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SAP Predictive Analytics’ Tools
Philippe Nemery - [email protected]
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nemeryphilippe@nemeryp
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• The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. This presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation and SAP's strategy and possible future developments, products and or platforms directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information in this document is not a commitment, promise or legal obligation to deliver any material, code or functionality. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP´s willful misconduct or gross negligence.
• All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.
Legal Disclaimer
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WHO
WRAP UP
WHAT
WHY EFFICIENT
MEETING
MEETIN
G M
AP
MEETIN
G M
AP
(Audience)
(Expected Outcome)
(Agenda)
AGENDA
• General Introduction to Predictive Analytics and SAP’s strategy
• Example of Predictive Use Cases
– Definition of some predictive problems
– Steps in Predictive Process - Operationalization
• Predictive Analytics w/wo HANA
• Conclusions
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GENERAL INTRODUCTION TO PREDICTIVE ANALYTICS AND SAP’S STRATEGY
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Rethink The FutureCompeting in today’s marketplace means leveraging
all types of data
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Analytics solutions from SAP
Predictive
EPM
Discover
Inform
Anticipate
Plan
GRC
Social
CloudAny Device
Trust
Big Data
Real-time Business
BI
Business Intelligence - Advanced / Predictive Analytics
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Business Intelligence - Advanced / Predictive Analytics
Sense and respond Predict and act
The key is unlocking data to move decision making from sense and respond to predict and act
Raw
Data
Cleaned
Data
Standard
Reports
Ad Hoc
Reports &
OLAP
Agile Visualization
Predictive
Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best
that could happen?
User
En
gag
em
en
t
Maturity of Analytics Capabilities
Self Service BI
Predictive
Analysis
Advanced / Predictive Analytics
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Predictive
Modeling
Optimization
What will happen?
What is the best
that could happen?
Predictive
Analysis
Predict and act
analyze current and historical facts to make predictions aboutfuture events and outcomes
Advanced / Predictive Analytics
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Predictive
Modeling
Optimization
What will happen?
What is the best
that could happen?
Predictive
Analysis
Predict and act
analyze current and historical facts to make predictions aboutfuture events and outcomes
Campaigns, Acquisition
Engagement, Retention
Forecasting, Finance
Credit, Debt, Suppliers
Offers, Coupons, Online, Mobile
Transactional, Internal, Cyber
Internet of Things, Assets
Recruitment, Retention
Predictive Use Cases and Successes
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• Improving customer loyalty and retention with better customer intelligence
• Predictive analytics on 360 Customer data to increase Wallet share in Wealth management
• SAP Predictive Analytics for real time integrated Risk and Fraud Detection
• Real time reporting for Customer Segmentation in Banks
• Predictive Cash Replenishment for better cash supply and reduced failures of ATMs
• Using Predictive Analytics for propensity to buy modeling in Retail Banking
Financial Services
Consumer Products
• Inventory and Logistics Planning
• Smart Vending
• Sales Forecasting
• Real-time, Demand Driven Business Planning
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Types of Business Problems Solved with Predictive
Predictive Maintenance
Load Forecasting
Inventory/demand
Optimization
Product Recommendation
Price Optimization
Manufacturing Process
Opt.
Quality Management
Yield Management
Operations
Fraud and Abuse Detection
Claim Analysis
Collection and Delinquency
Credit Scoring
Operational Risk Modeling
Crime Threat
Revenue and Loss Analysis
Fraudand Risk
Cash Flow and Forecasting
Budgeting Simulation
Profitability and Margin
Analysis
Financial Risk Modeling
Employee Retention
Modeling
Succession Planning
Financeand HR
Life Sciences
Health Care
Media
High Education
Public Sector/ Social
Sciences
Construction and Mining
Travel and Hospitality
Big Data and IoT
Others
Churn Reduction
Customer Acquisition
Lead Scoring
Product Recommendation
Campaign Optimization
Customer Segmentation
Next Best Offer/ Action
Sales and Marketing
Applying Predictive to Real Business Problems
DEFINITION OF SOME PREDICTIVE PROBLEMS
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SAP vision
Operationalize predictive analytics into decision processes
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A field of advanced analytics that encompasses a variety
of statistical techniques from predictive modeling,
machine learning, and data mining
that analyze current and historical
facts to make predictions about
future events and outcomes.
