Société Générale

How we built an internal personalization and offer-delivery platform for a regional office of a leading European banking group

Société Générale Advertising Platform

H-Studio worked with a regional office of the client.

Challenge

The client needed a unified internal system to consolidate multiple internal APIs and financial product pipelines. Key requirements included:

  • Near real-time personalization and contextual product offers
  • Fully internal delivery (no third-party ad networks)
  • High security standards and compliance-oriented architecture
  • Stability during heavy campaign loads
  • Consolidated data instead of fragmented systems

The platform had to support enterprise-level processes while remaining modular enough for rapid campaign iteration.

Our Approach

We developed a modular backend platform that unified several core areas:

1 — Customer Profiles & Real-Time Scoring

Integration of internal customer data, product logic, and behavior-based scoring logic.

2 — Campaign & Rule Engine

  • Flexible segmentation
  • Engagement tracking
  • Dynamic product offer delivery

3 — API Orchestration Layer

The platform acts as a unified gateway between:

  • CRM systems
  • Product and credit scoring services
  • Campaign management modules
  • Internal data APIs

4 — CI/CD & Deployment

A Jenkins-driven CI/CD pipeline provided:

  • automated testing
  • secure deployment
  • zero-downtime releases
  • consistent version control and auditability

5 — Infrastructure & Scaling

Kubernetes ensured:

  • horizontal auto-scaling
  • resilient microservices
  • stable performance under load
  • isolated service environments

A behavioral analytics layer was added to measure engagement and feed ML models — improving offer relevance over time.

Results

  • Fully automated, personalized delivery of financial product offerings
  • Campaign setup time significantly reduced — from days to minutes in typical scenarios
  • Consolidation of data from three separate systems
  • Unified monitoring and reporting for conversions and performance
  • Stable operation under high internal campaign workloads

Note: Client details and system scope have been partially anonymized due to confidentiality and regulatory requirements. Performance characteristics depend on infrastructure, configuration, and operational context.

Tech Stack

Backend: Java 11 · Spring

Database: Oracle

Infrastructure: Docker · Kubernetes

CI/CD: Jenkins

Duration: 12 months

Team: 5 engineers

Why It Matters

This project strengthened our capabilities in: • Real-time personalization • Internal API orchestration • High-scale enterprise microservice architecture • Behavioral analytics and targeting logic The design principles we developed here now power many of our CRM and automation systems — giving startups and enterprises enterprise-grade intelligence at startup speed.

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