Big data in Telecom: User churn prediction

Big data in Telecom: User churn prediction

Preserving customer loyalty through leading-edge software solutions for Telecom

X4

lower customer churn rate

15%

higher customer satisfaction rates

35%

annual revenue increase

Big data in Telecom: User churn prediction
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Challenge

Elevated customer attrition rates resulting in significant revenue losses and potentially shrinking market share.

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Solution

Develop a telecom data analytics solution that would integrate CRM, ML, predictive analytics, and big data technologies to timely predict and avert account cancellation.

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Tech stack

Hadoop, TensorFlow, Microsoft Dynamics, Python.

Client

Our client is a regional mobile network operator, providing a range of telecommunication services across Eastern and Central Europe for over 10 years. Facing the growing competition in the industry and realizing that innovative tech solutions should become the backbone of their business success, our partner turned to Modsen to hire a team of big data engineers who would develop an advanced data analytics platform and deliver business-focused end-to-end software development services.

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Challenge 

During the previous financial year, our client lost over 5% of their customer base to market competitors. Seeing no objective reason for such extensive customer loss over a relatively small period, our partner decided to leverage the power of big data and predictive analytics for telecom industry to look closely into customer behaviour and timely implement retention strategies to win subscriber loyalty back.

In technical terms, Modsen team was entrusted with custom end-to-end development of a big data integration and analytics platform, incorporating CRM, predictive modelling, and real-time analytics for effective customer churn prevention. The software had to perform the following key tasks:

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Analyse usage patterns, billing history, and customer service interactions.

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Identify at-risk customers and the reasons for potential churn.

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Offer customer-tailored retention campaigns.

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Provide real-time monitoring and churn alerts.

Team

1

Data scientist

2

Data engineers

2

Software developers

2

UI/UX designers

1

Business analyst

1

Project manager

2

QA testers

Big data in Telecom: User churn prediction team
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Development process

Requirements gathering

To develop a sophisticated big data analytics platform for telecom, capable of processing vast amounts of customer data to predict churn and facilitate proactive retention efforts, our team of telecom IT engineering experts closely collaborated with the client’s customer service, marketing, and IT departments.

During the requirements gathering stage, we collected the input of the key stakeholders and drafted a comprehensive document that outlined project essentials, including:

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Machine learning models to predict churn based on historical data and behavioral patterns.

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Integration with existing corporate CRM systems to manage and track retention strategies.

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Real-time analytics and reporting capabilities to monitor churn risk and the effectiveness of retention campaigns.

Planning

Having a comprehensive requirements guideline approved by our client, we got down to Agile-based project planning. Given the scale of the business challenge our partner was dealing with, the deadlines were pressing and any issue that could arise during the development process had to be tackled seamlessly by the team of experienced telecom software engineering experts.

Utilizing Agile methodologies, Modsen BA and PM chosen for the project crafted a detailed project plan, that defined the scope of work, set project milestones, allocated resources, and identified the essential features of the big data analytics platform architecture.

Team assembly

Matching a team of engineering experts to a specific project is a task that only a company CTO can manage with perfect precision. We’ve tested this hypothesis dozens of times and made sure that Modsen CTO’s in-depth knowledge of the team combined with his extensive experience in a variety of multi-industry projects allow our clients to hire the IT experts they truly need and receive the level of project implementation they’d expected.

To build a big data analytics platform for the telecom industry we assembled a team of:

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1 Data Scientist for developing machine learning models.

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2 Data Engineers to handle data integration and management.

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2 Software Developers for platform development and integration.

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2 UX/UI Designers to create user-friendly interfaces.

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2 QA testers to verify the quality of the final product at every step of the way.

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1 Business Analyst to ensure alignment with business objectives.

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1 Project Manager to oversee the project and ensure timely delivery.

Design

Modsen Design Studio professionals focused on creating a scalable and user-friendly telecom data analytics platform interface, designing intuitive dashboards for real-time data visualization and reporting, and convenient campaign management interfaces.

Design

Product building

The development process followed Agile methodologies, utilizing sprints to manage the project in controlled increments. This approach allowed our team to iterate on the platform's features and functionalities, making continuous improvements based on real-time feedback and evolving business requirements. Each sprint cycle included planning, development, testing, and review phases, ensuring that the data analytics platform for our telecom partner met the desired quality standards and functionality goals.

Key elements in the product development stage encompassed:

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Data integration: Integrating diverse data sources such as usage patterns, billing information, and customer interactions to build a comprehensive data warehouse.

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Model training: Developing and training machine learning models to predict customer churn and identify at-risk network subscribers.

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Feature implementation: Building features for real-time analytics in big data, visualization, and automated alerts to support proactive customer engagement strategies.

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Testing and QA: Conducting rigorous testing, including functional, performance, and security assessments, to ensure the platform's reliability.

Integration

The integration phase involved deploying the telecom data analytics platform into the client’s existing IT environment, connecting it with CRM systems and other data sources, and making sure that the big data analytics system we built was stable and operated impeccably by conducting comprehensive testing. Apart from that, Modsen project developers prepared software management guidelines and provided training to the client's staff members on the most effective use of the new sophisticated analytics tool.

Servicing

Stating the development of long-term partnerships as one of the key company priorities, our team conducts post-deployment software support, which includes performance monitoring, updating, and troubleshooting. Post-launch servicing and regular check-ins with our client ensured that the big data analytics platform for telecom continued to meet their evolving business needs and effectively navigated the challenges it was built to tackle.

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Result

The development and integration of the data analytics system into the client’s customer management routine significantly reduced the attrition rates which was the primary concern the platform had to remedy.

The final telecom data analytics software solution built by Modsen team allowed our partner to:

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Aggregate and process large volumes of data from various sources, such as customer interactions, usage patterns, billing information, and service history.

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Analyse historical data and identify patterns that are indicative of potential churn.

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Classify customers into different risk categories based on their likelihood of leaving.

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Segment customers based on the churn risk score and other relevant attributes.

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Implement personalized and targeted retention strategies.

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Integrate predictive analytics tools with the CRM system to manage customer relationships and execute retention strategies.

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Monitor the indicators of potential churn through data integration and analytics.

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Gain insights into churn trends, the effectiveness of retention campaigns, and other key performance indicators.

X4

lower mobile network customer churn rate in 6 months

35%

annual revenue increase compared to the previous fiscal year

15%

higher customer satisfaction rates according to online surveys
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