Building the model – Learning Phase
IF city= ‘Miami’ Score += 0.7
IF city= ‘Orlando’ Score += 0.2
IF age > 42 Score += 0.05*age + 0.06
IF age <= 42 Score += 0.01*age + 0.02
…..
Name City Age Churner
Mike Miami 42 yes
Jerry New York 32 no
Bryan Orlando 18 no
Patricia Miami 45 yes
Elodie Phoenix 35 no
Remy Chicago 72 yesClassification algorithmto predict probability of
Churner = yes
Estimation
Validation
Model
Analytical Data Set
Explanatory Variables Target
Produce a “scorecard”
Add up each component score to give an overall score for each customer – this will equate to their churn probability
Train the model
When we train the model, the outcome is known
Using the model – Applying Phase
Name City Age Churner
Marine Miami 45 ?
Julien Miami 52 ?
Fred Orlando 20 ?
Michelle Boston 34 ?
Nicolas Phoenix 90 ?
Name City Age Score
Marine Miami 45 0.8
Julien Miami 52 0.9
Fred Orlando 20 0.6
Michelle Boston 34 0.5
Nicolas Phoenix 90 0.4
New Data, Unknown Outcome
Scored Data
IF city= ‘Miami’ Score += 0.7
IF city= ‘Orlando’ Score += 0.2
IF age > 42 Score += age*0.05 + 0.06
IF age <= 42 Score += age*0.01 + 0.02
…..
Model
Select Score CutoffThreshold
“Apply” the model onto new data to calculate the overall “score” or “probability” for each customer
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Predictive Concepts
Customers
Products
Transactions
Equipments
Appetency
Risk
Fraud
Marketing
Finance
Maintenance
Historical dataTypical and recurrent
behaviorsPredict and act
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Steps in a predictive project
Data Connections
Data Manipulation
Application to business
Data Preparation Model ManagementModel Creation
Variable Reduction &
Sampling
Predictive model creation
Model Interpreta-
tion
Scoring & Validation
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Steps in a predictive project
Question
DesignManipulation
Connectto Database
ExecuteManipulation
AutomatedModeling
Deploy
Control
Model Management
Model Creation
Maintain
Results
Consumption
SemanticLayer
Data Preparation Model ManagementModel Creation
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Purpose : Define relationships between various explanatory variables and a dependent variable in order to predict its value in similar contexts.
Techniques : Mathematical and statistical techniques to analyze correlations and detects most pertinent predictors
Regression and Classification (Scoring)
Historical data(Model training)
Predictive model New customer data
(Model scoring)
Identify best targets
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Time-series models can capture multiple effects to explain and forecast demand and cash flows:
• Trend
• Seasonal pattern
• External variables
Demand forecast
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Network analysis capabilities allow you to understand influences and behaviors across customer communities
Network data comes from various sources, such as :
- Companies and citizens
- Transactions
- Social Networks
You can use network analysis to directly detect potential frauds or to enrich scoring models
Network Analysis
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Association Rules
Principles
• Association models are used to detect events that occur frequently together.
