CLIENT: iGaming client INDUSTRY: iGaming
Leveraging AI-powered predictive personalization in iGaming

Leveraging AI-powered predictive personalization in iGaming

A renowned gaming company (under NDA) partnered with Neurons Lab to leverage advanced analytics, reinforced learning, and AI-driven predictive personalization for a real-time recommendation system in the iGaming industry.

Partner Overview

In a competitive and rapidly growing industry expected to reach $321 billion in global revenue by 2026, personalized recommendations help companies to achieve a competitive edge by leveraging behavioral insights to increase engagement.

According to the AI Journal: “By delivering experiences players are more likely to remain engaged and committed to, the gaming platform increases the likelihood of return visits. Furthermore, AI-driven analytics can track player behavior in real-time and make dynamic adjustments accordingly to optimize their gaming experiences.”

Our client sought to apply advanced analytics, reinforced learning, and AI-driven predictive personalization to build a real-time recommendation system for the iGaming industry.

The client is an iGaming SaaS company building disruptive ML-powered solutions for the iGaming ecosystem. They aimed to create a state-of-the-art, AI-driven recommendation engine to help gaming operators analyze numerous interaction parameters, unique gaming behavior, and improve Lifetime Value (LTV) and user retention. 

Together with Neurons Lab, a certified advanced AWS partner, our client leveraged AWS Sagemaker and AWS Cloud Services to develop its artificial intelligence recommendations infrastructure and deploy it as a SaaS platform. 


The client sought to co-create the iGaming solution rather than outsource to combine their industry knowledge with specialized AWS and AI/ML expertise. They chose to collaborate with Neurons Lab because of the proven experience and track record implementing challenging AI solutions, clear processes and communications, and the short time to deployment (TTD).

Their goal was to develop a universal Customer Engagement Platform that helps gaming operators improve their customer experience and lifetime value (LTV). The client’s expectations were that the solution would:

  • Increase player engagement and retention by providing personalized and relevant offers to each player.
  • Improve offer creation and management by providing close to real-time data and insights on player behavior and offer performance.
  • Allow for easy experimentation and testing of different offer strategies to improve the effectiveness of the solution.

The greatest challenge was developing a complex multi-layered system that collects, integrates and processes vast and diverse input and turns it into accurate engagement predictions, close to real-time recommendations, and continuous learning machine adaptable to changing user’s behavior.


Neurons Lab proposed and implemented an AI-driven personalized recommendations solution specifically tailored to the customer’s wide target audiences across the iGaming sector. The solution encompassed the following components:

  1. Data injection and integration. The system gathered and integrated data from various sources, such as user demographics, web and mobile activity, in-game events, user interactions, transaction history, player preferences, betting patterns, etc. to form a detailed view of each player’s profile and behavior.
  2. Advanced analytics and machine learning. Neurons Lab applied sophisticated analytics techniques and machine learning models to analyze the collected data, recognize behavioral patterns, and forecast future actions.
  3. Close to real-time personalization. The solution used reinforcement learning algorithms to optimize actions, offers, and communications for each player being close to real time, enhancing user experience and engagement.

Cloud-Based Solution: Leveraging AWS Services for SaaS Platform Implementation

Neurons Lab employed AWS services to deliver a reliable and scalable SaaS platform. The following AWS-specific components were used:

  • Data ingestion pipeline. Data from various sources was collected and processed using Amazon Kinesis Data Stream and Amazon Kinesis Data Firehose. The raw data was then stored in Amazon S3, from where it was cataloged and prepared for further processing by AWS Glue, resulting in training data stored again in Amazon S3.

AWS architecture, predictive personalization

  • Recommendation engine. The engine was powered by Amazon SageMaker. It started with data processing jobs to clean and transform the training data. The transformed data was then used to train machine learning models with SageMaker Training Jobs. The trained models were evaluated for performance, and inference was done to provide real-time personalized recommendations.
  • Campaign application layer. This application was designed using Amazon CloudWatch and Amazon ECS Campaign Application. Its aim was to utilize the outputs of the SageMaker Recommendation Engine to conduct targeted campaigns, providing players with personalized promotions and communications. It ensured each campaign aligned with individual player behaviors and preferences.

MLOps Solution Powered by Amazon SageMaker Pipelines for Predictive Personalisation 

Neurons Lab offers an MLOps Accelerator, an advanced solution built upon Amazon SageMaker Pipelines, designed to streamline and scale machine learning workflows. With pre-built templates and CI/CD capabilities, it automates and standardizes MLOps practices across organizations. Optimized infrastructure and purpose-built tools enhance team productivity and enable quicker training times, while consistency is ensured through Infrastructure as Code.

AWS architecture, predictive personalization


Implementing the solution led to a successful development of a predictive personalisation machine and a comprehensive player engagement system fit for use across the iGaming industry. The solution was also deployed as a functional SaaS platform with following benefits for our client:

  • Streamlined injection pipeline enabling them to integrate diverse data sources from different iGaming companies.
  • Customizability which allows them to easily test and adjust their engagement engine to each of their client’s unique business objectives.
  • Low maintenance and reduced operational costs thanks to using open-source libraries and frameworks combined with a public cloud infrastructure.
  • Rapid pilot project implementation of only 12 weeks enabled the client to quickly test and assess their solution and their client base interest in their core business proposition.

With support from Neurons Lab, they reached a major milestone in developing the solution at the core of their business model, which they are now testing with the iGaming industry clients to see the improvements across key metrics related to user engagement and LTV.

Results for their target customer will:

  • Increase player engagement and retention through providing personalized and relevant offers and enhancing each player’s game experience.
  • Maximize revenue by identifying the most effective offers, promotions, and communication and increasing Lifetime Value (LTV) for each user
  • Improve offer creation and management by providing close to real-time data and insights on player behavior and offer performance.

About Neurons Lab

Neurons Lab is an AI consultancy that provides end-to-end services for mid and large-sized enterprises – from identifying high-impact AI applications to integrating and scaling the technology. We empower companies to capitalize on AI’s capabilities.

As an AWS Advanced Tier Services Partner and AWS Generative AI competency holder, our worldwide team includes Data Scientists, Subject Matter Experts, and Cloud Specialists, complemented by an extensive talent pool of 500 experts. This expertise empowers us to tackle the most complex AI challenges and deliver solutions with exceptional speed, addressing both immediate needs and long-term strategic goals.

Ready to leverage AI for your business? Get in touch with the team here.

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