BU.450.760 Assignment 2 Blue Apron CLV
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BU.450.760
Assignment 2: Blue Apron CLV
Consider the dataset D2.3, which contains simulated transactional data inspired by the subscription meal delivery service Blue Apron (https://www.blueapron.com/). The dataset records a random sample of Blue Apron’s subscribers’ activity (~22,400 individuals) during Jan 2019. A detailed codebook is available from the document C2.3.
Consider the following two specifications:
Specification 1:
Specification 2:
Using the Blue Apron data, perform the following tasks:
1. Task 1:
a. [2 points] Estimate the two listed specifications using churn indicator as the outcome and implementing f() as the logistic model.
b. [1 point] Select a model based on predictive performance criteria. Justify your decision.
c. [1 point] Use the selected model to predict churn probabilities for every customer in the sample. Present a histogram of these probabilities.
2. Task 2:
a. [2 points] Estimate the two listed models using MonthlyAddons as the outcome and implementing f() as linear regression.
b. [1 point] Select a model based on predictive performance criteria. Justify your decision.
c. [1 point] Use the selected model to predict MonthlyAddons for every customer in the sample. Make sure these predictions are within range. Present a histogram thereof.
3. Task 3:
a. [1 point] Export the full dataset to a csv file. The exported data must include individual predictions for churn probabilities (task 1) and monthly add-ons (task 2), each from their respectively preferred specification. After this file is saved as csv, convert it into xls or xlsx so that formulas can be saved (this last step is not in R, just a simple change of extension).
4. Task 4: consider the following policy currently being evaluated by BA’s leadership: by making a one-time $20 expenditure on each targeted customer (e.g., mailing a gift Champaign bottle), BA can reduce each targeted customer’s probability of churn by 0.01.
a. [2 points] Compute baseline CLV values for each customer in the initial scenario (i.e., if the new policy was not implemented).
b. [2 points] Determine the optimal targeting policy with unlimited budget. This is, determine the set of customers who the firm should send the one-time gift to. How many customers does the firm target?
a. [2 points] Compute the total financial gains/losses derived from implementing the campaign as the before/after difference between the total CLV values in the entire portfolio of customers. (Note: your calculations should account for targeting costs.)
Guidance
• Use a 70/30 training/validation data split
• For CLV calculations use the formula used in class,
1
CLV = MontℎlyNetContribution ×
1 − rd
where:
o MontℎlyNetContribution = (Montℎlybasepayments + predicted addons)× 0.3 (that is, the firm has a 30% margin)
o Retention rates vary individual by individual, as reflected by predicted churn
o Periods are months and the discount factor is d = 0.98
Submission guidelines
• Submit via Canvas, 8AM EST on the day of class 2
▪ Late submissions will be penalized
▪ Late corrections will not be accepted
• Note that assignments are automatically checked for similarity—it is ok to discuss with other students, it is not ok to copy
• Submit two files (one submission per individual):
1. Slide Deck (MS Powerpoint or pdf)
▪ In the slide deck, I expect you to present results in an executive way – you need to clearly describe:
• what is the goal (question/problem at hand)
• what you did to achieve the goal (analysis procedures)
• why you did it (rationales behind key steps)
• what you obtained (results)
▪ Use as many slides as you need.
▪ The title page must include your name.
▪ If you have worked/discussed with someone else, please also include their name(s) in a separate line on the title page.
2. R script file containing the codes that you used for your analysis.
▪ Include comments in the script to help the TA follow your procedures.
▪ The script file should be understood as a companion: you are encouraged to include screenshots of the command lines (with command line #) in your slide deck to demonstrate your key steps. This way TAs can easily go back and double check that your answers in the ppt are well supported.
2023-03-09