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Proactive Churn Management in Telecommunications: Optimizing Customer Retention and Profitability

This presentation discusses Suntel's shift from reactive to proactive customer management in a deregulated market, focusing on retaining and maximizing customer value. The company's data warehouse and decision support technology are leveraged to address churn modeling requirements. Various churn types and modeling techniques like Decision Trees and Neural Networks are explored, along with considerations for segmenting customer profiles and the impact of latency in predictive modeling. The Greek telecommunications market is also analyzed in relation to these concepts.

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Proactive Churn Management in Telecommunications: Optimizing Customer Retention and Profitability

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  1. „Churn Management in Mobile Telecommunications“Group B RequirementsWritten Work : pagesPresentation: 20 Minutes Discussion: 10 Minutes Dr. Evangelos Xevelonakis

  2. The Business Problem • Suntel, a large telephone company had been investing in decision support technology for several years. The mobile telephone market in this country had recently been deregulated and several recent entrants were growing rapidly. At the same time, the market was maturing and they recognised the need to move from reactive to proactive customer management. The maturing of the market and the increasing competition was now leading the company to focus on existing customers, how to keep them, and how to make them more profitable. • The company has over five million customers, with a very significant portion concentrated in major cities. Every month, about 1 percent of the customer base churns. Dr. Evangelos Xevelonakis

  3. The Business Problem • The company has developed a data warehouse with a lot of customer information. • Business users had access to an decision support application based on relational OLAP. They could slice and dice marketing and sales data along a number of dimensions, such as handset type, region, and time of day. The system allows quick answers to queries such as “What is the churn rate in April and May for Premium customers versus Classic customers?“ • Throughout the project, the company was interested in learning how modelling efforts in the future would interact with other systems. What other requirements does churn modelling impose on the data warehouse and on the data marts? Dr. Evangelos Xevelonakis

  4. Questions • Discuss and analyse the different types of churn. Which type of Churn is of Interest? • Churn Modelling depends critically on the data that is available. Suggest a data set with the required variables. Which variables can you derive? • You can use Decision Trees or Neural Networks. Decide which of these techniques is more appropriate and why • The marketing department of the company had specified an interest in two different customer segments: Premium (high value) and Classic (low value).Is the right solution to build one model and include Premium as a flag? Or is it better to build two different models, one for the Premium segment and one for the rest? And how can we make this decision? • The time element is critical to successful model building. It is critical to separate time into past, the present, and the future. Discuss the effect of latency. • Discuss the situation of telecommunication market in Greece. Dr. Evangelos Xevelonakis

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