms using AI-powered recommendation engines have seen a 43% boost in conversion rates, a clear testament to its transformative power.

3. Proactive security measures

With AI, iGaming operators have revolutionized fraud detection and risk management. Machine learning models can monitor thousands of transactions in real time, flagging suspicious activities before they escalate.

According to a recent report, AI has helped reduce payment fraud by 48% in the iGaming sector.

4. Scalability

As iGaming expands into new markets, AI ensures operators can scale their operations without increasing manual effort. This capability is especially crucial in regions with diverse player bases and regulatory complexities.

5. Real time insights

Unlike manual methods that require days or weeks to compile reports, AI-driven platforms like Blask offer real-time insights. Blask continuously collects data from various sources, including public search data and competitor analysis, and updates market trends every hour​​.

This means that iGaming operators can respond to changes as they happen, giving them a competitive edge.

6. Comprehensive market coverage

AI doesn’t just provide data on your brand — it covers the entire market. Relative Market Share metric from Blask allow you to see how your brand compares to competitors on a global scale​. This type of analysis is invaluable for making strategic decisions about entering new markets or adjusting your product offerings to better align with market demand

The manual approach: why some still rely on it

Despite AI’s advantages, manual data collection remains a cornerstone for certain aspects of iGaming. Experts like Justin Le Brocque of Yolo Group argue that manual methods allow for a more nuanced understanding of complex data sets.

He explains:

"AI is powerful, but sometimes, manual processes yield deeper insights, particularly when dealing with qualitative data or edge cases."

1. The human touch

Manual data collection can uncover subtleties that algorithms might miss, such as cultural preferences or unique behavioral trends.

For instance, human analysts are often better equipped to interpret the context behind anomalies in player data.

2. Reliability in edge cases

AI relies on historical data to function, which can be problematic in entirely new scenarios. Manual oversight ensures these edge cases are accounted for, reducing the risk of misinterpretation.

3. Ethical considerations

As AI grows more pervasive, concerns about data privacy and ethical usage loom large. Human intervention can act as a safeguard against over-reliance on algorithms that might inadvertently breach ethical boundaries.

The cons of each approach

AI’s limitations

• Data quality dependence:
  The phrase "garbage in, garbage out" aptly describes AI’s reliance on clean, accurate datasets. Poor data quality can lead to flawed predictions and decisions.
• High implementation costs:
  Developing and maintaining AI systems is expensive, often out of reach for smaller operators. However, new tools and services like Blask are affordable/
• Loss of human judgment:
  AI lacks the ability to interpret emotional and cultural nuances, which can sometimes lead to impersonal or inappropriate player interactions.

Manual collection’s drawbacks

• Time-consuming:
  Manual methods are significantly slower and more resource-intensive.
• Error-prone:
  Human error remains a persistent issue, particularly when dealing with large datasets.
• Limited scalability:
  As the iGaming industry grows, manual methods struggle to keep pace with increasing data demands.

The hybrid future: combining AI and manual insights

The industry’s future likely lies in a hybrid approach. AI can handle routine data analysis and predictive modeling, while human analysts focus on interpreting complex patterns and ensuring ethical standards. This balance maximizes efficiency while preserving the human touch.

Max Tesla emphasizes this synergy:

"AI isn’t here to replace humans; it’s here to augment their capabilities. At Blask, we see AI as a tool that empowers operators to make smarter, faster decisions while maintaining a human-centered approach."

Looking ahead: AI as the game-changer

As iGaming continues to evolve, the role of AI will only grow. Experts predict that by 2028, the global AI market will reach $930 billion, with iGaming poised to be a major beneficiary. Personalized experiences, predictive analytics, and real-time decision-making are just the beginning.

Industry expert encapsulates this vision:
"In three to five years, AI-driven personalization will be the standard in iGaming. Operators who fail to adopt it risk being left behind."

Conclusion

The debate between AI and manual data collection in iGaming is not about choosing one over the other — it’s about leveraging the strengths of both. AI offers unparalleled speed, scalability, and predictive power, while manual methods ensure context, creativity, and ethical oversight.

As Max Tesla wisely notes:
"The future of iGaming is data-driven. Whether through AI, manual methods, or a combination of both, the goal remains the same: to provide players with a safe, personalized, and engaging experience."

In this high-stakes industry, those who master the art of data-driven decision-making will undoubtedly come out ahead.