CIS 356 Week 6 Assignment 2: Attacking Customer Churn with Text and Web Analytics LATEST
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CIS 356 Week 6 Assignment 2: Attacking Customer Churn with Text and Web Analytics NEW
Assignment 2: Attacking Customer Churn with Text and Web Analytics
Due Week 6 and worth 100 points
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.
Use at least three (3) quality resources in this assignment.Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
The specific course learning outcomes associated with this assignment are:
Explain the application of business analytics and data mining in Business Intelligence (BI).
Describe methods of data mining and what insights may be gained by these methods.
Use technology and information resources to research issues in data mining.
Describe the application of data mining to information, data, and databases.
Explain the role of text mining and Web mining as they relate to BI and DSS.
Develop a decision support solution to solve a proposed business problem.
Write clearly and concisely about Decision Support and Business Intelligence topics using proper writing mechanics and technical style conventions.
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