• Association models produce association rules
Main usage
• Customer cross-sells: Identify next best offer considering previous purchases
Antecedents Consequence Confidence
Rome & Prague Paris 28%
Rome & Prague & Vienna Paris 35%
London Paris 12%
London & Rome Paris 16%
London & Phuket Paris 9%
London Rome 15%
PREDICTIVE ANALYTICS SOLUTIONS
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SAP vision
Operationalize predictive analytics into decision processes
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At The Point of Decision: Making predictive models more consumable and usable
In-Database ScoringEmbedded into Apps and
ProcessesEmpower the
BusinessOpen and Flexible
Platform
LOB
Today, Predictive Analytics is an Island
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BI“Invisible
Analytics” in BI
BI
PM
DM
PA
Cleaned Data
Raw Data
Reporting
Analysis
Discovery
Dashboards
Chasm
Spec
ializ
atio
n
Sophistication / Skill Set
ETL
ETL
Automated Predictive
EIM
Bringing Predictive Analytics To Business Users Is Key
Data Scientist
Data Analysts
Executives/Business Users
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On-Prem
SAP Predictive Analytics
SAP PA
DesktopCustom Applications
Agile BISAP Lumira
Partner Apps & Tools
(e.g. SAS)
Industry & LoB
Applications
Partner Solution
:
-
)
Brand Sentime
nt
Predictive Maintenance
Network Optimizat
ion
Insider Threats
Risk Mitigation,
Real-time
Asset Tracking
360O
Customer
View
Propensity to Churn
Personalized
Care
Product Recommend
ation
Fraud Detection
Real-time Demand/
Supply Forecast
MA
RK
ET
IN
G
SA
LE
S
SE
RV
IC
E
OP
ER
AT
IO
NS
SAP PREDICTIVE ANALYTICS
INDUSTRIES
SAP HANA OTHERS
CLOUD ON-PREMISE
SAP RDS
HCP
HEC
SAP HANA
In-Memory Processing Engine
Calculation Engine
PAL R-ScriptsAPL
SQL Engine Text EngineGraph Engine Spatial Engine
AFL
Database Services
ApplicationServices
ProcessingServices
IntegrationServices
Application Function Modeler
AFM – PAL / SQL
Hana Studio
• Association Analysis
• Cluster Analysis
• Classification Analysis
R-Engine
(external)
• Outlier Detection
• Link Prediction
• Time Series Analysis
• + 60 Native Algorithms
Predictive Analytics with / without SAP HANA [On-Prem, HPC, HEC]
SAP Predictive Analytics 2.4 with / without SAP HANA [On-Prem, HPC, HEC]
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SAP Predictive Analytics
SAP PA
Desktop
HCP
HEC Prem
ERP
SAP Business Suite ; Oracle E-
Business Suite ; PeopleSoft ; JD
Edwards
Unstructured data in Social
Media and Hadoop
OLAP Cubes
Microsoft Excel
Oracle, IBM DB2, Microsoft
SQL Server and other
relational data sources
EDW
SAP NetWeaver BW ; Teradata ;
Other data warehouses
SAP Predictive Analytics 2.4 - Insight to Action
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Expert AnalyticsVisually-rich, self-service predictive modeling
Insight - Find the ‘unknown unknowns’ Action - Tell the story
Automated AnalyticsIn-process, embedded, actionable analytics
ENGAGE PREDICTVISUALIZE
• Data manager
• Modeler
• Model manager
• Social
• Recommendation
• Algorithms PA, PAL, APL
• Custom R integration
• Advanced visualization
• Collaboration
Non-SAP SAP
HANA Automated Predictive Library (APL)
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Optimal model selected automatically
Automated and simplified by PA-Automated
Data Connections
Data Manipulation
Variable Reduction &
Sampling
Predictive model creation Scoring &
Validation
Model Interpreta-
tion
Application to business
Automated Automated Simplified
Data Preparation Model ManagementModel Creation
Automated Analytics ModelerPredictive power in days not months
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SAP Predictive Analytics - APLBetter models, faster
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In-database Automated Dataset Production
Automated model creationClassification RegressionClustering Forecasts
In-database DeploymentDeployment in other apps
Model productionizationControl Recalibration Batch production
Model Industrialization (Each Step has been Automated)
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Conclusions
SAP Predictive Strategy
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SAP Predictive Analytics: a modern user interface to support the definition of predictive analysis processes and their visualization
In- Database Predictive Analytics Library within SAP HANA for real time and large data volume data analysis
R integration for SAP Predictive Analysis and SAP HANA to provide a very comprehensive range of predictive algorithms
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THANK YOU
