Marketing Analytics

Assignment IV: AB Testing, a Statistical Test, and a Dashboard

Once you have completed this assignment, you will upload two files into Blackboard: The .ipynb file that
you create in Jupyter Notebook, and an .html file that was generated from your .ipynb file. If you run
into any trouble with submitting the .html file to Blackboard, you can submit it as a PDF instead.
For any question that asks you to perform some particular task, you just need to show your input and
output in Jupyter Notebook. Tasks will always be written in regular, non-italicized font.
For any question that asks you to include interpretation, write your answer in a Markdown cell in
Jupyter Notebook. Any homework question that needs interpretation will be written in italicized font.
Do not simply write your answer in a code cell as a comment, but use a Markdown cell instead.
Remember to be resourceful! There are many helpful resources available to you, including the video
library, the class slides, the recitation sessions, the Zoom office hours sessions, and the web.
Part I: A/B Testing Sales Promotion Strategies
Lobster Land is considering some different promotional campaigns for its online merchandise
store. To compare the performances of three different ad campaigns, Lobster Land has teamed
up with a convenience store retailer known as Kwik-E-Mart.
Inside of various Kwik-E-Mart locations throughout Maine, Lobster Land ran three unique types
of promotions. In each case, Lobster Land used unique QR codes; these codes enabled Lobster
Land to know the exact amount of online merchandise revenue generated by each campaign.
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This dataset contains the following variables:
MarketID Unique identifier for a market
MarketSize Size of market area by sales
LocationID Unique identifier for each store location
AgeOfStore Age of store in years
Promotion One of three promotions that was tested
Week One of the four weeks when the promotions were run
SalesInThousands Sales amount, in thousands, per row (each row is a unique LocationID,
Promotion, and Week combination)
Lobster Land is hoping that you can help them to better understand this data! Specifically, they
want to know about Promotion1, Promotion2, and Promotion 3. Can you analyze
campaign_data.csv and then offer them any insights about which Promotion is most
effective at increasing sales?
A. Generate a barplot to show the average SalesInThousands values, separated by
the different promotion types.
a. Describe your barplot in 1-2 sentences.
B. You want to make sure that the promotions were evenly-balanced across time.
Create another barplot — this time, build a barplot that shows the number of
instances in which each of the promotions was held. Include the ‘week’ variable
in your plot, too.
a. What does this show you about the experiment design? Do you think
the ‘week’ could be a confounding variable in the experiment?
C. Next, generate some summary stats here — group the observations by
‘Promotion’ and then describe the store ages.
a. How would you describe these results in general? You won’t use a
statistical test here, but instead, just summarize what this seems to show
— does the age profile of the stores seem to be very different, or does it
look like it’s pretty similar across these three groups?
D. Using an appropriate statistical test for each comparison, compare every possible
promotion (Promotion 1 vs. Promotion 2, Promotion 2 vs. Promotion 3, and
Promotion 1 vs. Promotion 3) to assess its impact on sales.
a. What were the t-statistics and p-statistics for each head-to-head test?
b. Based on these results, what can you conclude about the promotions?
Part II: Using a Statistical Test to Evaluate a Claim
A traveling salesman comes to Maine with a proposal for Lobster Land: He would like to set up
a dice game “called Lucky Evens” that will be held inside the park. He is offering to pay Lobster
Land $700 per day for the rights to operate his dice game.
If Lobster Land allows this man to run the game, it will work like this: Any park visitor can pay
$12 to roll one of his six-sided dice. If the dice roll results in a 2, 4, or 6, the visitor will receive
$20; however, if the roll results in a 1, 3, or 5, the visitor will lose his $12.
You are a little bit suspicious about this traveling salesman, so you decide to test out his dice.
On the one hand, you think that visitors to the park might enjoy this game, as well as the
opportunity to earn some extra money during their visit. On the other hand, what if this guy is
a thief who wants to just steal from the park’s guests by cheating them out of their money?
You roll the visitor’s dice 60 times, and you record the following results:
Recorded Values (60 Rolls)
Dice Value Number of Times Observed
13
7
12
8
14
6
Still unsure about whether to trust this out-of-town salesman, you decide that perhaps 60 rolls
of the dice were not enough. You bring over one of your analytics interns and you ask him to
double your previous effort — he needs to roll this thing 120 times! After rolling 120 times, he
records the following results:
Recorded Values (120 Rolls)
Dice Value Number of Times Observed
26
14
24
16
28
12
A. Using the results from the first set of dice rolls (in which you rolled the visitor’s
dice 60 times), conduct a chi-square goodness of fit test in Python.
a. What is the null hypothesis of this test? What is the alternative
hypothesis?
b. What is the p-value of this test? Based on this value, what will you
conclude? Be sure to mention the null hypothesis in your answer to this
question. (you can assume that Lobster Land uses an alpha value of 0.05
for statistical tests)
B. Now, using only the results from the second set of dice rolls (in which the intern
rolled the visitor’s dice 120 times), conduct a chi-square goodness of fit test in
Python.
a. What is the null hypothesis of this test? What is the alternative
hypothesis?
b. What is the p-value of this test? Based on this value, what will you
conclude? Be sure to mention the null hypothesis in your answer to this
question.
C. Demonstrate where the two chi-square values used above came from. Use
Jupyter Notebook to do this, but do not use any Python libraries or modules.
Instead, show the calculation used to determine the chi-square value for each
case (the 60-roll trial, and the 120-roll trial).
i. What pattern did you notice in the results, when comparing the
observed values from the two trials?
ii. If your chi-square value from the second trial was different from
the one you obtained from the first trial, describe in about 1-2
sentences why you think it changed. Just a couple sentences is
enough here — a full credit answer will ‘connect the dots’ between
the formula for the chi-square value and the way it was impacted
by the data here.
D. What should Lobster Land tell the traveling salesman? Why?
E. If using more dice rolls in the 2nd trial seems to have impacted the results, write
a completely intuitive (no math!) explanation for why this might make sense. To
write this answer, don’t use any math or statistics references. Instead, be
creative, and think about how you might explain to a small child (or an adult that
doesn’t know about math) about the impact of having more evidence in order to
make the decision here (2-4 sentences here will be enough).
Part III: Using Tableau to Build a Dashboard:
A. Bring lobster22.csv into your Tableau Public environment. (the same dataset that we
used for Homework #1)
B. Using any layout style, build a dashboard that includes any four unique visualizations of
your choice. By “unique” this just means that you should not build two of the same type
of plot (e.g. not more than one histogram, not more than one barplot, not more than
one treemap, etc.). Give a title to each of your four plots.
C. Write a one-paragraph description of your dashboard. Write about the plots that you
made and describe your process. You can do this in any file format, and upload it with
your Assignment 4 submission.
D. Paste a link to your file in the same document that you used to write the description. If
you used Tableau Desktop, you can just upload the .twbx file instead.
Note: This section is intentionally very open-ended. Each submission will be unique. The goal here is
not to arrive at a single “correct” answer but to have everyone gain some hands-on experience with
building a dashboard in Tableau. The dashboards will not be scored by some ‘beauty contest’ measure —
the key here is to (1) make a good-faith effort to build a dashboard with four separate types of
visualizations, and (2) include a thoughtful narrative paragraph. Every answer that does those things will
receive full credit for this section.