Wednesday, January 27, 2010

Overview of Analytics and Their General Business Application

Analytical tools enable greater transparency within an organization, and can identify and analyze past and present trends, as well as discover the hidden nature of data. However, past and present trend analysis and identification alone are not enough to gain competitive advantage. Organizations need to identify future patterns, trends, and customer behavior to better understand and anticipate their markets.

Traditional analytical tools claim to have a 360-degree view of the organization, but they actually only analyze historical data, which may be stale, incomplete, or corrupted. Traditional analytics can help gain insight based on past decision making, which can be beneficial; however, predictive analytics allows organizations to take a forward-looking approach to the same types of analytical capabilities.

Credit card providers offer a first-rate example of the application of analytics (specifically, predictive analytics) in their identification of credit card risk, customer retention, and loyalty programs. Credit card companies attempt to retain their existing customers through loyalty programs, and need to take into account the factors that cause customers to choose other credit card providers. The challenge is predicting customer loss. In this case, a model which uses three predictors can be used to help predict customer loyalty: frequency of use, personal financial situations, and lower annual percentage rate (APR) offered by competitors. The combination of these predictors can be used to create a predictive model. The predictive model can then be applied and customers can be put into categories based on the resulting data. Any changes in user classification will flag the customer. That customer will then be targeted for the loyalty program. Financial institutions, on the other hand, use predictive analytics to identify the lifetime value of their customers. Whether this translates into increased benefits, lower interest rates, or other benefits for the customer, classifying and applying patterns to different customer segmentations allows the financial institutions to best benefit from (and provide benefit to) their customers.


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