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 ABSTRACT
The mobile phone industry has reached a saturation point. With low growth rates and fewer new customers available to acquire, competition among mobile operators is now focused on attracting competitors' customers. This leads to a significant downward price pressure, the inability by mobile phone providers in deriving reasonable returns from basic telephony services, and an increasing reliance on value added services (VAS) for revenue growth. There are today thousands of such services available for companies to sell to their customers daily. These services include, for example, the provision of sports information, ring-tones, personalized news, weather forecast, and financial trends. Because of the many possible offers, and of the limited contact opportunities (operators tend to cap the number of commercial messages sent to their users and phones have limited-size screens), data mining can play an important role in optimizing message targeting. In this paper we describe our experience in developing a successful automatic system to target users with the most relevant offers. We describe the proposed data mining methods and report on their performance. In addition, we discuss several experiments we implemented on live data. These experiments have been useful to tailor our approach to the specific characteristics of the market under study. We believe this is a very interesting domain for data miners though it is still fairly unexplored. This is despite the availability of very large and detailed logs of customer activity.
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