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498 Part Three Key System Applications for the Digital Age

TABLE 12.5 EXAMPLES OF BUSINESS INTELLIGENCE PREDEFINED
PRODUCTION REPORTS

BUSINESS FUNCTIONAL AREA PRODUCTION REPORTS
Sales Forecast sales; sales team performance; cross selling; sales cycle times
Service/Call Center Customer satisfaction; service cost; resolution rates; churn rates
Marketing Campaign effectiveness; loyalty and attrition; market basket analysis

Procurement and Support Direct and indirect spending; off-contract purchases; supplier
performance
Supply Chain Backlog; fulfillment status; order cycle time; bill of materials analysis
Financials General ledger; accounts receivable and payable; cash flow; profitability
Human Resources Employee productivity; compensation; workforce demographics;
retention

predictors of driving safety when issuing auto insurance policies. A collection
of such predictors is combined into a predictive model for forecasting future
probabilities with an acceptable level of reliability.
FedEx has been using predictive analytics to develop models that predict
how customers will respond to price changes and new services, which custom-
ers are most at risk of switching to competitors, and how much revenue will be
generated by new storefront or drop-box locations. The accuracy rate of FedEx’s
predictive analytics system ranges from 65 to 90 percent.
Predictive analytics are being incorporated into numerous business intelli-
gence applications for sales, marketing, finance, fraud detection, and health
care. One of the most well-known applications is credit scoring, which is used
throughout the financial services industry. When you apply for a new credit
card, scoring models process your credit history, loan application, and purchase
data to determine your likelihood of making future credit payments on time.
Telecommunications companies use predictive analytics to identify which
customers are most profitable, which are most likely to leave, and which new
services and plans will be most likely to retain customers. Health care insurers
have been analyzing data for years to identify which patients are most likely to
generate high costs.
Many companies employ predictive analytics to predict response to direct
marketing campaigns. By identifying customers less likely to respond,
companies are able to lower their marketing and sales costs by bypassing this
group and focusing their resources on customers who have been identified
as more promising. For instance, the U.S. division of The Body Shop plc used
predictive analytics and its database of catalog, Web, and retail store customers
to identify customers who were more likely to make catalog purchases. That
information helped the company build a more precise and targeted mailing
list for its catalogs, improving the response rate for catalog mailings and
catalog revenues.
Big Data Analytics
Many online retailers have capabilities for making personalized online product
recommendations to their Web site visitors to help stimulate purchases and
guide their decisions about what merchandise to stock. However, most of
these product recommendations are based on the behaviors of similar groups

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