analysis. b) Demonstrate knowledge of key SAS Enterprise Mine

La Trobe Business School BUS5PA Predictive Analytics – Semester 2, 2017 Assignment 2 – Cluster Analysis and Predictive Modelling Release Date: 27th August 2017 Due Date: 24th September 2017 – 11.55pm Weight: 40% Format of Submission: A report (electronic form) + electronic submission of project in LMS site. Objective: a) Revise BUS5PA material on predictive modelling and cluster analysis. b) Demonstrate knowledge of key SAS Enterprise Miner nodes used in predictive analytics applications c) Demonstrate knowledge of Cluster analysis using SAS Enterprise Miner. d) Relate theoretical knowledge of predictive models and best practices to application scenarios PART A (60%) – A segmentation based exploration of customers in the churn case study Carry out an exploratory analysis to try and understand who these customers are and whether they have any behavioral patterns and tendencies which could be made use of. Although this analysis will not be directly linked to the earlier predictive analytics exercise, the results may provide useful insights when making decisions based on the predictive analysis results. Add another copy of the churn_telecom data source to the churn case study diagram (you may use new diagram). Go to the meta-data page and change variable roles to input (change the rejected ones to input), other than the ID roles. Add a cluster node and a segment profile node to the diagram. Link the data source to the cluster node and the cluster node to the profile node. Carry out the following clustering and profiling activities and report outcomes. (it is important to note that this is an exploration of the customer data set. Therefore there will be no correct or incorrect result. What is expected is a report on findings and where appropriate provide suggestions on possible business value). Open the variable information page of the cluster node (from the properties list). Since we are planning to conduct a cluster analysis using a limited number of variables, change the ‘use’ column to ‘no’ for all variables. 1. Carry out a demographics based profiling. Change the use of variables age, gender and customer value to ‘default’ (we will take customer value as a demographics variable although this may not be so – insufficient demographics in the data). Run the cluster node and see results. What can you say about the demography based segments? Run the segment profile node and comment on the results. La Trobe Business School 2. Include some customer status based information in the analysis – eg: tenure on network, no. of active services, total profitability of subscription, no of emails, internet/fix line revenue etc (use at least 3 variables). Run the cluster node and the segment profile node and discuss the outcomes. (Do you see any understandable groupings/segments?) 3. Remove the initial variables and carry out a cluster analysis of usage information with variables such as average number of outgoing calls, incoming calls, number of local/international calls etc (use at least 4 variables). 4. Carry out a cross cluster analysis to link identified segments in 3 (usage segments) to segments from previous analysis in 1 (demographics segments) and 2 (customer status segments) above. Comment on your findings highlighting business value. Prepare a report (maximum 4 pages) based on the outcome of the first 3 steps. You may include screen shots of results and point out the variables of significance. The report must have a section discussing the potential value of these results when taking action based on a churn prediction and survival analysis. PART B (40%) (a) Read two documents provided: 1. Seven reasons you need predictive analytics today – Eric Siegel 2. One of the analytics case studies -1 or 2 or 3 Discuss how the seven reasons stated in document 1) is achieved in the particular domain of your chosen case study ie. Relate the domain requirements and functions in 2) to the reasons for PA identified in 1). You may refer to other material, web links etc, but base your answers mainly on the content of these two papers. (3 pages or less) – 25% (b) This section is based on your tutorial 5 case study (customer retention and churn). Read the case study again and study the SAS diagram you created – and understand the flow of the process diagram. Relate the SAS diagram and process flow you created to the SEMMA analytics methodology proposed by SAS. You can use diagrams with brief expplanations. You can refer to links https://en.wikipedia.org/wiki/SEMMA and others and also read the article SAS_SEMMA in your assignment folder). The purpose of this exercise is for you to understand how the case study and SAS Enterprise Miner tasks you carried out relate to the SEMMA (1-1.5 pages) – 15%

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