The Indian mobile telephony market is a classic example of a volume based strategy. Average revenue per user (ARPU) is one of the lowest, while the subscriber figures are second only to China. The market itself is highly fragmented with more than 5 players holding less than 90% of the market. In this scenario, identifying new sources of revenues is critical for survival.
One of the most critical factors in Customer Relationship Management that can make or break
a company’s long-term profitability is churn. If a company can predict whether a customer is
likely to churn, it can take a more targeted approach to running promotions to reduce churn.
This is a sophisticated evolution from the traditional approach to incentivize all customers
Our business objective is to develop a model to profile the customers based on their usage patterns, the activities they indulge in and the preferred handsets in order to design a mobile handset based contract with service plans that will cater to their areas of interest. To realize our business objective, we need to apply unsupervised data mining techniques on given data to bring out clusters of people with similar data-usage attributes and mobile preferences. The clusters, so identified can then be targeted by service providers with customized data plans and contracts.
Prof. Galit Shmueli, Institute of Service Science, College of Technology Management, National Tsing Hua University, 101 Kuang Fu Road Sec. 2, Hsinchu 30013 Taiwan
