Business Case The CIO of Valeur, a large national supermarket/retail store chain has recently introduced a loyalty card program and keen to gain a more comprehensive understanding of the organisation’s clientele through traditional transactional data as well as the newer and effective demographics analysis techniques. A close friend, the CEO of Vigour has highly recommended you as an accomplished business analyst/consultant for data warehousing and OLAP projects based on the outstanding work completed on the dimensional modelling and design phase of a data warehouse at Vigour. The CIO of Valeur, impressed by the commendation, has approached you with his interests in customer analytics. The customer relationship management (CRM) team working under his supervision have already put together a summarised data warehouse containing some transaction data and customer demographics. Although the data is accurate and complete, the CIO believes the structure is not. He feels there’s more information that can be captured by the warehouse. He’s very impressed with the power of demographics as it is directly applicable to his client base. He has two main business problems in mind. Firstly, improving the current dimensional model/ warehouse to capture all relevant information that applies to customers, such as demographics. Secondly, using current data in the warehouse to better understand the customer base by identifying customer behaviours and thereby potential sales and marketing opportunities. You have agreed to take on this responsibility and deliver a complete report documenting your recommendations to improve the dimensional model and your findings from the current warehouse. What you are required to do 1. Suggest improvements to the dimensional model Study the given dimensional model to determine what’s lacking in response to the business need. Identify potential improvements to the dimensional model. This can be in the form of new attributes, new dimensions, new measures or the use of dimension design techniques. Present your recommendations with supporting evidence and the new dimensional model. 3. Create cubes and OLAP queries The data warehouse (ValeurDW) is accessible from the Azure server (BUS5WB-SQLSVR02). Use SSDT to create a multidimensional analysis project. Design and run OLAP queries to determine customer insights (demographics, buying behaviours, sentiment and the combination of these). You may decide the number of queries to run and you are free to use any query interface – MS Excel, SQL reporting, MDX or even the Cube browser. Master of Business Analytics BUS5WB La Trobe Business School 2 4. Recommendations Make recommendations based on your findings, taking into account the natural progression of a typical customer from low-value to high-value and the potential opportunities exposed in the consumer dynamics. Recommendations can be simple as cross-sell, up-sell and down-sell or a notch-up sophisticated. Deliverables A report on the above activities undertaken for Valeur. The report should be compiled as a series of topics of the abovementioned ‘to do’ list. • The report should be compiled in Microsoft Word only, font size 11. • It should not exceed 12 pages. Diagrams, tables and any other visualisation has to be in the main body of the report. • It should contain a reference to your SSAS project (project name) on the server and any other files. • Make realistic assumptions on any information (schema or business requirements) that may be missing in the above description. Mention all assumptions in the report. Dimensional Model DimTransactionDate TransactionDateKey TransactionDate Day Month Quarter Year DimCustomer CustomerKey CustomerID Age Gender Education SuburbProfile CreditRanking DimProductCategory ProductCategoryKey ProductCategoryID CategoryName DimFeedback FeedbackKey FeedbackID FeedbackService FeedbackPrice FeedbackVariety FeedbackValue FactTransactionSummary CustomerKey ProductCategoryKey FeedbackKey TransactionDateKey TotalQuantity TotalSale Master of Business Analytics BUS5WB La Trobe Business School 3 Rubric Criteria Pass Credit Distinction High Distinction Improve the dimensional model 5 marks A minimal attempt at improving the dimensional model. A basic attempt at improving the dimensional model. A complete attempt at improving the dimensional model. A complete attempt at improving the dimensional model with precision focus on integrating transaction data, and all aspects of demographics. Create cubes and run OLAP queries 10 marks A minimal multidimensio nal analysis project with minimal reports, findings, and outcomes. A basic multidimensional analysis project with a decent number of reports, findings, and outcomes. A complete multidimensional analysis project with a good number of reports, findings, and outcomes. A complete multidimensional analysis project with an optimal number of reports, findings, and outcomes providing a broad coverage. Recommendations for new markets 15 marks A rudimentary effort, limited to a minimal number. A decent effort, with some actionable recommendations. A good effort, with many actionable recommendations. A comprehensive effort, covering all actionable recommendations
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