Purdue Churn Analysis Using Machine Learning and Calculating Performance

User Generated

Fbhaql

Mathematics

Description

Start by creating an Annotated Bibliography:

  • Identify the articles you intend to read carefully for your review.
  • Create a properly formatted APA compliant citation for each article
  • Create a synopsis of each reference.
  • Create a properly formatted in-line citation for each synopsis. Some can begin the paraphrase, others can be embedded, or at the end. Make it read well.
  • All references must follow full APA compliant standards. See the slides from Topic 2, the Perdue OWL, and your copy of the APA guidelines. This is a requirement, no exceptions.

Then begin to transition it to a Literature Review:

  • Begin a new blank page at the end of your Annotated Bibliography
  • Center the word "References" at the top of the page in 12 point Times New Roman font.
  • MOVE (cut and paste, do not copy and paste) all the references from your annotated bibliography to the References page in alphabetical order. This will be your first draft of your references page, and will not change again this semester unless you add or remove references.
  • Get your approved hypotheses from the previous assignment. Your hypotheses will be the point of your writing arrow. Put your hypotheses in paragraph form at the end of the annotations. This paragraph requires no heading but should be recognizable. Your hypotheses will be the very last thing in your Annotated Bibliography and Literature Review
  • Using your hypotheses as your point, and the various writing and organizing skills that have been discussed, organize all your annotations by whatever means you've chosen.

Look for examples:

User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.

Explanation & Answer

Please let me know if there is anything needs to be changed or added. I will be also appreciated that you can let me know if there is any problem or you have not received the work. Please let me know if there is anything needs to be changed or added. I will be also appreciated that you can let me know if there is any problem or you have not received the work Good luck in your study and if you need any further help in your assignments, please let me know Can you please confirm if you have received the work? Once again, thanks for allowing me to help you R MESSAGE TO STUDYPOOL NO OUTLINE IS NEEDED

Running head: CUSTOMER CHURN ANALYSIS USING MACHINE LEARNING

Churn Analysis Using Machine Learning and Calculating Performance
Name
Course
Date

1

CUSTOMER CHURN ANALYSIS USING MACHINE LEARNING

2

Churn Analysis Using Machine Learning and Calculating Performance
Introduction
Customer churn analysis has proven to be one of the critical data analysis tools used in
understanding the variables affecting the profit of a company. This tool is especially useful in the
evaluation of the different issues that arise during the relationship between the company and the
customers. The resulting improvement of such a customer relationship through the assessment of
the problems identified results in considerable customer satisfaction that potentially increases the
sales volume (Rygielski, Wang & Yen, 2002). For example, customer churn analysis has proven
critical in identifying those customers that are likely to shift to a new company, such that the
company can design a strategy to retain them if possible. Such an application is critical in
companies that operate in a saturated highly competitive environment, as the company will likely
never recover any lost customer (Ge, He, Xiong & Brown, 2017).
Annotated bibliography
Ascarza, E., Neslin, S. A., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., ... & Provost, F.
(2018). In Pursuit of Enhanced Customer Retention Management: Review, Key Issues,
and Future Directions. Customer Needs and Solutions, 5(1-2), 65-81.
Ascarza et al. (2018) go a step further in pursuing customer retention management. In
contrast with other works that have limited to the prediction of customer churn rates, the work
carried out by Ascarza et al. (2018) focuses at a broader perspective by considering non-binary
systems. In this sense, Ascarza et al. (2018) suggest that the classification as churn/non-churn is
somewhat limited to be able of accurately defining the complexity of the problem, as different
intermediate situations may arise depending on the number of variables included in the survey.
This paper is highly valuable for the research, as it provides a critical point of view from which

CUSTOMER CHURN ANALYSIS USING MACHINE LEARNING

3

to evaluate the effectiveness ...


Anonymous
Great content here. Definitely a returning customer.

Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4

Related Tags