Transforming Customer Insights in the Telecommunications Industry with Databricks 

Overview

About the Client

The client operates in the telecommunications industry, serving millions of customers across mobile, broadband, and enterprise services. With rapid growth in customer data and service offerings, the company aimed to modernize its data architecture to deliver better customer experiences and optimize network performance. 

Challenges Faced 

The client was experiencing a range of data-related challenges that hindered business agility and customer engagement: 

• Data Silos: Customer data was fragmented across CRM, billing, network, and support systems, limiting unified analysis. 

• Slow Analytics Pipeline: Legacy ETL tools couldn’t keep pace with the real-time data volume, delaying reporting and decision-making. 

• Low Personalization: Inability to segment customers accurately reduced the effectiveness of marketing and service campaigns. 

• Scalability Issues: The current infrastructure couldn’t scale with growing datasets and user demands. 

• Limited AI/ML Integration: Data scientists faced difficulty in building and deploying models efficiently across departments. 

Our Solution 

We implemented the Databricks Lakehouse Platform to centralize, process, and analyze data in real-time, enabling faster insights and innovation. 

Key solution highlights included: 

• Unified data lakehouse architecture to break down silos 

• Real-time analytics for customer segmentation and churn prediction 

• ML model deployment pipelines for personalization and network optimization 

• Scalable cloud infrastructure to support data growth 

Technology & Tools Implementation 

The following Databricks features and integrations were implemented: 

• Delta Lake: Provided reliable and scalable storage for structured and unstructured data 

• Databricks Notebooks: Empowered data scientists to build collaborative analytics workflows 

• MLflow: Enabled lifecycle management of machine learning models 

• Auto-scaling Clusters: Ensured cost-effective resource management during peak loads 

• Integration: Connected seamlessly with Salesforce and other enterprise tools 

Results

The Impact and Outcome 

After adopting Databricks, the telecom company achieved: 

• 50% faster data processing across analytics pipelines 

• Improved customer targeting accuracy by 40% 

• Real-time churn prediction with 85% accuracy 

• Enabled self-service analytics for business teams 

• Scalable infrastructure reduced compute costs by 30% 

By leveraging Databricks’ unified data platform, the company transformed its approach to data-driven decision-making, enabling personalized services, efficient operations, and faster innovation.