Société Générale Case Study | Personalization…

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

Decision Context

This project operated within a highly regulated enterprise environment where personalization directly intersects with compliance and data governance.

In banking contexts, delivering personalized product offers is not a marketing exercise but a controlled internal process. Fragmented systems, external ad technologies, or loosely governed real-time logic can introduce significant regulatory, security, and operational risks.

Key considerations at the decision stage:

Personalization had to remain fully internal, with no dependency on third-party advertising platforms.
Multiple internal APIs and product systems needed to be consolidated to avoid inconsistent logic and duplicated data.
Real-time decisioning required strict control, auditability, and predictable behavior under load.
Campaign execution had to scale without introducing instability or operational bottlenecks.
The architecture needed to support rapid iteration while maintaining enterprise-grade reliability and compliance.

The objective was therefore not to "optimize marketing", but to design a secure, modular internal platform that enables controlled, real-time personalization aligned with banking standards and governance requirements.

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
releases with minimal downtime
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.