CASE STUDY

Reducing the Customer Churn Rate for a Telecom Client

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    Industry | Communication Services
    Specialty | Telecom (mobile, TV, internet)
    Location | Netherlands

    The client is a European company providing telecommunication services for mobile, internet, and TV.

    Their expertise ranges from installing, acknowledging and solving problems associated with troubleshooting and fixing devices and online TV, digital antenna, and secure internet usage.

    Industry | Communication Services
    Specialty | Telecom (mobile, TV, internet)
    Location | Netherlands

    The client is a European company providing telecommunication services for mobile, internet, and TV.

    Their expertise ranges from installing, acknowledging and solving problems associated with troubleshooting and fixing devices and online TV, digital antenna, and secure internet usage.

      By downloading this content, you are agreeing to receive communications from Royal Cyber, including our Insights newsletter.

      [recaptcha]

      Challenges

      The client was registering a high customer churn rate
      Reasons for customers switching to another telecom service provider couldn’t be ascertained
      No plan on how to retain existing customer

      How We Did It

      A machine learning model created through Python Stack
      An analytics dashboard that provided data and suggestions for customer retention and reducing churn rate
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      Key Outcomes

      80%

      The machine learning model was over 80% accurate

      25%

      The customer churn rate decreased by 25%

      70%

      More than 70% of customer complaints were handled from the data provided by analytics

      Hassan Sherwani

      Hassan Sherwani

      Head of Data Analytics & Data Science

      ‘It was a productive and learning experience working with a client from the telecom services sector. Our team understood the client’s pain points and expectations and devised a roadmap on how a machine learning solution could help them reduce their customer churn rate, retain more, and understand why they would switch to another telecom service provider.’

      Python Stack

      An ML model through Python Stack

      Audience

      • Executives, CTOs, Director

      • IT Consultants

      • Business Analysts

      • Project Managers

      • IT Project Coordinators

      • Architects and Specialists

      Case Study

      Learn how Royal Cyber helped a telecom business reduce customer churn rate, improved complaints management, and customer behavior predictions.

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