Imagine that you are a marketing executive at a major telecommunication company that has been facing the issue of increased customer churn recently. You are using traditional analytical methods with financial and location data to construct a predictive model for customer churn. However, you want to leverage memo data that has been obtained from the customer contact center and local sells stores. You also believe that the Web blogs and posts contain very important information that you could use to attack customer churn. Examining the unstructured data and the structured data together may provide more insight on why customers want to leave one company and join another company.
Write a four to five (4-5) page paper in which you:
Evaluate the importance of unstructured data in the churn analysis.
List other structured and unstructured data other than the memo and Web blogs that you need to use in your churn analysis.
Propose a series of steps for deriving a predictive model using text and Web analytics. Provide at least one (1) example of how the process can be integrated in the modeling process using structured data.
Identify at least two (2) technologies that you can use to construct the predictive model and highlight their pros and cons.
Explain why voice of the customer carries much more insight into churn analysis and prevention.
Suggest at least one (1) churn prevention method that you can use to reduce your churn rate based on your model.
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