AI language models are amazing at automating interactions and efficiently resolving queries while shaping conversations to individual user preferences and behaviors. The adaptability of these gambling AI models extends beyond just English, as they can generate and translate text into multiple languages after complex prompt engineering, as well as additional fine-tuning. This capability is crucial for making each communication—be it via email, SMS, or app push— more effective and user-specific, serving gamers’ most urgent needs.
Raid Arfua, Head of Artificial Intelligence
At GR8 Tech, implementing LLMs within our CRM systems is strategically tailored to navigate specific challenges. Through research, we realized that while the ChatGPT API is recognized for its superior text generation quality, its usage is limited by security concerns and policy restrictions regarding gambling. As an alternative, Llama2&3 has emerged as a robust option, offering quality nearly on par with ChatGPT. Furthermore, Llama’s versatility extends to translating texts generated in English into other languages, which could enhance our service’s global applicability.
To accommodate our sportsbook platform's multilingual needs, we are fine-tuning pre-trained LLM on carefully prepared texts of different languages because translation tools still provide too ‘machine-translated’ output with some minor mistakes. This allows for the efficient translation of English-generated texts into various languages, ensuring broad communication coverage. As part of our development process, the initial output from AI LLM undergoes a feedback cycle, which informs a subsequent iteration involving more complex prompts to refine the quality of generated texts. Finally, we developed a service that leverages GR8-7b LLM (which is a mix of a few pre-trained 7b models additionally fine-tuned on a private custom dataset) for real-time text generation, equipped with detailed and context-specific prompts tailored for each type of communication, including emails, SMS, and app notifications. This service also features text post-processing capabilities, utilizing a suite of advanced AI NLP tools to ensure the precision and relevance of communications. Additionally, the solution includes a user interface titled “Gen AI Texts UI,” which streamlines the text generation process for operational ease.
💡 We also tested other open-source alternatives, like GPT4All models. However, a fine-tuned version tailored for iGaming needs produces better communication messages.
How to Make No Mistakes With AI
Casino platform operators can either develop their own models using open-source solutions or use the services of a provider with in-house solutions. This is because more mainstream solutions like ChatGPT may be required. To make our models produce accurate results, we trained them on real gambling industry operational data. We aimed to create a simple, cost-effective model with complete in-house control.
GR8 Tech’s LLM has undergone numerous training iterations to minimize errors. This process ensures that the model performs reliably under the diverse and specific demands of the iGaming industry, providing us with a significant edge in delivering tailored and effective solutions.
The graph shows how training loss changed during four training sessions. The purple and red lines are more stable because there we used larger batch sizes and did not group data points together.
Training loss measures how much the model's predictions differ from the desired outputs made by humans. It shows how much the model's predictions deviate from the target values while aligning with the brand's voice and regulatory requirements.
Of course, loss is only one aspect of the training procedure. At the core of effective training are the SFT (Supervised Fine-Tuning) and Lora (Low-Rank Adaptation) mechanisms, which facilitate fast and efficient fine-tuning, even on comparatively small datasets.
How to Set up a Communication With AI Tool
In the operational scenario for GR8 Tech's CRM systems using LLMs, the process is designed to be highly intuitive and flexible, catering specifically to the needs of CRM managers. Here are 8 simple steps to create content with our model:
✅ Step 1: Brand Selection
CRM managers start by selecting the brand they are managing. This ensures all communications align with the brand's voice and regulatory requirements.
✅ Step 2: Raw Input
The CRM manager inputs raw information regarding promotions or offers. For instance, details about a bonus offer, such as eligibility criteria, validity period, and bonus percentage, are entered. This step is crucial as it forms the basis of the content that will be generated.
✅ Step 3: Communication Type
The CRM manager then chooses the type of communication through which the message will be sent, choosing between options like email or SMS. This choice will dictate the style and length of the text to be generated.
✅ Step 4: Emojis
Depending on the tone and formality of the message, the CRM manager decides whether or not to include emojis. This helps tailor the message’s tone to match the expected recipient's preferences and the communication's context.
✅ Step 5: Text Size
The CRM manager specifies the desired size of the text, ensuring it is suitable for the chosen communication method and is engaging yet concise enough to capture the recipient's attention effectively.
✅ Step 6: Generate AI Text
With all parameters set, the system uses the Llama2 model to generate the text automatically. The AI considers the input information, the type of communication, and any specified preferences to create a tailored message.
✅ Step 7: Regenerate AI Text
If the initial text generated does not meet the expectations or specific needs of the campaign, the CRM manager can opt to regenerate the text. This feature ensures flexibility and enhances satisfaction with the final output.
✅ Step 8: Feedback
Once the text is finalized and used, the CRM manager can provide feedback on its effectiveness and quality. This feedback is crucial as it helps refine the AI model and improve future text generation.
How It Looks Like
For instance, a CRM manager handling a promotion tied to the hockey World Cup match between Slovenia and Canada on May 14, 2023, would input details like the event date, associated stream ("Sport"), and specifics of the offer (e.g., "Get 10% cashback on lost bets for odds over 1.5 on any sports until May 14th"). Based on this input, the system would generate an appropriate promotional message, which could be further adjusted or regenerated as needed.
