Case Study: Powering Personalization for a Global MNC with AWS & Snowflake

At Polar Packet, we believe the most successful companies run on data. But what happens when that data is trapped? We recently partnered with a leading multinational conglomerate (MNC) to solve this exact problem, transforming their siloed data into a powerful engine for customer personalization.

This project was a showcase of our end-to-end capabilities, from Data Engineering to build the foundation, Data Analytics to create insights, and Data Science to predict customer behavior.

Due to a non-disclosure agreement (NDA), we cannot name the client, but their challenge is one many large enterprises face. Here’s how we helped them build a future-proof data strategy.

The Summary (TL;DR)

The Client: A major multinational conglomerate (MNC) with a diverse portfolio of subsidiaries.
The Challenge: Data was trapped in legacy systems, making it impossible to get a single view of their customers. This blocked critical cross-sell and upsell opportunities.
The Solution: Polar Packet deployed its full data capabilities:
Data Engineering: We designed a cloud-native strategy using AWS and built a high-performance, centralized data platform on Snowflake.
Data Analytics: We unified all data sources into a “Single Customer View,” making it accessible through BI (Business Intelligence) dashboards for the first time.
Data Science: We built a sophisticated customer recommendation engine on top of this unified data to predict and drive sales.
The Results: We delivered a real-time personalization engine that empowers the client’s marketing and sales teams, unlocking significant growth in customer lifetime value (CLV).

The Challenge: Data-Rich, Insight-Poor

Our client, a global MNC with a complex web of subsidiaries, faced a classic challenge: they were data-rich but insight-poor.

For decades, each business unit had operated independently, storing valuable customer data in its own siloed, often-legacy, system. This fragmentation created massive blind spots:

The group-level marketing team couldn’t personalize campaigns because they couldn’t see a customer’s full purchase history.
The sales team had no data-driven insight into what a customer might buy next, leaving valuable cross-sell and upsell opportunities purely to chance.
Valuable customer behavior data from one subsidiary was completely invisible to another.

They knew their scale was their biggest advantage, but their technology was holding them back. They engaged Polar Packet to design and execute an end-to-end data strategy.

Our Solution: A 3-Stage Data Transformation

Our approach was a multi-disciplinary effort to build a scalable foundation, generate clear insights, and deliver predictive value.

Part 1: The Data Engineering Foundation (AWS + Snowflake)

This was the foundational step. Our data engineering team designed the core architecture to handle the client’s massive scale.

Strategy & Architecture: We chose Amazon Web Services (AWS) for its robust, secure, and scalable cloud infrastructure (like Amazon S3 for data lake storage).
Centralized Platform: We selected Snowflake as the high-performance cloud data platform. Its unique architecture separates storage and compute, allowing different teams to query data simultaneously without slowing each other down.
Data Pipelines: Our engineers built robust, automated ELT (Extract, Load, Transform) pipelines to pull decades of data from all legacy systems and subsidiaries into the new Snowflake platform.

Part 2: The Data Analytics Layer (The Single Customer View)

With the data flowing, our data analytics team stepped in to make it usable and valuable.

Data Modeling: We transformed the raw data, blending disparate sources to create the client’s most valuable new asset: a “Single Customer View.”
BI & Dashboards: For the first time, the client could see every customer’s complete journey and purchase history from all business units. We delivered this insight through user-friendly BI dashboards, slashing reporting times from days to seconds.

Part 3: The Data Science Activation (The Recommendation Engine)

With a clean foundation and clear insights, our data science team could now build the “money-maker.”

Machine Learning: We used the unified data as “fuel” to design and deploy a sophisticated customer recommendation engine.
Predictive Models: Using machine learning models (like collaborative filtering), the engine analyzes billions of data points to predict which products a customer is most likely to buy next and identify “hidden gem” products they’ve never seen.
Activation: This engine feeds real-time recommendations directly to the client’s marketing tools and sales CRMs, putting actionable, predictive insights into the hands of the teams who need them.

The Results: From Siloed Data to Smart Decisions

This solution fundamentally transformed the client’s ability to engage with their customers.

Quantitative Results:
Achieved a 100% unified, 360-degree view of the client’s entire customer base.
Deployed a recommendation engine capable of delivering personalized “next best offer” suggestions in real-time.
Slashed data query and reporting times from hours or even days to mere seconds.
Qualitative Business Impact:
Empowered Marketing: The team can now move from generic, “mass-blast” emails to highly targeted 1-to-1 personalization.
Empowered Sales: The sales team now has “Next Best Offer” insights directly in their CRM, turning every customer conversation into a smart, data-driven opportunity.
Future-Proofed: The client now owns a scalable, modern platform to power all future AI and machine learning initiatives.