Société Générale

Personalized Advertising & Credit Service Platform for enterprise-scale financial marketing

Société Générale Advertising Platform

Challenge

The client needed a unified internal system to consolidate multiple advertising APIs and financial product pipelines. Key requirements included: • Real-time personalization and product targeting • Fully internal delivery (no third-party ad networks) • High security and strict compliance • 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 models. 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 • deployment strategies designed to minimize downtime • consistent version control and auditability 5 — Infrastructure & Scaling Kubernetes ensured: • horizontal auto-scaling • resilient microservices • designed for stable performance under load • isolated service environments A behavioral analytics layer was added to measure engagement and feed ML models — improving ad targeting over time.

Results

  • Automated, personalized delivery of financial product offerings
  • Significantly reduced campaign setup time compared to the previous workflow
  • Consolidation of data from three separate systems
  • Unified monitoring and reporting for conversions and performance
  • Stable operation during periods of high campaign traffic

Results reflect project-specific conditions, system context, and initial architecture.

Technical 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 capabilities combined with fast iteration cycles.

Project delivery was conducted in collaboration with a regional business unit.

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Client name used based on publicly available project references or with permission.