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MIS771 Descriptive Analytics And Visualisation

tag 0 Download 12 Pages / 2,957 Words tag 21-10-2020
  • Course Code: MIS771
  • University: Deakin University
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  • Country: Australia


AusPaper, a subsidery of Pinnon Paper Industries, is an Australian company with a long history in local manufacturing of paper products. In 2013 only, AusPaper produced 619,000 tonnes of paper products, and sold more than 690,000 tonnes of products to local and overseas markets. They export their products to over 75 countries in Asia, USA, Europe, Middle East, the Indian subcontinent, Latin America and Africa. AusPaper sells paper products to two market segments: the newspaper industy (e.g. Australian Finacial Review, Herald Sun etc.) and the magazine industry (e.g. Mens’ Style Magazine, Homes and Gardens etc.). Also these products are sold to these market segments either directly to the customer or indirectly through a broker.
Despite their successful operations and solid financial turn-overs over the last two decade, AusPaper is forecasting a major shift in business climate within the next seven years. This is a result of a change in end-consumers preferences (i.e., readers’ preference to access newspapers, magazines online or via ereaders, and social media).
Now more than ever, AusPaper management feels the need to ensure a strong customer base and ideally a strong strategic alliance with their clients in newspaper and magazine industry. In addition, they are planning to put in place a formal procedure to be able to project future financial turnovers using historical data. Consequently, AusPaper has approached ANALYTICS7 (a Market Research Company) and asked them to conduct a large-scale survey of their clients to better understand the characteristics of AusPaper customers, their perceptions of the company, and the likelihood of customers building long-term strategic alliance with AusPaper.
Data Collection Process (Conducted by ANALYTICS7) To address AusPaper concerns, ANALYTICS7 has contacted purchasing managers of firms buying from Auspaper and encouraged them to participate in an online survey. The collected data are then supplemented by other information compiled and stored in AusPaper’s data warehouse and accessible through its decision support system. Primary Database (accessible via AusPaper.xlsx file) The primary databse consists of 200 observations on 18 separate variables. Two types of information are accessible in this database. The first type of information is perceptions of AusPaper’s performance on 13 attributes. Purchasing managers of firms buying from AusPaper were asked to rate the company on each of these 13 attributes using a 0 – 10 scale, with 10 being “Excellent” and 0 is being “Poor”.
The second type of information relates to purchase outcomes and business relationships (e.g., satisfaction with AusPaper and whether the purchasing firm would consider strategic alliance / partnership with AusPaper). A third type of information is available from AusPaper’s data warehouse and includes information such as size of customer and length of purchase relationship, as well as quarterly turnover of AusPaper operations. A complete listing of variables, their definitions, and an explanation of their coding are provided in AusPaper.xlsx file.
MIS771 - Descriptive Statistics and Visualisation Trimester 1, 2018 Page 4 of 11 Your Role as an ANALYTICS7 Data Analyst Intern You are a master of business analytics student doing an interenship at ANALYTICS7. The research team manager (Hugo Barra, with PhD in Data Science and a Master Degree in Digital Marketing) has asked you to lead the data analysis process for AusPaper project and directly report the resultsto him. You and Hugo just finished a meeting wherein he briefed you on key purposes of AusPaper research project.
Hugo explained that an important prerequisite in building a strategic alliance in a B2B environment is “customer satisfaction” with a firm’s operations. Therefore, the first goal is to identify key factors that predict customer satisfaction with past purchases from AusPaper. He is also interested in gaining deeper insights into factors that predict the “likelihood of AusPaper customers building strategic alliance” with the firm. The final analytics goal is constructing a forecasting model to predict AusPaper’s turnover in the upcoming three quarters of 2017. From these understandings, Hugo and consequently AusPaper will be in a good position to develop plans for the next financial year. In addition to briefing you about key research questions, Hugo also allocated relevant research tasks and explained his expectations from your analysis. 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.
Task 1 – Summarising Dependent Variables The purpose of this task is to analyse and explore key features of these two variables individually. At very least, you should thoroughly investigate relevant summary measures of for these two variables. Proper visualisations should be used to illustrate key features of these two variables. Your technical report should describe ALL key aspects of each variable.
Task 2.1. – Identifying relevant factors for predicting customer satisfaction 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 analysis. Your technical report should describe why some variables were selected while others were dropped from subsequent analyses.
Task 2.2. – Model Building (Predicting Customer Satisfaction) You should follow model building process outlined in Module 2 – Topic 2. All steps of model building process should be included in your analysis. You can have as many Excel worksheets (tabs) as you require to clearly demonstrate different iterations of your predictive model (i.e., 2.2.a., 2.2.b., 2.2.c. etc.). Note that your final model should only include those variables that have predictive value. Your technical reportshould clearly explain why the model may have undergone several iterations. Also, you must provide detailed interpretation of ALL elements of the final model. MIS771 - Descriptive Statistics and Visualisation Trimester 1, 2018 Page 8 of 11
Task 2.3. – Interaction Effect To accomplish this task you need to develop a regression model using ONLY factors discussed in the ‘minutes of 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 Building Strategic Alliance/Partnership) You should start building the predictive model by including ONLY the variables listed in the ‘minutes of the meeting – Task 3.1.’. You are required to demonstrate all iterations of your predictive model. Note that your final model should only include those variables that have predictive value. Your technical reportshould clearly explain why the model may have undergone several iterations. A detailed interpretation of ALL elements of the final model must be provided.
Task 3.2. – Visualising and Interpreting Predicted Probabilities Your technical report must include the predicted probability visualisation and be supplemented by practical recommendations to Hugo Barra (or AusPaper). These recommendations should answer the following question: “How change product quality (scores from 0 to 10) and price flexibility (scores of 0, 5, and 10) may affect the predicted probability of building strategic alliance with AusPaper for customers who have neutral feeling (fixed score of 5) towards personnel image and product line?” MIS771 - Descriptive Statistics and Visualisation Trimester 1, 2018 Page 9 of 11
Task 4. – Forecasting Turnover AusPaper’s quarterly turnover from quarter one 2008 to quarter one 2017 are given in the AusPaper_Turnover worksheet. Your job is to develop a proper forecasting model to predict turnover for the next three quarter (i.e. Q2 to Q4 2017). In your technical report, you must explain the reason for selecting the forecasting method to predict future turnover. The report also must include a detailed interpretation of the final model. (e.g. a practical interpretation of the time-series model, choices about smoothing technique etc.). Reference: Module 2 – Topic 4
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. Remember, your audience (i.e., Hugo) is an expert in Analytics and he expects nothing but perfection from your report. Perfection means quality content (demonstrated attention to details) as well as an aesthetically appealing report. Note that you can use as many technical terms as you require. Your report should also include an introduction as well as a conclusion. Introduction begins by highlighting the main purpose(s) of analysis and concludes by explaining the structure of the report (i.e., subsequent sections). Conclusion should highlight the key findings of analyses and explain the main limitations. MIS771 - Descriptive Statistics and Visualisation Trimester 1, 2018 Page 10 of 11 Submission Guide The assignment consists of two parts: Analysis and Technical Report. You are required to submit both your written report (Word.docx document only) and analysis (Excel.xlsx file only).
This assignment is equivalent of 2,500 words. Analysis (excel.xlsx) The analysis should be submitted in the appropriate worksheets in the Excel file. Each step in the model buildings should be included in a separate tab (e.g. 2.2.a., 2.2.b., …; and 3.2.a. 3.2.b., …). If you need more worksheets, then add them. Before submitting your analysis make sure it is logically organised and any incorrect or unnecessary output has been removed. Marks will be penalised for poor presentation or disorganised/incorrect results. Your worksheets should follow the order by which tasks are allocated in the minutes of meeting document. Note: Give the Excel file an appropriate name such as MIS771_A2_studentID.xlsx. Use a short file name while you are doing the analysis – once you complete your analysis rename the file to the format mentioned above. Technical Report (word.docx) Your technical report consists of four sections: Introduction, Main Body, Conclusion, and Appendices.
The report should be no longer than 2,500 words. It could be shorter as long as all the aspects of the assignment tasks are addressed in detail. Use proper headings (i.e., 1., 2.1., 2.2., …) and titles in the main body of the report. Use sub-headings where necessary. Relevant tables, charts, or graphs MUST be included in the report as Appendices(not included in the word count). Make sure these outputs are visually appealing; have consistent formatting style and proper titles (title, axes titles etc.); and are numbered correctly. Where necessary, refer to these outputs in the main body of the report.
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