By Richard Boire (auth.)

Big info is a becoming enterprise pattern, yet there little suggestion to be had on the best way to use it essentially. Written via a knowledge mining specialist with over 30 years of expertise, this ebook makes use of case stories to assist retailers, model managers and IT pros know the way to trap and degree information for advertising purposes.

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Extra resources for Data Mining for Managers: How to Use Data (Big and Small) to Solve Business Challenges

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Data miners must understand that if the focus is on retention, then cost-effectiveness may or may not play a role. For instance, a given company may have a highly selected group of high-wealth customers. These customers number about 5,000 and represent 5% of all the customers, but they account for 50% of all revenues. The company may find that attrition rates have increased by 50%. In this case, data miners could challenge whether the identification of high-risk high-wealth defectors through data mining will provide real business value.

For example, using gender as a potential variable in sports analytics involving major league baseball would be meaningless since the analytical file would consist of males only. We cannot draw any insight regarding gender as there are no females for comparative purposes. Another problem concerns the integrity of the data pertaining to values in a field that don’t make sense. 2, all product codes are comprised of letters and likely relate to a specific product category. The product category “999” suggests that some investigation is required to better understand what this relates to.

Engagement as a Quick Win Change and value are both good quick wins from a data mining perspective, yet another quick win relates to customer engagement. Although engagement can be similar to value, this is not always the case. A highvalue customer of a bank may have a large loan and large mortgage but make all payments through automatic withdrawal. If this is the way the customer interacts with the bank, the customer’s engagement level is very low. Other information must be considered to obtain a true reflection of customer engagement.

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