SAS: Analytics, Artificial Intelligence and Data Management | SAS - … · 2018. 7. 4. · Prepare an analytic base table used for reporting and analytics • Examine, profile, and

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SAS Viya WorkshopAccelerating Innovation with the SAS Platform


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


Introduction to SAS



Analysts rank SAS as a leader in:

In 2017, analysts ranked SAS a leader or leader equivalent in more than 30 reports


VALUEANALYTICSDATA



Key Focus Areas

Analytics for EveryoneAnalytics EverywhereArtificial Intelligence



SAS Platform for the Citizen Data Scientist


The SAS Platform


Our digital transformationto power the analytics economy


Visual Exploration

Programming Interface

Visual Drag-and-DropInterface

Data Preparation

Machine Learning / Artificial Intelligence

Interactive Reporting

Predictive Analytics

Share, Collaborate,

DeployGovernance

Extending the Paradigm





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



Customer Demographic Data

Customer Usage Data

Roll-up by customer IDs


Types of Analytics



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


Descriptive Analytics


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




1

2

3



1

2




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



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




Diagnostic Analytics


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




49

Diagnostic Analytics

Methods:

• Build simple statistical models to identify factors contributing to patterns, relationships, and trends

Hands-On Exercise

90MINUTES


Types of Analytics




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


Clustering…Conceptually



56

Hands-On Exercise


Method:

• Create a visual K-means cluster which segments customer based on usage behaviors.

30MINUTES



Prescriptive Analytics


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


How is it done?

Training

Operational

SAS VDMML(Model studio)

SAS VDMML(MAS)



62

Hands-On Exercise

Prescriptive Analytics

Method:

• Build a predictive model to facilitate the execution of marketing campaigns

30MINUTES



Media Links

This is You: SAS Visual Analytics

• https://www.youtube.com/watch?v=AuCBMDELhr4

https://www.youtube.com/watch?v=AuCBMDELhr4


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

https://www.sas.com/en_us/trials/software/visual-analytics/ep-form.html

https://www.sas.com/en_us/trials/software/visual-statistics/ep-form.html

https://www.sas.com/en_us/trials/software/data-mining-machine-learning/ep-form.html

https://www.sas.com/en_us/23289/2323/sas-trials/trials-category.html

Documents

SAS: Analytics, Artificial Intelligence and Data Management | SAS - … · 2018. 7. 4. · Prepare an analytic base table used for reporting and analytics • Examine, profile, and