Case Study

Leveraging Customer Segmentation for Targeted Marketing — Powered by Databricks Lakehouse

Financial Services

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BUSINESS NEED

The client, a prominent asset management firm, wanted to scale its personalized outreach initiatives by better segmenting its customer base across diverse markets. Traditional engagement relied on static lists, fragmented data silos, and assumptions-driven targeting. They needed a unified segmentation engine to drive smarter engagement and measurable lift — one that could harness behavioral data at scale while ensuring compliance and control.

SYSTECH’S DELIVERY

Systech implemented an AI-powered segmentation framework on the Databricks Lakehouse platform.
Our solution unified structured and semi-structured data from multiple systems — including customer interactions, marketing platforms, and behavioral sources — to generate real-time, ML-based customer segments.

By using Databricks’ AutoML, Delta Lake, and Unity Catalog, we operationalized advanced segmentation with governance baked in. Campaign teams were now empowered with dynamic, high-value cohorts that responded better to tailored messaging — improving campaign performance while reducing overhead.

LAKEHOUSE-ENABLED ARCHITECTURE

The solution was architected using Databricks Lakehouse principles:

  • Ingested data from 30+ raw sources, including CRM, internal systems, marketing platforms, and web activity logs

  • Multi-layered Delta architecture with Bronze (raw), Silver (curated), and Gold (business-ready)

  • ML models trained using campaign behavior, customer lifecycle patterns, and interaction signals

  • Segment outputs published to activation-ready layers and exposed through dashboards and APIs

TOOLS USED

Databricks Delta Lake | Unity Catalog | AutoML | Python | MLflow | Power BI | Azure Data Factory | Azure Event Hub

SOLUTION APPROACH USING DATABRICKS

Systech developed a modular, scalable data pipeline governed with Unity Catalog and orchestrated using Azure Data Factory and Event Hub.

After consolidating behavioral and demographic profiles in the Silver layer, segmentation models were trained and managed with MLflow, using both clustering and RFM logic. AutoML was used to iterate across model candidates for uplift prediction and campaign affinity. Models were monitored for drift and versioned for reuse.

Segments were delivered to end users via Power BI and APIs, enabling integration into campaign orchestration tools.

LEGACY CONSTRAINTS AND DATA SILOS

Before the Databricks solution, segmentation efforts were hindered by:

  • Data fragmentation across platforms and formats

  • No unified customer view

  • Manual modeling and refresh cycles

  • Delayed campaign launches due to refresh lags

This led to reduced marketing effectiveness and high operational overhead.

THE IMPACT

The Databricks-enabled segmentation engine delivered:

  • 40% uplift in campaign response rates

  • 70% faster refresh cycles via automated retraining

  • 4× agility in launching new segments

  • Improved compliance with centralized metadata via Unity Catalog

WHY DATABRICKS + SYSTECH

This engagement showcases how Systech turns AI into production-ready outcomes.
With deep expertise in Databricks Lakehouse, MLOps, and data unification, we build intelligent, scalable, compliant, and business-aligned systems.

From ingestion to activation — we help organizations modernize customer engagement.

Learn how Systech can modernize your customer engagement strategy with Databricks Lakehouse.

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