Company Overview:
Imagine a leading telecommunications company in South Africa serving millions of customers through mobile, broadband, and digital services. The company is facing increasing competition and is struggling to retain customers, impacting its revenue and market share.

The Challenge:
The provider is facing a significant challenge in reducing its high churn rate, driven by:

• Lack of Engagement Insights: Limited visibility into customer behavior and dissatisfaction points.
• Customer Pain Points: Issues such as dropped calls, inconsistent network performance, and billing errors are causing frustration among users.
• Revenue Loss: Customer churn is translating to substantial revenue loss and higher costs for acquiring new customers.

The Solution:
Digibit Solutions would partner with the telecom provider to deploy a targeted and data-driven solution:

1. Predictive Machine Learning Model:
   • Build a machine learning model that will analyze historical data, including call records, billing interactions, and customer complaints.
   • Identify customers at high risk of switching providers based on behavior patterns, usage trends, and sentiment analysis from support interactions.
2. Pain Point Identification:
   • The model can flag common issues such as frequent dropped calls, poor network quality in specific regions, and recurring billing discrepancies.
   • Provide actionable insights to address these problems proactively.
3. Retention Strategies:
   • Design personalized retention campaigns targeting at-risk customers with tailored offers such as discounts, free data, and improved service guarantees.
   • Empower customer support teams with real-time alerts and recommendations for resolving issues quickly.

The Outcome:
The implementation of these solutions could lead to remarkable improvements in customer retention and satisfaction:

• Reduced Churn Rate: The churn rate could drop by as much as 30%, preserving a significant portion of the customer base.
• Increased Customer Satisfaction: Satisfaction scores could improve by 20%, reflecting better service quality and engagement.
• Revenue Growth: The company could retain over $1 million in annual revenue by preventing customer attrition.
• Operational Efficiency: Insights from the model could enable targeted improvements,