Reference no: EM132341066

Assignment

Background

This is an individual assignment, which requires you to analyse a given data set, interpret and draw conclusions from your analysis, and then convey your conclusions in a written technical report to an expert in Business Analytics.

Graduate Learning Outcome (GLO)

GLO1: Discipline-specific knowledge and capabilities - appropriate to the level of study related to a discipline or profession.

GLO3: Digital Literacy - Using technologies to find, use and disseminate information

GLO5: Problem Solving - creating solutions to authentic (real-world and ill-defined) problems.

Unit Learning Outcome (ULO)

ULO 1: Apply quantitative reasoning skills to solve complex problems.

ULO 2: Use contemporary data analysis and visualisation tools and recognise the limitation of such tools.

Case Study (Background to Mad Dog Craft Beer)

Your Role as a BEAUTIFUL-DATA Data Analyst Intern

You are a graduate student doing an internship at BEAUTIFUL-DATA. The research team manager (Todd Nash, with a PhD in Data Science and a Master Degree in Digital Marketing) has asked you to lead the data analysis process for the Mad Dog Craft Beer project and directly report the results to him. You and Todd just finished a meeting wherein he briefed you on the primary purpose of the project.

Todd explained that a model should be built to estimate Order Quantity. Therefore, the first goal is to identify critical factors that influence the quantity ordered. Todd is also interested in gaining more profound insights into factors that predict the likelihood of current clients to recommend Mad Dog Craft Beer's products to others. The final goal is to construct a model which forecast Mad Dog Craft Beer's Pale Ale production in the upcoming four quarters. From these insights, Mad Dog Craft Beer will be in an excellent position to develop plans for the next financial year.

Todd also allocated relevant research tasks and explained his expectations from your analysis in the meeting. Minutes of this meeting are available on the next page.

Now, your job is to review and complete the allocated tasks as per this document.

Meeting Purpose:
Specifying and Allocating Data Analytics Tasks
Discussion items: • Variable(s) description.
• Modelling Quantity Ordered.
• Modelling the likelihood of recommending Mad Dog Craft Beer to others.
• Forecasting Pale Ale production in the upcoming four quarters.
• Producing a technical report.
Detailed Action Items Who: Graduate Intern What:
1. Provide an overall summary of the following two variables:
Order_Qty
Recommend

2. Identifying potential factors that may influence Order_ Qty:
An appropriate statistical technique could be used here to identify a list of possible factors.
Build a model (through a model building process) to estimate the Order_ Qty.
Todd has done a separate regression analysis and found that the perception of beer quality is a significant predictor of the quantity ordered. In line with his findings, prior research shows that the strength of this relationship may vary according to brand image. That is, customers tend to associate the brand image with product quality. Therefore, Todd believes that the relationship between quality and quantity ordered should be stronger for those who have a more favourable perception of a brand. Your task here is to test Todd's assumption by modelling the interaction between the predictors mentioned above and the target variable. Comment whether there is sufficient evidence that the interaction term makes a significant contribution to the model.

3. Finalise the model to predict the likelihood of recommending Mad Dog Craft Beer to others:
Todd has already done an initial analysis for this task. Based on his analysis, Todd has narrowed down the key predictors to Distribution Channel, Quality, Brand Image and Shipping Speed. Your responsibility now is to continue his work and develop a predictive model to ascertain the likelihood of recommending Mad Dog Craft Beer to others.
Todd would like to gain a deeper understanding of the likelihood of recommending the Mad Dog Craft Beer product to other customers. He is specifically interested in understanding the probability of clients who meet the following criteria to recommend Mad Dog Craft Beer to others.

Those who,
• Feel neutral (i.e. score of 5 on the relevant scale) towards Mad Dog Craft Beer's speed of delivery;
• With varying levels of perception towards product quality (i.e., scores from 1 to 10) and brand image (scores of 1=negative, 5=neutral, and 10=positive);
• And across two customer segments: 1) those who purchase directly; and 2) those who purchase through a sales representative.

