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Data Mining and Predictive Analytics, 2nd Edition by Daniel T. Larose, Chantal D. Larose

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Chapter 1An Introduction to Data Mining and Predictive Analytics

1.1 What is Data Mining? What Is Predictive Analytics?

Recently, the computer manufacturer Dell was interested in improving the productivity of its sales workforce. It therefore turned to data mining and predictive analytics to analyze its database of potential customers, in order to identify the most likely respondents. Researching the social network activity of potential leads, using LinkedIn and other sites, provided a richer amount of information about the potential customers, thereby allowing Dell to develop more personalized sales pitches to their clients. This is an example of mining customer data to help identify the type of marketing approach for a particular customer, based on customer's individual profile. What is the bottom line? The number of prospects that needed to be contacted was cut by 50%, leaving only the most promising prospects, leading to a near doubling of the productivity and efficiency of the sales workforce, with a similar increase in revenue for Dell.1

The Commonwealth of Massachusetts is wielding predictive analytics as a tool to cut down on the number of cases of Medicaid fraud in the state. When a Medicaid claim is made, the state now immediately passes it in real time to a predictive analytics model, in order to detect any anomalies. During its first 6 months of operation, the new system has “been able to recover $2 million in improper payments, and has avoided paying hundreds of ...

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