Academic journal article The Journal of Business Forecasting Methods & Systems

New Product Forecasting Tools Find a Home in Telecommunications Credit Scoring

Academic journal article The Journal of Business Forecasting Methods & Systems

New Product Forecasting Tools Find a Home in Telecommunications Credit Scoring

Article excerpt

In a recent February address before the Freedom Forum at Georgetown University, Federal Communications Commission Chairman Reed E. Hunt gave a concise summary for the impact of recent events within the telecommunication industry. He said, "The biggest change over the last several years has been a massive acceleration in the movement from a monopoly based communications policy to a competition based policy." He further added that competition has completely changed the operating environment within the telecommunication industry in recent years citing long-distance statistics (prices down by 70% since 1984) as well as those for wireless (the average monthly cellular bill down by 61% since 1988).

The news is both good and bad. For the consumer, the news is good. Competition means lower prices making more products affordable. For example, products like "additional phone lines" for the PC have skyrocketed. Caller ID services have experienced increased penetration in recent years. However, a competitive market with falling prices means lower revenue for telecom firms. Although battling back by seeking new geographic markets oversees and implementing significant downsizing strategies, telecommunication companies are now putting more pressure on their risk managers to get the most out of their portfolios.

One solution that is finding increasing acceptance is credit scoring - a toolbox of statistical procedures, very similar to those used by market researchers in forecasting the demand for a new product. In the world of new product forecasting, mathematical techniques such as linear regression and discrete choice models have been historically popular because of their flexibility, ease of understanding, and relatively inexpensive way of extracting useful information about consumer preferences and behavior. These types of forecasts typically involve using intent to purchase surveys or data from product trials to obtain penetration rates and create simulation tools for target marketing.

In the credit and risk management world, much is the same. A credit score is a measure of the likelihood that a customer will become delinquent in paying their bills within a certain period of time, given knowledge of their past credit behavior. Statistical techniques incorporate a variety of predictive information into a single measure capable of rank ordering a portfolio by risk of delinquency, or in the world of a new product forecaster-the likelihood of product purchase. By using a single measure, telecommunications credit managers can quickly evaluate their portfolios and determine which customers are more risky than others.


Given recent increases in computing power, a number of tools and techniques are now available at the desktop level to examine credit information in a quick and understandable framework. SAS, SPSS, SHAZAM, LIMDEP, SPLUS, and other statistical software packages offer a variety of statistical techniques covering a wide range of applications. …

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