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Big data analytics for telecom industry- Thinking beyond connectivity

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Client Location

USA

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Client Industry

Telecom

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Services Provided

Consumer behaviour analytics tool & Master data visualization tool

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Technologies Used

Python, NodeJs, Angular, Java/Kotlin for Android & Swift for iOS, Greenplum, AWS

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Allocated Team Size

4
Engineers

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Project duration

52+ Weeks and ongoing

Client

Client Background

A leading player in the telecom industry with hundreds of thousands of subscribers, our client has changed the modes of communication forever. Providing a wide range of communication services, our client provides quality Voice and Data services for individuals, businesses, and organizations, empowering them to connect with each other.

Requirement

Our client needed a data analytics system to leverage data from multiple data sources across countries to help the service providers cross-sell and up-sell. They also needed a customized visualization tool that gives a 360-degree view of the data at hand. Using this data, our clients can categorize their subscriber's base, allowing them to identify subscribers with more data plans and pitch the adequate plan to them.

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Problems

Business Challenges

01.

One of the most critical challenges in the industry is customer retainment. Finding long-term subscribers is the primary goal of the client.

02.

Quality insights and data of subscribers are not easy to obtain. Our client has to get more creative and efficient to stay relevant in the market.

03.

Not all subscribers have the same usage pattern. The challenge is to devise models that attract maximum subscribers.

04.

To imagine and access the extracted data under different contexts can be challenging.

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Methodologies

Our Process

01

Setting up the premise

Understanding the nature of the data and the context involved to build a responsive visualization tool

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Pin-pointing potential data points

Optimizing the data pipeline to identify and leverage first-hand data and insights

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Being fluid for maximum adaptability

Organizing the data according to various categories for easy access and interpretation

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Focusing on maximum interaction

Building a responsive front-end structure for easy management and organization

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Our Team

Team Structure

The team is built in such a way that it's inclusive of diverse skills and shares the ability to deconstruct a problem into fragments logically.

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Data Engineer

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Cloud Architect

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Quality Analyst

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Backend Developer

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Projects Manager

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Front End Developer

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Tech Stack

Tools and Technologies

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Communication Tools:

  • Slack for internal communication.
  • Microsoft Teams, Outlook and Zoom for client communication.
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Technologies

  • Backend - Micro service, Qlik, APIGEE (Python, NodeJs)
  • Frontend UI - Angular Framework
  • Mobile App - Android -Java/Kotlin/iOS - Swift
  • Database - Greenplum
  • Cloud - AWS
  • Libraries: Iframe, Dygraphs , Google Map , Highcharts, Websocket
  • Third party API's: Hubspot, Constant contact, Mailchimp, Facebook
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Project Management Tools

  • JIRA- Task tracking and sprint plans.
  • Github- version control.
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Outcome

Solutions Offered

Result

Business Impact

Leveragi

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Leveraging quality insights

Leveraging first-hand data is hard, but efficient extraction and visualization allow businesses to leverage such key resources.

Enabling

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Enabling the client to cross-sell and up-sell

The first-hand data that is converted to insights allows businesses to tap into advanced consumer behavior, ultimately enabling them to cross-sell and up-sell.

Bringing

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Bringing multiple contexts to the process

Simplifying the overall process allows the client to bring a higher context into the picture. This helps the business to achieve a greater level of optimization and widen its customer base.

Frequently Asked Questions

How does big data analytics work for telecom companies?

By integrating network optimization with big data, telecom operators can improve the quality of their services. For example, using data collected from users in a particular area, companies can find solutions to issues such as improved connectivity.

What impact does big data analytics have on the telecom industry?

Big data can help telecom operators improve the quality of their services by integrating network optimisation. For example, companies can record and collect issues raised by users in a specific area and look for solutions to these issues, such as improving connectivity.

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