Predictive Analytics: From Insight to Action

SAP Predictive Analytics’ Tools:From Insight to Action

Philippe Nemery - [email protected]

22/03/2016 SAPience.be USERday 2016 1

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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


GENERAL INTRODUCTION TO PREDICTIVE ANALYTICS AND SAP’S STRATEGY



Rethink The FutureCompeting in today’s marketplace means leveraging

all types of data


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



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

RawData

CleanedData

Standard Reports

Ad Hoc Reports &

OLAP

Agile Visualization

PredictiveModeling

Optimization

What happened?

Why did it happen?

What will happen?

What is the bestthat could happen?

Use

r E

ng

ag

em

en

t

Maturity of Analytics Capabilities

Self Service BI

PredictiveAnalysis

Advanced / Predictive Analytics


PredictiveModeling

Optimization

What will happen?


PredictiveAnalysis

Predict and act

analyze current and historical facts to make predictions aboutfuture events and outcomes

Advanced / Predictive Analytics


PredictiveModeling

Optimization

What will happen?


PredictiveAnalysis

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


• 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


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


SAP vision

Operationalize predictive analytics into decision processes


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.


Predictive Concepts

Customers

Products

Transactions

Equipments

Appetency

Risk

Fraud

Marketing

Finance

Maintenance

Historical dataTypical and recurrent

behaviorsPredict and act

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


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


Steps in a predictive project

Question

DesignManipulation

Connectto Database

ExecuteManipulation

AutomatedModeling

Deploy

Control

Model Management

Model Creation

Maintain

Results

Consumption

SemanticLayer



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


Time-series models can capture multiple effects to explain and forecast demand and cash flows:

• Trend

• Seasonal pattern

• External variables

Demand forecast


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


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


SAP vision

Operationalize predictive analytics into decision processes


At The Point of Decision: Making predictive models more consumable and usable

In-Database Scoring

Embedded into Apps and Processes

Empower the Business

Open and Flexible

Platform

LOB

Today, Predictive Analytics is an Island


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


On-Prem

SAP Predictive Analytics

SAP PA

DesktopCustom

Applications

Agile BISAP Lumira

Partner Apps & Tools (e.g. SAS)

Industry & LoBApplications

Partner Solution

:-)

Brand Sentiment

Predictive Maintena

nce

Network

Optimization

Insider Threats

Risk Mitigati

on, Real-time

Asset Trackin

g

360O

Customer View

Propensity to Churn

Personalize

d Care

Product Recommendation

Fraud Detect

ion

Real-time

Demand/

Supply Forecast

MARKETING

SALES

SERVICE

OPERATIONS

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]


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

http://www.google.com/url?sa=i&rct=j&q=excel+icon&source=images&cd=&cad=rja&docid=WUbumcVYoHvT_M&tbnid=fK-6zHhfHquhlM:&ved=0CAUQjRw&url=http://findicons.com/search/excel&ei=9t9VUabrC-fO0QHp_oH4Aw&psig=AFQjCNENQolgwoP32fvwtSTSeG3zasl7KQ&ust=1364668783033504


http://www.google.com/url?sa=i&rct=j&q=any+data+source&source=images&cd=&cad=rja&docid=OfZFVH_H4TvjXM&tbnid=5Er_7hDqdLR--M:&ved=0CAUQjRw&url=http://www.iconfinder.com/search/?q=iconset:PLASTICXP&ei=pN9VUerFMI2v0AHL8oCgDQ&psig=AFQjCNEfCiALR4OvRXGpVb4WSaxpJXF85Q&ust=1364668480592498

http://www.google.com/url?sa=i&rct=j&q=any+data+source&source=images&cd=&cad=rja&docid=OfZFVH_H4TvjXM&tbnid=5Er_7hDqdLR--M:&ved=0CAUQjRw&url=http://www.iconfinder.com/search/?q=iconset:PLASTICXP&ei=pN9VUerFMI2v0AHL8oCgDQ&psig=AFQjCNEfCiALR4OvRXGpVb4WSaxpJXF85Q&ust=1364668480592498

SAP Predictive Analytics 2.4 - Insight to Action

22/03/2016

SAPience.be USERday 2016

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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)Automated Predictive Analytics


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


Automated Analytics ModelerPredictive power in days not months


4 clicks = model

Screenshots Here.

SAP Predictive Analytics - APLBetter models, faster


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)


Conclusions

SAP Predictive Strategy


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

SAP Learning Hub & Digital TransformationA cloud-based learning platform for individuals and enterprises


Powerful learning management capabilities to manage enterprise enablement programs

Learning manage-ment system

Exclusive purchase option for on-demand access to live training systems

Live systemaccess

Unlimited access to all of SAP’s learning content, including role-based learning and certification paths

Educational content

Structured collaboration and social learning lead by experts from SAP

Social learning and learning rooms

https://training.sap.com/shop/course/paii10-sap-predictive-analytics-22-classroom-002-gb-en/

Room: Z6 area

https://training.sap.com/shop/course/paii10-sap-predictive-analytics-22-classroom-002-gb-en/


THANK YOU

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Back to Presentation

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Back to Presentation1

Specify your data.

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Data is processed.

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3

Define Target.

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Choose output.

45


Model is generated.

46


What explains the target?

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How reliable is the model?

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Take action: cost - benefit ?

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How to deploy the model ?

53


Choose the deployment.

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Predictive Analytics: From Insight to Action

Data & Analytics

Transcript of Predictive Analytics: From Insight to Action