SAP Predictive Analytics Tools

Transcription

1 SAP Predictive Analytics Tools Philippe Nemery - nemeryphilippe 31/03/2016 SAPience.be USERday

2 Legal Disclaimer 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. 31/03/2016 SAPience.be USERday

3 WHY (Expected Outcome) WHAT (Agenda) EFFICIENT MEETING MEETING MAP WRAP UP WHO (Audience) MEETING MAP 31/03/2016 SAPience.be USERday

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5 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 31/03/2016 SAPience.be USERday

6 GENERAL INTRODUCTION TO PREDICTIVE ANALYTICS AND SAP S STRATEGY 31/03/2016 SAPience.be USERday

7 Rethink The Future Competing in today s marketplace means leveraging all types of data 31/03/2016 SAPience.be USERday

8 Analytics solutions from SAP Big Data Inform GRC Any Device Cloud BI Discover EPM Social Plan Anticipate Predictive Trust Real-time Business 31/03/2016 SAPience.be USERday

9 Business Intelligence - Advanced / Predictive Analytics 31/03/2016 SAPience.be USERday

10 User Engagement Business Intelligence - Advanced / Predictive Analytics Sense and respond Optimization Predict and act Predictive Modeling What is the best that could happen? Predictive Analysis Agile Visualization Raw Data Cleaned Data Standard Reports Ad Hoc Reports & OLAP Self Service BI Why did it happen? What will happen? What happened? Maturity of Analytics Capabilities The key is unlocking data to move decision making from sense and respond to predict and act 31/03/2016 SAPience.be USERday

11 Advanced / Predictive Analytics Predict and act Optimization Predictive Modeling What is the best that could happen? Predictive Analysis What will happen? analyze current and historical facts to make predictions about future events and outcomes 31/03/2016 SAPience.be USERday

12 Advanced / Predictive Analytics Predict and act Campaigns, Acquisition Engagement, Retention Offers, Coupons, Online, Mobile Optimization Predictive Modeling What is the best that could happen? Recruitment, Retention Forecasting, Finance Credit, Debt, Suppliers Predictive Analysis What will happen? Transactional, Internal, Cyber Internet of Things, Assets analyze current and historical facts to make predictions about future events and outcomes 31/03/2016 SAPience.be USERday

13 Predictive Use Cases and Successes Financial Services 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 Consumer Products Inventory and Logistics Planning Smart Vending Sales Forecasting Real-time, Demand Driven Business Planning 31/03/2016 SAPience.be USERday

14 Applying Predictive to Real Business Problems Types of Business Problems Solved with Predictive Sales and Marketing Operations Fraud and Risk Finance and HR Others Churn Reduction Predictive Maintenance Fraud and Abuse Detection Cash Flow and Forecasting Life Sciences Customer Acquisition Load Forecasting Claim Analysis Budgeting Simulation Health Care Lead Scoring Inventory/demand Collection and Delinquency Profitability and Margin Media Product Recommendation Optimization Credit Scoring Analysis High Education Campaign Optimization Product Recommendation Operational Risk Modeling Financial Risk Modeling Public Sector/ Social Customer Segmentation Next Best Offer/ Action Price Optimization Manufacturing Process Opt. Crime Threat Revenue and Loss Analysis Employee Retention Modeling Succession Planning Sciences Construction and Mining Travel and Hospitality Quality Management Big Data and IoT Yield Management 31/03/2016 SAPience.be USERday

15 DEFINITION OF SOME PREDICTIVE PROBLEMS 31/03/2016 SAPience.be USERday

16 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. 31/03/2016 SAPience.be USERday

17 Building the model Learning Phase Analytical Data Set Classification algorithm to predict probability of Churner = yes Model Train the model Explanatory Variables 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 yes IF city= Miami Score += 0.7 IF city= Orlando Score += 0.2 IF age > 42 Score += 0.05*age IF age <= 42 Score += 0.01*age Produce a scorecard Target When we train the model, the outcome is known Estimation Validation Add up each component score to give an overall score for each customer this will equate to their churn probability

18 Using the model Applying Phase New Data, Unknown Outcome Model Scored Data Name City Age Churner Marine Miami 45? Julien Miami 52? Fred Orlando 20? Michelle Boston 34? Nicolas Phoenix 90? IF city= Miami Score += 0.7 IF city= Orlando Score += 0.2 IF age > 42 Score += age* IF age <= 42 Score += age* Name City Age Score Marine Miami Julien Miami Fred Orlando Michelle Boston Nicolas Phoenix Apply the model onto new data to calculate the overall score or probability for each customer Select Score Cutoff Threshold

19 Predictive Concepts Historical data Typical and recurrent behaviors Predict and act Customers Products Transactions Equipments Appetency Risk Fraud Marketing Finance Maintenance 31/03/2016 SAPience.be USERday

