2 topics for discussion

Discussion

1

: For this discussion post, you will choose ONE of the following THREE topics to write about. You will make your initial post by 11:59 p.m. on Day 3, and you will respond to two of your classmates’ posts by 11:59 p.m. on Day 7. You may respond to any two posts, regardless of topic.

• Find an example of a business forecast that turned out to be completely wrong. What happened? What did the forecaster fail to anticipate? Finally, what were the consequences of the failed prediction(s)?
• In your own experience as a customer, can you think of a time when a company certainly (or almost certainly) lost money on a specific transaction with you? If so, what happened? Write about the experience and speculate as to whether the company’s assessment of your Customer Lifetime Value could have played a role.
• Using the def keyword, write an original function in Python. Describe the purpose of your function in words, and demonstrate the way it generates output based on user input. (you may either upload an HTML file generated in Jupyter Notebook that includes your code, results, and written statements, or a screenshot of your results in Jupyter along with your written statements — either way is fine).

Discussion 2: For this discussion post, you will choose ONE of the following THREE topics to write about. You will make your initial post by 11:59 p.m. on Day 3, and you will respond to two of your classmates’ posts by 11:59 p.m. on Day 7. You may respond to any two posts, regardless of topic.

• How effective are loyalty programs in improving customer retention? Write about a loyalty program that you personally find to be particularly effective (or ineffective). If you don’t have any personal experience to write about, that’s okay — you can look one up, and then write about it.
• Write about a time when you felt a strong reaction to a feature changing on a website or app that you frequently use. Did you love the change, or hate the change? Why? Speculate about the other versions of the feature that could have been used in an A/B test.
• Make up your own example of a situation in which a marketer might wish to perform a chi-square goodness of fit test to assess an experiment. Randomly generate values for your scenario using numpy. Run a chi-square goodness of fit test in Python to assess the null hypothesis. (you may either upload an HTML file generated in Jupyter Notebook that includes your code, results, and written statements, or a screenshot of your results in Jupyter along with your written statements — either way is fine).