Instructions:
S bank plans to implement a new marketing campaign. As consultants working for a leading analytics consulting firm, your team has been hired by the S bank to generate insights and provide recommendations that may help them effectively design and deliver the campaign.
As a business consultant, you should demonstrate the knowledge of and the understanding required for delivering an effective marketing campaign. You may therefore need to do some external research to understand the key features, metrics and strategies in developing and delivering an effective marketing campaign. The source below provides some information on how to structure a marketing campaign https://www.bloominari.com/blog/how-to-structure-complete-marketingcampaign-tips-steps. Feel free to conduct additional research to further your understanding around delivering an effective marketing campaign.
Using SAS Visual Analytics in SAS Viya for Learners you will explore the dataset: VA_SAMPLE_BANKCAMPAIGN (accessible through SAS VA, a data description that is provided by S bank is available as appendix 1 at the end of this document.
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The report should clearly outline the research objectives or/and Page 2 of 8 questions. Your objectives or questions should be well-justified and well articulated to clearly highlight the suitability and value of your analysis to address your client’s needs. Your objectives or questions should guide the use of analytical methods and techniques with appropriate variable selections (e.g., descriptive and/or predictive modelling methods, different techniques, and features selection plus implementation) to generate insights with actionable recommendations. You need to demonstrate critical thinking1 in the process of developing the solution (e.g. analytics models/techniques). Please note that your analysis should draw upon the data variables that are available in the VA_SAMPLE_BANKCAMPAIGN data set.
You should be targeted at the chosen audience (S Bank executives) and it needs to provide a justified explanation of the rationale outlining the research objectives and the research design. This includes how the chosen analytics methods/techniques and variables are a good fit to help address the business objective and answer the identified questions. It should also demonstrate how your analysis is being performed by using visual statistics functions in SAS Viya for Learners.
•Business Issues Identified
Identify and explain key issues that you aim to generate insights from to present to S bank and justify the meaningfulness of the issues to the bank executives.
Assumptions (if any are made) need to be clearly justified and presented, you also need to explain the impact they have on your modelling.
Method – decision tree
•Modelling Techniques Applied – is the part you should do （must provide variety of models from SAS Viya）
1. Explain the modelling technique(s)to be deployed and justify your choice.
2. Specify the variables for each modelling technique and justify your selection.
3. Explain your data preparation if any, such as the investigation of outliers and missing data, and their corresponding treatment where applicable.
4. Demonstrate how analysis is being performed, also evaluate the model’s performance and interpret the modelling outcomes.
5. At the end you need to indicated the Result – (what is the outcome? Which group to target?)
Example：To identify the characteristics of customers who are likely to large quantity of products, we leveraged the decision tree due to the discrete dependent variable that is Count New Product and before using the model the response is divided by three classes: zero, 1-3 and 4-6. and we also use the filter function to excluding our missing value. As the result we can see the amount of last product purchased reflecting the previous purchase behaviour has the most significant on the prediction of their next purchase followed by months last purchase count purchased the past three years and customer value level and for the evaluation we are focused on the model comparison. So we compare the logistic regression model with the decision tree model based on the roc the logistic regression perform better than the decision tree. However, based on the logistic regression p-value of the customer value level is not significant. So Decision tree does not develop a prediction equation but provide a set of rules based on select variables.
Example is Not perfect but can be refer.
All models Must be based on SAS Viya
Here is how to use SAS Viya below
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