This system is designed to maximize efficiency in CRM operations while maintaining high standards of personalization and relevance in customer communications.
Here’s another example
In GR8 Tech's CRM systems, the practical application of LLMs is demonstrated vividly through our capability to generate highly engaging and customized promotional emails from simple prompts. Here are two concrete LLM examples of how LLM technology transforms basic input into compelling marketing content:
From a Raw Input Prompt to a Generated Email:
Raw Input Prompt: "play this summer few times in 3 sports and get 10% cashback!"
From a Different Raw Input Prompt to Another Generated Email:
Raw Input Prompt: "bet this summer on 3 sports and receive bonuses for each"
These examples showcase how LLMs are used in GR8 Tech’s CRM not only to automate content creation but also to enhance the appeal and relevance of communication by embedding specific promotional details and engaging language that resonates with the target audience.
Generated messages look subtle and gentle and follow a similar style, which our CRM managers like the most.
This capability significantly streamlines the marketing process, ensuring that promotional campaigns are swiftly deployed and effectively customized to meet the market's needs. Such efficiency saves considerable time and resources and improves the return on investment for marketing campaigns by elevating customer engagement and response rates.
In comparison, here are examples of Emails generated by one accurate open-source pre-trained 7b model—even after complex prompt engineering, English messages are too pushy, while Portuguese messages are not of high quality at all, and this is the reason we are fine-tuning our model additionally.
The Impact of LLMs on Efficiency and Productivity
Implementing LLMs in GR8 Tech's CRM tools can significantly boost efficiency and scalability. By leveraging advanced models, content creation time can be reduced from an average of 30 minutes to just 2 minutes for direct drafting. The entire process, including backlog tasks and queues, can be streamlined from two days to 15 minutes. This faster approach can substantially improve productivity, allowing for an unlimited increase in weekly content output. Consequently, it can reduce the required number of content writers without increasing the workforce.
Our new system requires only native speakers for translation purposes, eliminating the need for dedicated writers. These improvements will be measured against predefined success metrics. Previously, the text creation process, including all associated tasks, could take up to two days. However, we aim to reduce this to 15 minutes. Similarly, while we previously produced between 150 and 250 content pieces monthly, our new approach aims for potentially limitless production capacity. This enhancement can significantly reduce labor costs and dependencies, making our CRM efforts more agile and cost-effective. GR8 Tech's automated text generation system using LLMs can provide substantial cost savings.
By adopting LMM, each operator can save thousands of dollars per month on costs previously allocated to content writers and translators.
Raid Arfua, Head of Artificial Intelligence
Other LLM Trends That Could Benefit iGaming
So, can LLMs do more than write emails for CRM managers and annoy customers desperate to talk to a human? You’d better believe it. Here are some of the major trends materializing in the LLM space as you read this:
📊 Advanced Market Insights
By leveraging LLMs integrated with market data, player feedback, competitor analysis, and economic indicators, companies can gain strategic market insights and inform their marketing and operational strategies. Key metrics for success include market forecast accuracy and strategic alignment with business objectives.
While established firms like Bloomberg and Thomson Reuters already employ advanced data analytics tools to predict market trends. By integrating LLMs, they can combine numeric and textual data sources and receive in-depth analysis and actionable insights.
📈 Data-Driven Decision-Making
Advanced Textual Data Analysis is fancy talk for customer reviews, social media data, and support logs that all businesses gather nowadays so they can gain valuable insights and make informed decisions quickly, minimizing operational risks.
The trick is to make the possess as efficient as humanly possible and gather truly valuable data. Companies like Palantir and SAS Institute offer deep-text analysis solutions, often combining LLM machine learning and other methodologies. LLMs may not necessarily be a core component of their offerings. However, LLMs specialize in handling and generating insights from textual data, making them a valuable asset in this domain.
✅ Regulatory Compliance
If at least once in your life you ventured into an Apple user agreement, you know that these humongous blocks of legal mambo jumbo simply cannot be written by a human. However, superhumans are scarce, and regulatory compliance and reporting are critical functions for compliance officers, ensuring adherence to regulations and reducing legal risks. LLMs can be invaluable in handling complex regulatory texts, transaction records, player data, and communication logs; companies can significantly enhance their compliance efforts.
Established players like IBM Watson and Comply Advantage have already demonstrated the potential of LLM integration in this domain. LLMs excel in processing and interpreting intricate regulatory documents, enabling compliance officers to navigate the complex landscape of rules and regulations with greater efficiency and accuracy.
🎰 Game Development
Game developers must have been in absolute joy when ChatGPT first came online in 2022. Imagine the possibilities! Game studios can now generate and analyze textual feedback from players, gameplay data, market trends, and competitor analysis and use all that precious data to craft increasingly intricate gameplay.
While industry giants like Unity Technologies and Epic Games may not directly integrate LLMs into their game development pipelines, LLMs can still process and interpret textual data and help increase engagement rates and in-game time. Tailoring game experiences to player preferences can directly influence both of these.
