Reference no: EM133370601

Consider our telco churn example from class. We would like to target some subset of our customers with a brand-new special offer prior to the expiration of their contracts, in order to minimize our losses due to churn. The special offer has a fixed cost of C irrespective of whether the customer accepts it. As we mentioned in class, in a real business application you want to design your solution carefully and specific to the application. You can assume that the value of a particular customer group, if she were to stay, can be estimated directly using this particular customer's past value and knowledge of the business going forward and the incentive. We would like to see whether our investment customers tend to cluster in understandable groups.

What precisely is the difference between segmenting my customers using clustering and segmenting my customers using tree induction, and when would I use one rather than the other? What is the practical difference- i.e., in the data mining process where and what would the main differences be?

Considering that you answered the last question, what might still be very unclear for you before running k-means algorithm?