sas: analytics, artificial intelligence and data management | sas - … · 2018. 7. 4. · prepare...
TRANSCRIPT
![Page 1: SAS: Analytics, Artificial Intelligence and Data Management | SAS - … · 2018. 7. 4. · Prepare an analytic base table used for reporting and analytics • Examine, profile, and](https://reader035.vdocuments.mx/reader035/viewer/2022071010/5fc7c50c92367c76a802b1ff/html5/thumbnails/1.jpg)
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SAS Viya WorkshopAccelerating Innovation with the SAS Platform
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AgendaTime Activity
9:00am Introduction to SASSAS Platform for the Citizen Data Scientist
9:30am Descriptive Analytics
10:30am Break
11:00am Diagnostic Analytics
12:30pm Lunch
2:00pm Predictive AnalyticsPrescriptive Analytics
3:00pm Q&A Session
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Introduction to SAS
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Analysts rank SAS as a leader in:
In 2017, analysts ranked SAS a leader or leader equivalent in more than 30 reports
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VALUEANALYTICSDATA
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Key Focus Areas
Analytics for EveryoneAnalytics EverywhereArtificial Intelligence
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SAS Platform for the Citizen Data Scientist
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The SAS Platform
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Our digital transformationto power the analytics economy
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Visual Exploration
Programming Interface
Visual Drag-and-DropInterface
Data Preparation
Machine Learning / Artificial Intelligence
Interactive Reporting
Predictive Analytics
Share, Collaborate,
DeployGovernance
Extending the Paradigm
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Scenario: TelecommunicationsCross-sell and Up-sell Mobile Plans to Increase Revenue
Identify target customers with a high likelihood to respond to the cross-selling and up-selling of mobile plans
• Ensure completeness and cleanliness of data
• Generate meaningful insights from data
• Identify best offerings to be provided to target customers
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Scenario: TelecommunicationsCross-sell and Up-sell Mobile Plans to Increase Revenue
Customer Demographic Data
Customer Usage Data
Roll-up by customer IDs
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Types of Analytics
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Scenario: TelecommunicationsCross-sell and Up-sell Mobile Plans to Increase Revenue
1. How have customers reacted to our mobile plans?
2. What are the factors contributing to their response?
3. Craft a new mobile plan based on customer segmentation
4. Recommend target customers based on their propensity to upgrade
1 2 3 4
Descriptive Diagnostic Predictive Prescriptive
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Descriptive Analytics
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Data Preparation
Prepare an analytic base table used for reporting and analytics
• Examine, profile, and cleanse customer demographic data
• Merge customer demographic data with customer usage data
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1
2
3
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1
2
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Scenario: TelecommunicationsHow have customers reacted to our mobile plans?
Prepare a dynamic, interactive report to generate meaningful insights
• Identify the mobile plan with a high upgrade rate that would be ideal to market for cross-selling and up-selling purposes
• Identify the acquisition channel with the highest upgrade rate
• Identify significant usage measures that contribute to upgrades
1
Descriptive
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41
Descriptive Analytics
Methods:
• Prepare an analytic base table used for reporting and analytics
• Prepare a dynamic, interactive report to generate meaningful insights
Hands-On Exercise
60MINUTES
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Diagnostic Analytics
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Scenario: TelecommunicationsWhat are the factors contributing to their response?
Build simple statistical models to identify factors contributing to patterns, relationships, and trends
• Identify predictors significantly related to mobile plan upgrades
• Identify customers with a high likelihood to upgrade
• Identify possible outliers of the analysis
• Understand how different variables contribute differently to a model
2
Diagnostic
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49
Diagnostic Analytics
Methods:
• Build simple statistical models to identify factors contributing to patterns, relationships, and trends
Hands-On Exercise
90MINUTES
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Types of Analytics
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Predictive Analytics
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Scenario: TelecommunicationsCraft a new mobile plan based on customer segmentation
Use K-means clustering to segment customers based on the following behaviors:
• 6-month billed data usage
• 6-month average roaming minutes
• 6-month average minutes on network
• 3-month average overaged charges
• 3-month average premium data charges
3
Predictive
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Clustering…Conceptually
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56
Hands-On Exercise
Predictive Analytics
Method:
• Create a visual K-means cluster which segments customer based on usage behaviors.
30MINUTES
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Prescriptive Analytics
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Scenario: TelecommunicationsRecommend target customers based on their propensity to upgrade
Build a machine learning model to recommend target customers for the new mobile plan based on their propensity to upgrade
• Using historical data, train a machine-learning model to predict the propensity of a customer to upgrade
• Use the model to score a list of potential customers
4
Prescriptive
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How is it done?
Training
Operational
SAS VDMML(Model studio)
SAS VDMML(MAS)
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62
Hands-On Exercise
Prescriptive Analytics
Method:
• Build a predictive model to facilitate the execution of marketing campaigns
30MINUTES
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Media Links
This is You: SAS Visual Analytics
• https://www.youtube.com/watch?v=AuCBMDELhr4
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Useful Links14-days Trial
SAS Visual Analytics Trial
• https://www.sas.com/en_us/trials/software/visual-analytics/ep-form.html
SAS Visual Statistics Trial
• https://www.sas.com/en_us/trials/software/visual-statistics/ep-form.html
SAS Visual Data Mining and Machine Learning Trial
• https://www.sas.com/en_us/trials/software/data-mining-machine-learning/ep-form.html
Other Trials
• https://www.sas.com/en_us/23289/2323/sas-trials/trials-category.html