Question 1 (50 points):

Imagine you are part of the marketing team for a restaurant called ‘TastyBites.’ TastyBites is eager to expand its customer base by attracting new customers from a variety of platforms,  including Facebook, TikTok, Instagram, Google, and X. Given the competitive nature of the restaurant industry and the desire to make the most of this opportunity, the restaurant’s

management is considering two distinct approaches for this expansion:

1. Voucher-Inclusive Advertising: In this approach, TastyBites will run advertising campaigns on various platforms that promote the restaurant and provide vouchers to new customers who engage with the ads. These vouchers may include discounts, free items, or other

incentives to encourage participation.

2. Standard Advertising: The alternative approach focuses on standard advertising without including vouchers. TastyBites will run ad campaigns on the selected platforms to attract new customers, but these promotions will not offer any vouchers.

A.   Do consumers in the Voucher-Inclusive Advertising approach generate more profit (compared to the Standard Advertising approach)? (Explain) (15 points)

B.   How should platforms be ranked for future Voucher-Inclusive Advertising approach (based on the profit from the first order of the new customers)? (15 points)

To assist in the evaluation (for parts A and B), you have access to campaign results data from the different platforms, containing information about the acquired customers

(‘TastyBites_A.csv’). Using the ‘TastyBites_A.csv’ dataset, run a linear model that helps you address the CEO’s questions in parts A and B.

In addition to answering the questions raised by the CEO, you conducted additional analysis to investigate:

C.   Did the Voucher-Inclusive Advertising approach generate more long-term profit than the Standard Advertising approach? (Explain) (10 points).

To investigate the question posed in part C, you calculated a new variable representing the expected long-term profitability of each customer (variable ‘LTP’). All this data is accessible from the ‘TastyBites_B.csv’ dataset.

Finally, for parts A and B, or C, discuss:

D.   If you had the opportunity to collect more data, what would be the three most  important variables/information you would want to know from the customers? (Explain; 10 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 (including all your coefficients).

•   Critically evaluate the implications (based on all your results) for TastyBites. 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.

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

Question 2 (25 points):

Imagine you are the driving force behind an Electric Vehicle (EV) manufacturing company. You have initiated a survey to gain deeper insights into consumer preferences and their willingness to pay for specific EV features.

In this pursuit, you have access to the 'Taste_EV.csv'dataset, which contains the following variables:

Based on the structure and the information in the dataset (i.e., ‘Taste_EV.csv’ file), your task is as follows:

A.   Find the correlation between ‘T_speed_UK’ and ‘ price’ and interpret the results. Find and interpret the correlation between ‘T_speed_UK’ and ‘speed_US’ (5 points).

B.   Suggest a tree-based model that allows you to understand what EV features affect the consumers’ willingness to pay (i.e., ‘price’). Apply your suggested method (using R) and explain your results (15 points).

C.   Evaluate the model’s performance developed 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.

Question 3 (25 points):

Suppose you are the digital marketing manager for ane-commerce website. You have a

dataset with information about your customers’ website visits, including the number of

pages visited, time spent on the site, the product categories they viewed, and more (see the table below). Your goal is to:

A.   Identify (and target) different customer segments into meaningful clusters in which individuals within a cluster are similar but different from those individuals in other clusters. After clustering, discuss and provide a marketing strategy for each resulting cluster to maximize the effectiveness of your marketing efforts (15 points) .

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

B.   If you were to perform separate clustering for males and others, would you expect the clustering results to be significantly different or similar? Justify your answer based on the dataset (10 points).

Notes that you should consider in your answer:

•    Based on the structure and the information in the dataset, 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.

The submission deadline is precise and uploading of the document must be completed before 20.00 (UK time) on the submission date. Any document submitted even seconds later than 20.00 precisely will be penalised for late submission in line with WBS policy. Please consult your student handbook on my.wbs for more detailed information.

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All the scripts should also have the following paragraph included on the front page:

PLEASE ENSURE YOU KEEP A SECURITY COPY OF YOUR ASSESSMENT

Your Academic Writing and Avoiding Plagiarism Module on my.wbs has lots of useful information on structuring assignments, academic style and demonstrating critical engagement.

Please ensure that any work submitted by you for assessment has been correctly referenced as WBS expects all students to demonstrate the highest standards of academic integrity at all times and treats all cases of poor academic practice and suspected plagiarism very seriously. You can find information on these matters on my.wbs, in your student handbook and on the University's library webpageshere.

The University's Regulation 11 clarifies that '…'cheating' means an attempt to benefit oneself or another by deceit or fraud. This includes reproducing one's own work…' It is important to note that it is not permissible to re-use work which has already been submitted by you for credit either at WBS or at another institution (unless you have been explicitly told that you can do so). This is considered self-plagiarism and could result in significant mark reductions.

Upon submission of assignments, students will be asked to agree to one of the following declarations: