Acedemic Report-Literature ReviewAPA referencing
Important Instructions on Pages 6-8 of pdf
Business Data Analysis MIS784 - Marketing Analytics Assignment 2 – Trimester 2, 2018 Page 1 DEAKIN BUSINESS SCHOOL DEPARTMENT OF INFORMATION SYSTEMS AND BUSINESS ANALYTICS MIS784 Marketing Analytics Assignment Two Background This is an individual assignment, which requires you to analyse a given dataset, interpret, draw conclusions from your analysis, and then convey your conclusions in a written report. Percentage of final grade 30% Due date Monday 17th September 2018 11:59pm The assignment must be submitted by the due date electronically in CloudDeakin. When submitting electronically, you must check that you have submitted the work correctly by following the instructions provided in CloudDeakin. Please note that we will NOT accept any hard copies or part of the assignment submitted after the deadline or via Email. Extensions of time are not permitted. 5% will be deducted from the 30 marks allocated to this assessment task for each day that the assessment is late, up to five days. Penalties include weekend days. Where work is submitted more than five days after the due date, the task will not be marked and the student will receive 0% for the task. The assignment uses the files TESCO.training.xlsx and TESCO.test.xlsx which can be downloaded from CloudDeakin. MIS784 - Marketing Analytics Assignment 2 – Trimester 2, 2018 Page 2 Assurance of Learning This assignment assesses following Graduate Learning Outcomes and related Unit Learning Outcomes: Graduate Learning Outcome (GLO) Unit Learning Outcome (ULO) 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 define) problems. ULO1: To apply analytics models to a wide range of marketing activities ULO2: Use computer software to analyse consumers’ data and understand the strength and limitations of each software ULO3: Analyse and interpret the output of a range of Customer analytics models in order to improve the decision making process ULO4: Demonstrate comprehensive understanding of Customer analytics models Feedback Prior to submission Students are able to seek assistance from the teaching staff to ascertain whether the assignment conforms to submission guidelines. Please post your questions on CloudDeakin’s discussion forum for Assignment 2. After submission Your assignment feedback will be returned in a rubric via CloudDeakin with an overall mark together with comments. In order to understand any areas of improvement, students are expected to refer, and compare, their answers to the suggested solutions. MIS784 - Marketing Analytics Assignment 2 – Trimester 2, 2018 Page 3 Case Study Tesco PLC is a British multinational supermarket chain headquartered in Welwyn Garden City, Hertfordshire, England, United Kingdom. It is the third largest retailer in the world measured by profits and second-largest retailer in the world measured by revenues. It has stores in 12 countries across Asia and Europe and is the grocery market leader in the UK, Ireland, Hungary, Malaysia, and Thailand. The company currently offers products in nine different categories including Apparel, Bakery, Deli, Dairy, Fresh Produce, General Merchandise, Grocery, Liquor, and Meat. Tesco launched its customer loyalty scheme, the Tesco Clubcard, in 1995, with two levels (Silver and Gold). It has been cited as a pivotal development in Tesco's progress towards becoming the UK's largest supermarket chain and one that fundamentally changed the country's supermarket business. Cardholders can collect one Clubcard point for every £1 they spend in a Tesco store, or at Tesco.com. This enables the company to collect data on purchase behaviour of customers and utilize it to design customized offers and conduct targeted retention campaigns. MIS784 - Marketing Analytics Assignment 2 – Trimester 2, 2018 Page 4 Data The data of this assessment task relates to a random sample of 30,000 customers from Tesco Clubcard (20,000 training set & 10,000 test set) in a period from 1 January 2015 to 31 December 2015. The 18 variables in the data table are described below: Variable Name Description ID Unique ID of customers Purchase Number of purchases during the observation period1 T.last The time gap between customer’s first purchase and last purchase during the observation period T.active The time gap between customer’s first purchase and last day of the observation period Loyalty A binary variable to show membership level: (0) Silver (1) Gold Service Failure Number of service failures during the observation period Total Profit Total profit generate by the customer during the observation period AP.spent Total spending on Apparel category during the observation period BH.spent Total spending