Smarter CRM: Personalization at Scale in iGaming

Overview

In the competitive iGaming space, retaining high-value players is critical—but most customer care systems rely on rigid templates that ignore customer behavior and lifecycle stage. Our goal was to replace that with a smart, automated engine that could tailor offers to each user dynamically.

By combining machine learning models with rule-based logic and scalable data pipelines, we delivered a custom customer care system that helped marketing teams reallocate budgets more effectively—reducing wasted spend by up to 40% and boosting retention with more relevant offers.

Problem

Customer support teams were relying on static, template-based offer systems that treated players as one-size-fits-all. There was no way to differentiate between a high-LTV player and a casual newcomer—every player received the same promotions regardless of behavior, preference, or churn risk.

This approach led to:

• Wasted marketing budgets on low-value customers

• Missed opportunities to retain high-risk, high-value users

• Inconsistent user experience across markets and product verticals

Without intelligent automation, the CRM team was stuck in reactive mode—manually adjusting campaigns instead of proactively driving engagement.

Team & My Role

I worked as a Senior Data Scientist, deeply embedded within the CRM and Analytics function of an international iGaming company.

• Our core team included:

  • Myself and another Data Scientist

  • 1 Machine Learning Engineer

  • 2 Data Engineers

  • A dedicated CRM Manager and Analytics Lead

  • Stakeholders from customer care and marketing ops

My responsibilities:

• Built and trained ML models for risk and value prediction (XGBoost, Random Forest)

• Designed and developed feature pipelines using PySpark, Snowflake, and Airflow

• Presented impact analysis and dashboards directly to Head of CRM and Analytics

• Supported production deployments with AWS, GitLab, and MLFlow

• Oversaw cross-functional coordination between data, product, and operations

Solution

We built a modular, scalable customer care engine that merged rule-based decision logic with predictive machine learning—tailored for each customer segment across markets.

Key Components:

• Customer Segmentation: Lifecycle stage, spend history, engagement patterns

• Risk & Value Modeling: Predictive scores used to drive campaign decisions

• Budget Allocation Logic: Rules to avoid wasting offers on low-LTV or churned users

• Personalization Framework: Offers tailored by favorite games, preferences, and behavior

• Data Pipelines: Automated updates using Airflow, Snowflake, and PySpark

This system was not only data-driven—it was deeply embedded into how the marketing and customer care teams operated, turning personalization into a daily workflow.

Technical Implementation

• Languages & Tools: Python, Scala, SQL, PySpark, Snowflake, AWS

• Modeling:

  • XGBoost for customer value prediction

  • Random Forest for churn classification

• Infrastructure:

  • Airflow for orchestration

  • MLFlow for experiment tracking

  • GitLab CI/CD for model deployment

• Data Engineering:

  • Feature stores built from transactional and behavioral data

  • Daily pipeline refreshes for real-time accuracy

• AI Integration:

  • Models served as APIs within existing marketing dashboards

  • Results interpreted and acted upon by CRM managers

Results

MetricBeforeAfterMonthly marketing spend100% baseline-30–40% (reallocated)Retargeting accuracyLowSegment-aware + churn-adjustedOffer personalizationManualFully automatedCampaign alignmentInconsistentLifecycle-aware across regions

Why It Worked

Several key principles made this system successful:

• Tight CRM collaboration: We embedded the system into real daily decisions, not just dashboards.

• Hybrid design: Rules and ML worked together—making the system both interpretable and flexible.

• Modular infrastructure: Easy to scale across new markets and campaigns.

• Results-first mindset: We tied predictions directly to business outcomes—budget saved, churn reduced, value gained.

Takeaways

• A templated CRM approach leaves value on the table—custom ML pipelines can unlock 6–7 figure savings.

• Rule-based systems don’t need to be replaced—just upgraded with predictive insight.

• Embedding AI into daily business processes matters more than the model’s complexity.

This wasn’t an AI gimmick. It was a system built to scale, to serve humans, and to make smarter decisions daily.

About Me

I’m Vajk, a senior machine learning engineer and founder of AI Blacksmiths, where we craft intelligent, reliable AI systems for real-world business impact.

If you’re ready to build scalable AI that actually works, let’s talk.