💵 Maximizing Player Retention
Player Retention Analysis is critical; after all, you do want them coming back for more. Player loyalty and churn are important metrics in iGaming, and by integrating text-based player feedback with gameplay data, companies can understand player behavior and preferences.
Industry leaders like Electronic Arts and Activision Blizzard have already demonstrated the power of data analytics in understanding and predicting player behavior, but LLMs take this a step further by seamlessly blending quantitative data from player activity logs, transaction records, and support interactions with qualitative insights from player feedback.
But AI Is Not a Cure-All. Here’s Why
Despite LLMs' impressive capabilities, a series of persistent challenges continues to cast a shadow over the field. These advanced systems, while transformative, are not without their flaws. From issues of bias and data privacy to the complexity of maintaining and updating LLM architectures, the problems are multifaceted and often deep-rooted.
Raid Arfua, Head of Artificial Intelligence
This is particularly true for iGaming AI, where the stakes are high, and the demand for precision and reliability is constant. Over time, we’ve discovered that operators who attempt to navigate these complexities on their own often find themselves overwhelmed. Without specialized knowledge, they risk getting bogged down in technical nuances and operational hurdles that could significantly delay progress and efficiency.
❌ Hallucinations
LLMs have a big issue with "hallucinating"—generating fake responses that sound realistic. Even with improvements in gaming technology, these hallucinations are getting worse. This shows that the models do not understand the facts and operate as more than autocomplete tools. Therefore, a CRM manager needs to provide input to the models to enhance the dialogue rather than just throwing content randomly.
However, in most cases, the industry responds to these shortcomings with a mix of denial and desperation. Some companies retrain their models using outputs generated by other AI systems, a method known to degrade information quality further. This recursive training does little more than amplify the errors and biases of the initial data sets.
❌ Hype
The hype surrounding instances where AI systems appear to develop functionalities they weren't explicitly trained for often turns out to be more mirage than magic. Claims of AIs suddenly acquiring the ability to translate unknown languages or perform complex reasoning have repeatedly fallen flat upon closer scrutiny. Experts doubt the industry's claims that AI systems can reason, plan, and remember. They point out that these promises often need to be revised with current technology.
In the context of iGaming, it's crucial to recognize that CRM managers are still at the helm. They are the linchpin that gives meaning to the player and platform interaction. It's a misconception to believe that “the system” will handle everything for the operator. While we can ensure that AI saves you considerable money, people must remain in charge, bridging the gap between automation and personal touch.
❌ Money
Financially, the sector is starting to feel the pinch. High-profile startups are burning through cash at alarming rates, with little to show for sustainable development or profitable functionality. The broader financial outlook isn't promising either; some predict that the AI venture capital bubble is nearly bursting, likening it to other speculative bubbles that have burst in the past.
❌ People Against an AI-dominated future
Compounding these technical and financial troubles are ethical and social concerns. The misuse of AI, whether through promoting unproven technologies like voice stress analysis or perpetuating biases under the guise of objectivity, highlights the darker aspects of a rush toward an AI-dominated future. The push to replace human judgment and oversight with algorithms could lead to a host of unintended consequences, from job displacement to eroded personal privacy to good old scamming—something painfully relevant to iGaming.
It's important to be realistic about the potential of AI and LLMs in iGaming. While technology offers great possibilities, it also poses challenges and risks. As we move forward, AI development should be guided by scientific standards, ethics, and a clear understanding of machine learning's limits. This way, we can take advantage of AI's potential without falling into its pitfalls. As excitement fades, iGaming platform providers will likely focus on basic and cost-effective developments to provide their clients competitive advantages.
Closing Thoughts
Again, iGaming and betting industry platform providers face a pivotal decision: to develop their own models based on open-source code or to utilize the in-house solutions offered by career AI professionals. Specifically tailored for the iGaming industry, models like ours are finely tuned on data from real operators, ensuring precise and reliable results that are crucial for gaining a competitive edge. Such an approach underpins the effectiveness of LLMs in personalized gaming experiences, mirroring the personalization engines of e-commerce platforms like Amazon. It also optimizes customer interactions through intelligent support systems like those used in customer service.
Furthermore, the integration of predictive analytics from fields such as healthcare to predict player behaviors, alongside the adoption of real-time interaction technologies from collaborative tools like Slack and Microsoft Teams, illustrates the potential of LLMs to augment gambling experiences considerably. These technologies provide a blueprint for next-generation support and enhance the social aspects of gaming platforms, ensuring dynamic and engaging user experiences.
However, when choosing between proprietary and open-source technologies, navigating the costs associated with implementing these advanced AI features becomes challenging. While the current expense is substantial, as adoption grows and technology advances, these costs are expected to decrease, making advanced AI tools more cost-effective. Ultimately, the strategic development of affordable, simple models that operators can fully control will be key to leveraging the transformative potential of LLMs in the iGaming industry.
🔍 What do you find most interesting about AI in iGaming? Drop a comment below, and we'll make sure to cover the juiciest bits of this topic in our future posts. ⬇️⬇️⬇️