Todd believes that the quality of the product and brand image define Mad Dog Craft Beer's success in being recommended. Therefore, it is essential for Mad Dog Craft Beer to know whether effort and money should be put in improving perceptions of product quality and brand image to increase the probability of being recommended. Accordingly, your job is to visualise the predicted probability of being recommended to others by customers with the attributes described above.

4. Develop a time-series model to forecast Mad Dog Craft Beer production of pale ale in the next four fiscal quarters.

5. Produce a written report detailing ALL aspects of your analysis. Your report should be as detailed as possible and should describe ALL key outputs of your analysis. Make sure to provide recommendations to Mad Dog Craft Beer's management that will guide them to improve their customer relationships management. Your recommendations / insights should be driven by the results of your analyses.

To accomplish allocated tasks, you need to examine and analyse the dataset (mdcb.xlsx) thoroughly. Below are some guidelines to follow:

Task 1 - Summarising Dependent Variables

The purpose of this task is to analyse and explore the key features of these two variables individually. At the very least, you should thoroughly investigate relevant summary measures of these two variables. Proper visualisations should be used to illustrate key features.

Your technical report should describe ALL key aspects of each variable.

Task 2.1. - Identifying relevant factors that may influence quantity ordered

Analyse the relevant dependent variable against other variables included in the dataset. Your job is to decide which variables to include here. Use an appropriate technique to identify important relationships.

The outcome of this task is a list of variables that should be included in the subsequent regression analysis.

Your technical report should describe why some variables were selected while others were dropped from subsequent analyses.

Task 2.2. - Model building (estimating quantity ordered)

You should follow a model building process. All steps of the model building process should be included in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your predictive model (i.e., 2.2.a., 2.2.b., 2.2.c. etc.).

Your technical report should clearly explain why the model may have undergone several iterations. Also, you must provide a detailed interpretation of ALL elements of the final model.

Task 2.3. - Interaction effect

To accomplish this task, you need to develop a regression model using ONLY the factors discussed in the meeting (Task 2.3). In other words, this section of analysis is separate from the regression model constructed in Task 2.2.

Your technical report should clearly explain the role of each variable included in the model. A proper visualisation technique should be used. Make sure you interpret all relevant outputs in detail and provide managerial recommendations based on the results of your analysis.

Task 3.1. - Model building (likelihood of recommending Mad Dog Craft Beer)

You should start building the predictive model by including ONLY the variables listed in the ‘minutes of the meeting - Task 3.1.'. You must make reasonable/realistic/practical assumptions about the parameters mentioned in Task 3.1.

You are required to discuss all details of your predictive model.

Task 3.2 and Task 3.3. - Calculating predicted probabilities, Visualising and interpreting predicted probabilities
Your technical report must include the predicted probability visualisation and be supplemented by practical recommendations to Mad Dog Craft Beer's Management. These recommendations should answer the following question:

"How a change in perceptions of quality (scores from 1 to 10) and brand image (scores of 1, 5, and 10) may affect the predicted probability of recommending Mad Dog Craft Beer by two customer segments (i.e. those purchasing directly, and those purchasing through sales representative)."

Task 4. - Forecasting production

Mad Dog Craft Beer's quarterly beer production from the third quarter of 2008 until the first quarter of 2019 are given in the Product worksheet. Your job is to develop a proper forecasting model to predict turnover for the next four quarters.

In your technical report, you must explain the reason for selecting the forecasting method to forecast future beer production. The report also must include a detailed interpretation of the final model (e.g. a practical interpretation of the time-series model, errors etc...).

Task 5. - Technical report

Your technical report must be as comprehensive as possible. ALL aspects of your analysis and final outputs must be described/interpreted in detail.

Note: The use of technical terms is acceptable in this assignment.
Your report should include an introduction as well as a conclusion. The introduction begins by highlighting the main purpose(s) of analysis and concludes by explaining the structure of the report (i.e., subsequent sections). The conclusion should highlight the key findings and explain the main limitations.

Attachment:- Descriptive Analytics and Visualisation.rar