Mohamed Belhadj

As part of a strategic mission for SNCF, I addressed a key challenge: advanced traveler segmentation to enhance loyalty, in the context of increased competition in the European rail market by 2030.

The client had a dual objective:

To better understand the diversity of its customer base,

To activate concrete retention and value levers against increasingly aggressive competitors.

Approach & Value Delivered

I designed and deployed a 100% data-driven segmentation, going beyond traditional marketing segmentation by combining behavioral, financial, and risk dimensions:

Frequency and regularity of travel

Consumption habits (types of journeys, channels, timing)

Revenue and profitability per customer

Indicators of fragility / sensitivity to competition

Loyalty and upselling potential

This approach identified segments with significant business implications, as well as customer groups at risk of churn that were previously not well understood.

Operational Implementation

The mission covered the entire data value chain:

Data exploration, preparation, and modeling using SQL, Python, and Dataiku

Construction of actionable and statistically robust segmentations

Data visualization and decision-support dashboards in Power BI

Strategic synthesis and business recommendations formalized in PowerPoint presentations for top management.

I also presented the results and recommendations during several steering committees (COPIL), adapting the communication to business, data, and decision-maker stakeholders.

Key Results:

- A unified and actionable customer view

- Segments directly usable by marketing, CRM, and strategy teams

- Concrete decision support in a context of transformation and European competition

I utilize advanced data science techniques to monitor and analyze sensitive credit portfolios, ensuring compliance with internal policies and U.S. regulatory requirements. I developed a comprehensive Power BI dashboard that provides non-technical teams with real-time financial risk metrics, enabling informed decision-making. I leverage SQL Server and Excel to conduct deep data analyses, supporting critical business decisions while ensuring regulatory accuracy in monthly and quarterly reports. To improve operational efficiency, I applied Python for automating key processes, reducing manual tasks, and minimizing the risk of errors. By combining data science methodologies with financial expertise, I can uncover actionable insights and drive the creation of data driven strategies that enhance risk management and streamline processes. My role requires continuous collaboration with cross functional teams to ensure that data solutions align with broader business objectives and regulatory standards.

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