Data Mining Techniques For Marketing, Sales, and Customer by Gordon S. Linoff, Michael J. A. Berry

By Gordon S. Linoff, Michael J. A. Berry

* jam-packed with greater than 40 percentage new and up to date fabric, this version indicates enterprise managers, advertising and marketing analysts, and knowledge mining experts easy methods to harness primary info mining tools and strategies to resolve universal kinds of company difficulties* each one bankruptcy covers a brand new facts mining approach, after which exhibits readers the best way to practice the approach for superior advertising, revenues, and customer service* The authors construct on their popularity for concise, transparent, and useful factors of complicated ideas, making this booklet the right creation to facts mining* extra complex chapters conceal such issues as the best way to arrange information for research and the way to create the mandatory infrastructure for information mining* Covers center information mining recommendations, together with choice bushes, neural networks, collaborative filtering, organization ideas, hyperlink research, clustering, and survival research

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How can a large enterprise accomplish some­ thing similar when most company employees may never interact personally with customers? Even where there is customer interaction, it is likely to be with a different sales clerk or anonymous call-center employee each time, so how can the enterprise notice, remember, and learn from these interactions? What can replace the creative intuition of the sole proprietor who recognizes cus­ tomers by name, face, and voice, and remembers their habits and preferences?

Although that knowl­ edge is one reason to shop there rather than at a big discount liquor store, it is their intimate knowledge of each customer that keeps people coming back. Another wine shop could open across the street and hire a staff of expert oenophiles, but it would take them months or years to achieve the same level of customer knowledge. 1 Chapter 1 Well-run small businesses naturally form learning relationships with their customers. Over time, they learn more and more about their customers, and they use that knowledge to serve them better.

T I P Customer patterns become evident over time. Data warehouses need to support accurate historical data so that data mining can pick up these critical trends. One of the most important aspects of the data warehouse is the capability to track customer behavior over time. Many of the patterns of interest for customer relationship management only become apparent over time. Is usage trending up or down? How frequently does the customer return? Which channels does the customer prefer? Which promotions does the customer respond to?

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