Question 1 Fashy Online (referred to as Fashy), an online store, has been in operation for...

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Question 1 Fashy Online (referred to as "Fashy"), an online store, has been in operation for the last 15 years and sells a wide range of apparel. All customers will need to register with Fashy before they can start buying items from the shop. Fashy also has an online mailing list for those who wish to receive monthly updates on Fashy's promotion and latest apparel, these subscribers will indicate their personal information such as their age, gender, and place of residence, etc. Like most businesses, one of the primary objectives of Fashy is to increase profit, which is a function of revenue and cost (Profit = Revenue - Cost). However, in the past 12 months, Fashy has experienced a dip in its profit. Fashy is concerned about this and is currently looking at ways to reverse the trend. Assess and recommend how data mining can be applied to help Fashy to increase its profit. (a) For each of the following areas of application, state one (1) business objective: Acquisition of new customers; (i) (ii) Engagement of existing customers; and (iii) Retention of existing customers. (b) (c) (9 marks) With reference to the business objective for each area of application stated in 1(a), discuss how association analysis, clustering, and predictive modelling can be used to meet the business objective (each data mining technique can only be assessed once; for example, if you have assessed association analysis for the acquisition of new customers, you are not to assess association analytics for other two applications). Assess how this is done by providing an example for each of the identified data mining techniques. Do not exceed 180 words for each area of application. (i) Acquisition of new customers Engagement of existing customers (iii) Retention of existing customers (7 marks) (7 marks) (7 marks) For the application using clustering in (b), explain in not more than 300 words how you would implement the data mining project with respect to the phases of the CRISP-DM framework (from data exploration to deployment). (Note: Any words beyond 300 words will not be marked.) (d) For the application using association analysis in (b), discuss one (1) potential limitation of association analysis that may affect the deployment of the association rules, and how this can be overcome. (10 marks) Question 1 Fashy Online (referred to as "Fashy"), an online store, has been in operation for the last 15 years and sells a wide range of apparel. All customers will need to register with Fashy before they can start buying items from the shop. Fashy also has an online mailing list for those who wish to receive monthly updates on Fashy's promotion and latest apparel, these subscribers will indicate their personal information such as their age, gender, and place of residence, etc. Like most businesses, one of the primary objectives of Fashy is to increase profit, which is a function of revenue and cost (Profit = Revenue - Cost). However, in the past 12 months, Fashy has experienced a dip in its profit. Fashy is concerned about this and is currently looking at ways to reverse the trend. Assess and recommend how data mining can be applied to help Fashy to increase its profit. (a) For each of the following areas of application, state one (1) business objective: Acquisition of new customers; (i) (ii) Engagement of existing customers; and (iii) Retention of existing customers. (b) (c) (9 marks) With reference to the business objective for each area of application stated in 1(a), discuss how association analysis, clustering, and predictive modelling can be used to meet the business objective (each data mining technique can only be assessed once; for example, if you have assessed association analysis for the acquisition of new customers, you are not to assess association analytics for other two applications). Assess how this is done by providing an example for each of the identified data mining techniques. Do not exceed 180 words for each area of application. (i) Acquisition of new customers Engagement of existing customers (iii) Retention of existing customers (7 marks) (7 marks) (7 marks) For the application using clustering in (b), explain in not more than 300 words how you would implement the data mining project with respect to the phases of the CRISP-DM framework (from data exploration to deployment). (Note: Any words beyond 300 words will not be marked.) (d) For the application using association analysis in (b), discuss one (1) potential limitation of association analysis that may affect the deployment of the association rules, and how this can be overcome. (10 marks)