Our client has been one of the largest US-based holding
companies for over 30 years specializing in a range of
telecommunication services, including voice and data
communications. They offer domestic Intra prefectural
communication solutions such as IP (Internet Protocol), fixed-
voice, and wireless broadband networks. Along with these
services, the company also deals with data transmission, leased
lines, mobile telecommunications, cable television, and
Striving to find more ways to connect people, our client needed personalized customer data insights to boost data access speed and improve customer experience. The company collected and stored over 50 million events from multiple sources and therefore needed a way out to eliminate extra data silos and enhance data quality and consistency.
Another standard issue was customer data management and optimization. Our client required implementation of a data analytics platform that could optimize and segment customers’ historical and current data to analyze the user preferences. They needed a data analytics platform for telecom that could track and improve customer churn rate, estimate customer lifetime value, and provide personalized product and content recommendations.
In short, our client needed data analytics services and solutions that could transform the telecom company into a customer-centric enterprise.
Our first step was to store and analyze data on the cloud using a broad spectrum of Apache technologies, including Spark and Hadoop. This allowed us to streamline the unwanted data cluster and analyze and prioritize data on one centralized platform in real-time. Our data analytics in telecom approach, coupled with various ETL tools, resulted in a master repository that provided a 360 degree overview of our client’s 90 million+ customers.
To make data insights accessible, we deployed BI solutions that provided actionable data visualization through interactive dashboards for various customer management areas. The team used artificial intelligence and machine learning tools to optimize more than 50 million transactional data points.
We followed an agile methodology to create an ecosystem that could process high volumes of data and classify it according to customer behavior and preferences. This implementation involved complex integrations with channels and other data systems. We deployed our data analytic software model on a test server where it started working with the actual data, and we could monitor results. Once the model ran successfully on the test server, we deployed it in production.
We were not sure if any company could deliver what we actually wanted with our data. Appinventiv exceeded our expectations when they proposed their strategy and implementation of a data analytics platform. We had a common understanding of how valuable every customer data is to us and keeping that in mind, Appinventiv did not only help us manage the user data but also enhanced the operations by upgrading our database.
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