Reference no: EM132998264
MIS771 Descriptive Analytics and Visualisation
Assignment Two
You need to analyse the given dataset and then interpret and draw conclusions from your analysis. You then need to convey your findings in a written report to an expert in Business Analytics.
Learning Outcome 1: Apply quantitative reasoning skills to solve complex problems.
Learning Outcome 2: Plan, monitor, and evaluate own learning as a data analyst.
Learning Outcome 3: Deduce clear and unambiguous solutions in a form that they useful for decision making and research purposes and for communication to the wider public.
The Case Study
The B-Craft is a South Australian micro-brewery company with fifteen years of experience in brewing ale. Although its operations are limited to Adelaide and regional South Australia, the company has been financially successful.
B-Craft beer is sold directly to their customers (pubs, bars, restaurants and bottleshops) or indirectly through an external distribution network.
Despite their successful operations and solid financial turnovers in the last two years, B-Craft is forecasting a shift in business climate within the next five years. Now more than ever, B-Craft management feels the need to ensure a strong relationship with its customer base. In addition, they are planning to put in place a formal procedure to forecast their beer production. The formal forecasting would help B-Craft with production planning.
B-Craft approached BEAUTIFUL-DATA (a market research company) and asked them to conduct a large-scale survey of their customers to better understand the characteristics of B-Craft's customers and their repurchase intention.
Data
Subsequently, BEAUTIFUL-DATA contacted B-Craft's customers and encouraged them to participate in an online survey. B-Craft's customers rated the company on nine attributes using a 1 - 10 scale in the survey. They also indicated the likelihood of recommending B-Craft to others. The survey data was supplemented by information such loyalty duration, type, region, and distribution channel in B- Craft's database.
Your Role at BEAUTIFUL-DATA
You are a modeller at BEAUTIFUL-DATA. The team leader (Todd Nash, with a PhD in Data Science and a Master Degree in Digital Marketing) has asked you to lead the modelling component for the B- Craft project. You need to review and complete the modelling activities as per the document below. The minutes of the team meeting is below.
B-Craft Project - Analytics Details
Specifying and allocating Data Analytics Tasks
• Model Quantity Ordered.
• Model the likelihood of recommending B-Craft to others.
• Forecast the Pale Ale production volumes for the upcoming four quarters.
• Produce a technical report.
What:
1. The ability to meet project deadlines is a highly sought-after skill at ANALYTICS7. Report how you plan to deliver the outputs on or before the set date of this project.
2. Build a multiple regression model to estimate the order quantity.
3. Todd has performed 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 with a more favourable perception of a brand. Model the interaction between these variables to test Todd's assumption and comment whether there is sufficient evidence to conclude that the interaction term is statistically significant in the model.
4. Finalise Todd's logistic regression model to predict the likelihood of recommending B-Craft to others:
4.1. Todd has completed the initial analysis for this task. He has narrowed down the key predictors to Distribution Channel, Quality, Brand Image and Shipping Speed. Your task is to continue his work and develop a predictive model to ascertain the likelihood of recommending B-Craft to others.
4.2. Todd is specifically interested in understanding the probability of customers who meet the following criteria to recommend B-Craft to others.
Those who,
a) Feel neutral (i.e. score of 5 on the relevant scale) towards B-Craft's speed of delivery;
b) 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);
c) And across two market segments: those who purchase directly; and those who purchase through a sales representative.
Todd believes that the quality of the product and brand image define B- Craft's success in being recommended. Therefore, it is essential for B-Craft 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.
5. Develop a time-series model to forecast B-Craft production volumes of pale ale for the next four fiscal quarters.
6. Produce a written technical report detailing all aspects of your analysis.
Your report should be as detailed as possible and should describe all critical outputs of your analysis. The results of the analysis should drive the recommendations to B-Craft management.
Task 1. - Assignment planning and execution
The purpose of this practical task is to help you keep track of your progress with the project and complete it on time. To report how you plan your project and turn the plan into action, you must complete the tables provided in dot points as clearly as possible. Remember, effective planning, execution, and completing given tasks on time are essential professional development skills.
Note: Dot point writing requires you to use 'point form', not complete sentences.
Task 2. - Model building
You should follow an appropriate model building process. You should include all steps of the model building activities (especially all relevant pre and post model diagnostics) in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model (i.e., 2.2.a., 2.2.b., 2.2.c. etc.). If you make any reasonable/realistic assumption about the parameters, please note them next to the analysis.
Your technical report should clearly explain why the model might have undergone several iterations. Also, you must provide a detailed interpretation of ALL elements of the final model/regression output.
Task 3. - Interaction effect
To accomplish this task, you need to develop a new regression model using ONLY the factors discussed in the team meeting (Item 3). If you make any reasonable/realistic assumptions about the parameters, please note them next to the analysis.
Your technical report should clearly explain the role of each variable included in the model and use visualisation to illustrate the interaction effect. Make sure you interpret all relevant outputs in detail and provide managerial recommendations based on the results of your analysis.
Task 4.1 - Model building
You should follow an appropriate model building process. You should include all steps of the model building activities (especially all relevant pre and post model diagnostics) in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model (i.e., 4.1, 4.1.a). If you make any reasonable/realistic assumptions about the parameters, please note them next to the analysis.
You are required to discuss all details of your predictive model/logistics regression output.
Task 4.2. - Visualising and interpreting predicted probabilities
Your technical report must include the predicted probability visualisation and the practical recommendations. These recommendations should broadly 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 B- Craft by two customer segments (i.e. those purchasing directly, and those purchasing through sales representative)."
Task 5. - Forecasting Production
Past quarterly beer production numbers are in the Excel file. Your job is to develop a suitable model to forecast Quarterly production volumes 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...etc.)
Task 6. - 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.
Remember, your audience are experts in analytics and expect a very high standard of work from your report. High standards mean quality content (demonstrated attention to details) as well as an aesthetically appealing report.
Attachment:- Descriptive Analytics and Visualisation.rar