This is module 4 of 10 for the Business Analytics course.
Readings
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Self-Directed Learning
Search the internet for articles, other resources, and examples of how to use business analytics is used in marketing. See what you can learn from what others are saying. Marketing is one of the most active users of data on customers, especially data collected on the internet.
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Discussion of Module Topic
Write down your thoughts and experiences while you’re learning. For this module, this should include writing about your experience of learning about the use of data analytics in marketing. Here are some prompts to get you started for writing about the module topic:
- Provide links to any useful and relevant resources about data analytics in marketing that you can find on the internet.
- Do you have any experience in using analytics in marketing?
- How have you experienced analytics in marketing as a consumer?
- Why is data analytics so important in marketing?
- What do you find interesting or useful about analytics in marketing?
- Describe any interesting examples of how data analytics is being applied in marketing.
- Do you have any concerns about the use of data analytics in marketing?
- What do you think is the future of data analytics in marketing?
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Discussion of Module Exercises
For this module, the discussion of the exercises should include writing about your experience of working on the case. This can be done in the Posts tab for the module. Here are some prompts to get you started for writing about it:
- What did you find interesting about the data?
- What was your experience of using statistics to summarize the data?
- How did you decide which type of data visualization would be useful for this exercise?
- What did you find interesting about the results of the statistics and data visualization?
- Why is the final step of drawing business insights so important?
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Module Exercises
For this module, we will be working on a Disney Marketing Case developed by Dr. James Mourey of DePaul.
The materials for the case include the case description and the case data. These files can be found on Microsoft Teams under the M4 files tab in the “Disney Case Materials” folder. The end of the case description has 10 case questions that we will work on together.
Your Files contributions for this module will be based on your contribution to the case analysis. All of the relevant shared files for working on the case are under the M4 files tab in the “Disney Case Work” folder.
Excel work for answering the case questions should be done in the Excel files, such as “Q1 Work Disney.” I have included files for Q1, Q2, Q5, and Q7. If you would like to create a file for another question, you are welcome to do so. Each workbook has a ReadMe worksheet at the beginning. If you add your own worksheet, make sure to list it, your name, and the purpose on the ReadMe.
Answers to the questions can be written in the “Q1-Q10 WriteUp Disney.docx” file. Simply write in the spaces for each question. You DO NOT need to answer every question. You can contribute to the questions that you find most interesting. Remember that this is an analytics course, so some of your work should be related to the data. You can do your work in an Excel workbook and then include statistics or screenshots of charts in the Word doc.
The last part of the Files contribution is the file “Q1-Q10 Presentations Disney.pptx.” This is a shared PowerPoint deck where you can add your own slides for questions that you worked on. In the second session for module 4, the main part of the session will be presenting this slide deck. When a student’s slide is next, they will present their work. Please be aware of what other slides are being presented and try to present ideas that are new and relevant. Like the Word doc, the slides should primarily highlight thoughts and results related to the analytics. Statistics and screenshots are encouraged. This slide deck should be viewed as the culmination of your work on the Disney Case.
The goal of this exercise is start using data analytics to provide insights for business decision-making.