on Bakery category during the observation period DL.spent Total spending on Deli category during the observation period DY.spent Total spending on Dairy category during the observation period FV.spent Total spending on Fresh Produce category during the observation period GM.spent Total spending on General Merchandise category during the observation period GR.spent Total spending on Grocery category during the observation period LQ.spent Total spending on Liquor category during the observation period MT.spent Total spending on Meat category during the observation period Socio.Economic Socio Economic status of the customer on a scale from 1(lowest) to 10 (highest) Churn A binary variable to show the churn status of the customer in the prediction period2 (0) non-churner (1) churner Analysis Tasks 1- Construct a model to predict customer churn using binary classification trees (C&R Tree) and evaluate the performance of the constructed model on the holdout sample provided (use metrics related to confusion matrix). 2- Evaluate the performance of the constructed model against the RFM method (use lift chart- i.e. concentration to make the comparison). Report Tasks 1- Introduction and problem definition 2- Literature review: Use only academic journal articles. APA style should be used for referencing. 1 Observation Period: From 1 January 2015 to 31 August 2015 2 Prediction Period: From 1 September 2015 to 31 December 2015 MIS784 - Marketing Analytics Assignment 2 – Trimester 2, 2018 Page 5 3- Methodology and empirical study: this should include a discussion of your analytical techniques, your model evaluation metrics, your working data, and your model building process. 4- Results: evaluate your analysis results to explain how the constructed models perform and also how they are positioned against a random model (random guessing). 5- Conclusion and Recommendations: This should consider the implications of your results and how they may contribute to customer retention and reduce marketing expenditure. MIS784 - Marketing Analytics Assignment 2 – Trimester 2, 2018 Page 6 Submission The assignment consists of two parts: Analysis and Report. You are required to submit both your written report (approx. 3000 words) and analysis files (conducted in Excel and IBM SPSS Modeler). Analysis Students are expected to complete the first analysis task in IBM SPSS Modeler and the second analysis task in Excel. Both files are expected to be a part of your submission. Before submitting your analysis, make sure it is logically organised and any incorrect or unnecessary output has been removed. In your Excel file all calculated figures are expected to be tied to appropriate Excel functions. Note: Give your analysis files an appropriate name such as MIS784_A2_studentID.xlsx & MIS784_A2_studentID.str. Report The report should be written based on your analysis output and be formatted as an academic report (journal/conference paper). Note: Name the report with an appropriate file name such as MIS784_A2_studentID.docx. MIS784 - Marketing Analytics Assignment 2 – Trimester 2, 2018 Page 7 Criteria Unsatisfactory Satisfactory Good Very Good Excellent Analysis: Analytical results (Marks: 5) GLO1 and GLO3 1.2 No analysis is presented or Irrelevant/inappropriate techniques have been used to analyse the data with many errors in the analysis 0-2.4 2.7 Consistently, independently and skilfully uses appropriate data analysis techniques, demonstrating some expertise and specialised skills 2.5-2.9 3.2 Consistently, independently and skilfully uses appropriate data analysis techniques, demonstrating good expertise and specialised skills 3-3.4 3.7 Uses appropriate data analysis techniques independently, efficiently and effectively, and demonstrating a consistently high levels of expertise and specialised skills. 3.5-3.9 5 Uses most suitable data analysis techniques independently, efficiently and effectively, and demonstrating a consistently high levels of expertise and specialised skills. 4-5 Report: Discussion of the background or context of the inquiry (Marks: 4) GLO1 and GLO5 1 Provides no relevant research question/s and/or little or no discussion of the learning and teaching problem or issue which the inquiry is intended to address 0-1.9 2.2 Provides research question/s linked to the inquiry as well as some discussion of the learning and teaching problem or issue which the inquiry is intended to address 2-2.3 2.6 Provides clear and actionable research question/s well linked to the inquiry as well as a good discussion of the learning and teaching problem or issue which the inquiry is intended to address 2.4-2.7 3 Provides clear and actionable research question/s very well linked to the inquiry as well as