Customer segmentation and marketing analytics leveraging AI/ML

Overview

The project, ”Marketing Analytics & Customer Segmentation with AI”, was undertaken for a Fortune 500 global telecom giant. The goal was to enhance the company’s enterprise data strategy by implementing a Datalake and transitioning to a Datamesh architecture. This upgrade enabled the company to leverage AI and predictive analytics for improved customer journey insights and more effective marketing campaigns.

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The Challenge

The telecom giant faced a challenge in maximizing the potential of its vast customer data. The existing Teradata warehouse infrastructure was limiting, making it difficult to analyze customer usage data in real-time and hampering the ability to generate actionable insights. The company needed to upgrade its data strategy to better segment its customers and drive more effective marketing campaigns. Additionally, the firm sought to offer a “Data-As-A-Service” model to its third-party partners, enabling coordinated customer campaigns and improving customer conversion ratios.

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The Approach

To meet these challenges, the project focused on upgrading the company’s data infrastructure and enhancing its analytics capabilities through AI and machine learning.

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Key steps in the approach included:

Datalake Implementation:

Transitioning from the Teradata warehouse to a Datalake using Databricks on Azure, with Kubernetes and Datamesh to support scalable and flexible data management.

Customer Segmentation Model:

Utilizing AI and predictive analytics to analyze customer usage data, enabling the creation of a sophisticated Customer Segmentation Model to improve marketing campaign decision-making.

Data-As-A-Service Implementation:

Developing a Datamesh architecture that facilitated the “Data-As-A-Service” model, allowing third-party partner vendors to access data via APIs and collaborate on coordinated marketing campaigns.

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The Solution

The project resulted in a comprehensive data strategy upgrade, with the implementation of a robust Datalake and Datamesh architecture. The AI-driven Customer Segmentation Model provided deep insights into customer behaviors and preferences, enabling the telecom giant to tailor its marketing efforts more effectively. The “Data-As-A-Service” model further expanded the company’s capabilities, offering third-party partners seamless access to data for collaborative marketing campaigns.

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The Impact

The implementation of the new data architecture and analytics capabilities had a significant impact on the telecom giant's marketing effectiveness. The AI-powered Customer Segmentation Model improved the precision of marketing campaigns, leading to better targeting and higher conversion rates. Additionally, the “Data-As-A-Service” model empowered third-party partners to participate in coordinated campaigns, further enhancing customer engagement and driving business growth.