This document provides the details for Assignment 2 of the CIS 356 course. The assignment asks students to imagine they are a marketing executive facing increased customer churn. Students are to write a 4-5 page paper evaluating the importance of unstructured data like customer memos and web blogs for churn analysis. They should propose steps to build a predictive model using text and web analytics, identify technologies for the model, and suggest a method to reduce churn based on their analysis. The paper must follow APA formatting and cite at least 3 quality resources.

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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

2. 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

3. 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.