Sales of Singapore’s food & beverage services were hit hard when the COVID-19 pandemic rages on. According to the Retail Sales Index and Food & Beverage Services Index released by the Singapore Department of Statistics in March 2021, the business fell by 24.7% in January 2021 on a year-on-year basis. As the industry is on a path of steady recovery, the big driver behind the rise of sales is from accelerated adoption of technology by business owners and customers.
As many businesses made the switch to take-away and food delivery services and customers could remain more digitally engaged, it is critical to improving customer experience to retain existing customers and recruit new ones. Assume you are a business analyst who will advise F&B outlets on possible data mining initiatives to leverage customer data and improve business performance.
Read the two articles below as a backdrop. You need to conduct research to develop your data mining initiative. You may contextualize the data mining initiative in a real or fictitious F&B business.
(a) Briefly describe the F&B business (real or fictitious) including its mission and key products or services. Describe one (1) strategic objective that can be addressed using data mining and discuss at least one (1) data mining goal for the data mining initiative. (Use up to 200 words for part (a))
(b) Recommend at least one (1) data mining technique to use and explain why it/they can be used to address the strategic objective and the data mining goal. Use up to 200 words for your answer.
(c) Develop one (1) dashboard that includes at least three (3) professional graphical charts that provide useful information about the business problem you intend to address. Produce the charts and the dashboard using Tableau. You may use real or fictitious data. Provide a screenshot of each produced chart. Use up to 250 words to explain how the chart is produced and discuss why the charts are recommended.
(d) Use up to 300 words to explain how various stages of CRISP-DM (except for the stage of business understanding) are applied in different parts of this data-mining initiative.
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