Question 1 (20 points):

Choose a sports club (of your own choice) and briefly describe the business.

A.   Identify and evaluate the role of 'marketing analytics' in the current activities of the focal business in delivering value to its customers (your answer should cover but not be limited to describing the data at hand, the methods that are currently applied, and how these are translated into value; 7.5 points)

B.   Critically discuss other opportunities (than those you discussed in part A) that can provide the focal business with delivering (even more) value to its customers (7.5 points)

C.   In your answer, discuss the potential ethical issues that may arise from the implementation of 'marketing analytics' for the focal business (5 points)

Notes that you should consider in your answer:

•   Premier League clubs, Cricket clubs, Rugby clubs, and Golf clubs are just some examples of sports clubs.

•   Your answer (including arguments, discussions, recommendations, etc.) must be realistic, coherent, and logical.

Question 2 (35 points):

Imagine that you work for an energy tech company, namely, gSUNSolar. The company is       active in selling and installing solar panels for homeowners. The recent increase in the price  of oil and gas has increased interest in new renewable energy sources; therefore, gSUNSolar wants to get the most out of this situation.

In this regard, the CEO decided to run an ad campaign on Facebook. The CEO decided to run the following two different types of ad campaigns on Facebook:

1.   ‘No targeting’: do not target any specific user on Facebook

2.   ‘Targeting’: target users who are interested in one of the following topics (which is provided by Facebook):

a.   “Vegan food”

b.   “Saving on energy bills”

c.   “Saving the planet”

d.   “Luxury products”

e.   “Green energy”

The CEO of gSUNSolar is now done with the campaign and is asking you to evaluate the results. In particular, the CEO would like to understand the following:

A. Did the ‘Targeting’ campaign generate more shares (compared to the ‘No targeting’ campaign)? (explain) (15 points)

B. Which topic(s) should be considered for targeting users in future campaigns (that potentially create more shares)? (10 points)

To this end, you are provided with a sample of Facebook campaign results, containing around 100,000 users (around 40% were in the ‘Targeted’ campaign and the rest in the ‘No targeting’ campaign; see 'gSUNSolar_a.csv' dataset). The dataset includes the following       variables:

Use the provided dataset (i.e., 'gSUNSolar_a.csv') and run a linear model that helps you to address CEO’s questions.

In addition to answering the questions raised by the CEO, you conducted a similar analysis (as above) to investigate:

C. Did the ‘Targeted’ campaign generate more profit than the ‘No targeting’ campaign? Discuss ‘why’ based on your results (10 points).

To investigate the question in part C, you use the same dataset as above and replace the variable ‘share’ with two other variables: ‘profit’ (i.e., expected profit in £ realized from those users that shared the ad) and ‘ad_cost’ (i.e., the money in £ that you paid to Facebook to display the ad to a user). All this information is available in dataset 'gSUNSolar_b.csv.'

Notes that you should consider in your answer:

•   Include your R code and its respective results in your solution.

•   Make sure you clearly explain, justify, and detail all the assumptions and steps in your solution. These might include data cleaning (e.g., dropping variable(s), observation(s), changing type of variable(s), etc.) or any other assumptions or steps.

•   Carefully and completely interpret your results (including all your coefficients).

•   Critically evaluate the implications (based on all your results) for gSUNSolar. Make sure that you use specific and concrete examples in your solution.

•   The only part NOT counted in your word limit is your output from the RStudio’s console. Everything else (e.g., your code, tables, and words in your figures) counts.

Question 3 (20 points):

An apparel retailer would like to understand its current customers better. To this aim, the retailer is asking you to: Identify and group its existing customers into meaningful clusters    that individuals within a cluster are similar to each other but different than those individuals in other clusters.

To this end, you put together a dataset (i.e., 'Retailer.csv') including the following list of variables:

Notes that you should consider in your answer:

•   Based on the structure and the information in the dataset (i.e., 'Retailer.csv' file), apply your suggested method using R. Include your R code and its respective results in your solution.

•   Make sure you clearly explain, justify, and detail all the assumptions and steps in your solution. These might include data cleaning (e.g., dropping variable(s), observation(s), changing  type  of  variable(s),  etc.),  your  decision  (and  justification  why!)  on  the number of clusters, or any other assumptions or steps.

•   Carefully and completely interpret your results. Your answer should cover but not be limited to explaining why the final solution is appropriate, describing the characteristics of the clusters, and discussing managerial implications.

•   Critically evaluate the implications (based on your results). Make sure that you use specific and concrete examples in your solution.

•   The only part NOT counted in your word limit is your output from the RStudio’s console. Everything else (e.g., your code, tables, and words in your figures) counts .

Question 4 (25 points):

Assume you are the CEO of a real estate company. You would like to gain more insights about the market. To this end, you are provided with the 'Sample.csv' dataset. The dataset has the following list of variables:

Based on the structure and the information in the dataset (i.e., 'Sample.csv' file):

A.   Find the correlation between 'price' and 'area_a' and interpret it; Find the correlation between 'area_b' and 'area_b' and interpret it (5 points).

B.   Suggest a tree-based model that allows you to understand what house features affect the property's price (in £). Apply your suggested method (using R) and explain your results (15 points).

C.   Evaluate your model performance in B. (note that you are not required to split your dataset into train and test; 5 points).

Notes that you should consider in your answer:

•   Include your R code and its respective results in your solution.

•   Make sure you clearly explain, justify, and detail all the assumptions and steps in your solution. These might include data cleaning (e.g., dropping variable(s), observation(s), changing type of variable(s), etc.) or any other assumptions or steps.

•   Carefully and completely interpret your results.

•   Critically evaluate the implications (based on all your results). Make sure that you use specific and concrete examples in your solution.

•   The only  part  NOT counted  in your word  limit  is your output from the  RStudio’s console. Everything else (e.g., your code, tables, and words in your figures) counts .