20 Steps in a predictive project Data Preparation Model Creation Model Management Data Connections Data Manipulation Variable Reduction & Sampling Predictive model creation Model Interpretation Scoring & Validation Application to business 31/03/2016 SAPience.be USERday

21 Steps in a predictive project Data Preparation Model Creation Model Management Model Management Question Maintain Control Connect to Database Design Manipulation Execute Manipulation Semantic Layer Automated Modeling Deploy Results Consumption Model Creation 31/03/2016 SAPience.be USERday

22 Regression and Classification (Scoring) 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 Historical data (Model training) Predictive model New customer data (Model scoring) Identify best targets 31/03/2016 SAPience.be USERday

23 Demand forecast Time-series models can capture multiple effects to explain and forecast demand and cash flows: Trend Seasonal pattern External variables 31/03/2016 SAPience.be USERday

24 Network Analysis 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 31/03/2016 SAPience.be USERday

25 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% 31/03/2016 SAPience.be USERday

26 PREDICTIVE ANALYTICS SOLUTIONS 31/03/2016 SAPience.be USERday

27 SAP vision Operationalize predictive analytics into decision processes At The Point of Decision: Making predictive models more consumable and usable Empower the Business Open and Flexible Platform Embedded into Apps and Processes In-Database Scoring 31/03/2016 SAPience.be USERday

28 Specialization Today, Predictive Analytics is an Island Executives/ Business Users ETL LOB Chasm ETL PM PA DM Automated Predictive Data Scientist Cleaned Data EIM Raw Data Reporting Analysis Discovery BI BI Dashboards Invisible Analytics in BI Data Analysts Sophistication / Skill Set Bringing Predictive Analytics To Business Users Is Key 31/03/2016 SAPience.be USERday

29 I I I SAP Lumira Agile BI Predictive Analytics with / without SAP HANA [On-Prem, HPC, HEC] HEC SAP HANA On-Prem SAP RDS SQL Engine In-Memory Processing Engine Graph Engine Spatial Engine Text Engine SAP Predictive Analytics SAP PA Calculation Engine Custom Applications PAL APL R-Scripts AFL Desktop Industry & LoB Applications : Brand Sentime nt - Person alized ) Care M A R K E T N G Predictive Maintenance Fraud Detection SAP HANA CLOUD Network Asset Optimizat Tracking ion Real-time Product Demand/ Recommend S S Supply INDUSTRIES ation E AForecast R L V E SAP S PREDICTIVE ANALYTICS C E Insider Threats 360 O Customer View OTHERS ON-PREMISE Propensity to Churn O P Risk EMitigation, RReal-time A T O N S Application Services Association Analysis Cluster Analysis Classification Analysis Processing Services Outlier Detection Link Prediction Time Series Analysis Database Services + 60 Native Algorithms Integration Services R-Engine (external) Partner Apps & Tools (e.g. SAS) Partner Solution HCP Application Function Modeler AFM PAL / SQL 31/03/2016 SAPience.be USERday Hana Studio

30 SAP Predictive Analytics 2.4 with / without SAP HANA [On- Prem, HPC, HEC] HEC Prem SAP Predictive Analytics SAP PA Desktop Unstructured data in Social Media and Hadoop Microsoft Excel Oracle, IBM DB2, Microsoft SQL Server and other relational data sources HCP OLAP Cubes EDW SAP NetWeaver BW ; Teradata Other data warehouses ERP SAP Business Suite ; Oracle E- Business Suite ; PeopleSoft ; JD Edwards 31/03/2016 SAPience.be USERday

31 SAP Predictive Analytics Insight to Action ENGAGE VISUALIZE PREDICT Automated Analytics In-process, embedded, actionable analytics Insight - Find the unknown unknowns Expert Analytics Visually-rich, self-service predictive modeling Action - Tell the story Data manager Modeler Model manager Social Recommendation Algorithms PA, PAL, APL Custom R integration Advanced visualization Collaboration Non-SAP SAP SAPience.be USERday /03/

32 HANA Automated Predictive Library (APL) Data Preparation Model Creation Model Management Data Connections Data Manipulation Variable Reduction & Sampling Predictive model creation Scoring & Validation Model Interpretation Application to business Automated and simplified by PA-Automated Optimal model selected Automated Automated Simplified automatically 31/03/2016 SAPience.be USERday

33 Automated Analytics Modeler Predictive power in days not months 31/03/2016 SAPience.be USERday

34 SAP Predictive Analytics - APL Better models, faster Model Industrialization (Each Step has been Automated) In-database Automated Dataset Production Automated model creation Classification Regression Clustering Forecasts In-database Deployment Deployment in other apps Model productionization Control Recalibration Batch production 31/03/2016 SAPience.be USERday

35 Conclusions 31/03/2016 SAPience.be USERday

36 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 31/03/2016 SAPience.be USERday

37 THANK YOU 31/03/2016 SAPience.be USERday