powering your decision makers with predictive analytics...
TRANSCRIPT
Powering your Decision Makers with Predictive Analytics Leveraging SAP HANA and R
Sundar Dittakavi, SAP COE Lead – HANA & Analytics DMI – Big Data insights
February 21, 2014
Overview
Business Intelligence
Describes “What” is happening today
Enables an organization to visualize and consume data
Advanced Analytics
Identifies “Why” events happen
Two Parts:
• Data Analysis
• Statistical Model
Results are integrated with and delivered via the BI Platform
Predictive Analytics (PA)
Extracts information from past data to predict “What Will” occur
Allows business analysts and data scientists to predict business outcomes
Data mining and algorithms are used to derive meaning from data - discover meaningful patterns, trends and rules
Overview
So, what is Analytical Process?
• Cross Sell
• Up Sell
• Reduce Price
• Add-On
Activate
Operationalize and update
strategy
• KPIs
• Dashboards
• ROI
Measure
• Declining Profit Growth
Business Problem
• Attrition issue
• Margin Customer Acquisition
• Volume Mix Shift
Analytics
• Health Check Dashboard
• Customer Migration Report
• Price/Attrition Alerts
Activate
Analytic Approach
Businesses gain business value and competitive advantage using predictive modeling and optimization techniques.
Bu
sin
ess
Imp
act
& B
usi
ne
ss V
alu
e
Traditional Business Intelligence
Visualization Tools
Data Mining Predictive
KPIs (Aggregates)
What Happened? (Information)
Why did it Happen? (Insights)
What will Happen? (Prediction)
Sales Budget, Evolution of Sales
Sales by region, family, Fleet, month
What differentiates best customers
Which customers will leave? Which services to recommend?
Competitive Advantage
to grow...
Analytics Roadmap
Predictive Analytics
Customer Migration Reporting
Key Drivers & Metrics
Customer Loyalty
Customer attrition
Retention Modelling
Attrition Modelling
Revenue and Margin Forecasting
Pricing Strategy
Relevant Communications
Merchant Product Enhancements
Merchant Behavioural Selection (targeting)
Analysis
Trends
Segmentations
Capture relevant attributes
Health Check Reporting
Merchant Engagement
& Growth enables... to drive...
Diagnostics
Knowing future alerts,
KPI’s and metrics
Forecasting time series
data: sales, costs,
headcount, revenue
Identifying hidden
revenue opportunities
within the customer base
Retaining high value
customers, employees
vendors etc. with the right
retention offers
Increasing cross sell and
up sell effectiveness
through cross channel
coordination
Building long term
relationships with
intelligent interactions
Cluster segmentation
analysis of products,
customers, cost centers
Text mining
What if analysis
Risk analysis
Model building, Business
Process monitoring etc.
Business Value creation using Predictive Analytics
Data Mining and Analytics Examples
Example 1: Scenario - By using Data Mining & Analytics we were able to identify a relationship between the sale of two products, say A and B.
Business Decision - Decided to bundle and sell the two products together – while offering a discount on one of them if the customer buys the other one.
Business Outcome - Sales of product A and B combined went up 20% compared to last quarter.
Example 2: Business Problem - Declining Profit Growth.
Scenario - By using Data Mining and Analytics we were able to perform a Health check, and implement Price/Attrition alerts.
Business Decision - We found opportunities to cross-sell and reducing pricing on certain products.
Business Outcome - Lower prices compared to competitor resulting in increased number of sales.
Real Time and Proactive – What does it take?
Information Set
Decision Set
Predict
And Act
Analytics Process
Action Distance
Source: Richard Hackathorn at TDAN.com
Speed of Analytics – Why business reaction is slow? B
usi
ne
ss
Val
ue
Capture Latency
Analysis Latency
Decision Latency
Time
data stored
information delivered
action taken
Captured Business Value
Source: Richard Hackathorn at TDAN.com
business event
Bu
sin
ess
V
alu
e
Time
Captured Business Value
Speed of Analytics – How can we increase speed?
business event
data stored
information delivered
action taken
Source: Richard Hackathorn at TDAN.com
Bu
sin
ess
Val
ue
Captured Business Value
Speed of Analytics – Getting Real Time …….
business event
Time
data stored
information delivered
action taken
Source: Richard Hackathorn at TDAN.com
Processes operational and analytical data
Much faster than traditional RDBMS
No need for a separate DW for Analytics – build real-time virtual OLAP functions on top of OLTP store
RETURN ON INVESTMENT
HANA - SAP's in-memory database (IMDB) and analytics platform
Huge volumes of data analyzed in real time
In-database analysis of 'big data'
Super high speed of analysis
SAP Integration
RETURN ON INVESTMENT
SAP HANA as source of data for In-database Predictive Analysis provides powerful analytics and visualization with data analysis in real time and real-time business
insights with near-zero latency
SAP’s Predictive Analysis + SAP HANA
Predictive Analysis using SAP HANA
Native Algorithms
Integration with HANA Predictive
Library (PAL)
“R” integration in HANA
3000+ Predictive Analysis
algorithms
KEY LEARNINGS
SAP HANA – R Integration - Technical Architecture
Operationalizing Predictive Analytics using SAP HANA and R – Data flow
Pre
sen
tati
on
Lay
er
Reports
Dashboards
Mobile
Explorer
Business Users
R /HANA Developer
SAP HANA Studio
Data Sources
SAP HANA
R Serve
R Model updates
Data Integration / ETL
R script Development
Real Time Decision Making using Data enhanced by Predictive Analytics
Pre
sen
tati
on
Lay
er
Decision Making
Data Sources
SAP HANA
R Serve
Data Integration / ETL
Transactions - Real Time
ETL Execution Near real time possible
Report Query Execution Near real time possible
R Model Execution Based on complexity of the model
How to?
Utilize Open Source R as Data Mining calculation engine or embed R
scripts in SQL scripts
Use Open Source R's console to interact with SAP HANA data
Using Business Functions Library (BFL) that runs as part of HANA engine
Analysis Methods
Step 1:
Organize Data
Step 2:
Understand and Visualize Data
Step 3:
Analyze Data (Build and run model)
Step 4:
Publish, Share, Store Results or
Model
The Process
Data from several
enterprise data sources
- HANA DB
Data in unstructured
formats -> HANA DB
Exploratory Data
Analysis to observe
patterns, trends
Filter, Sample Create
time and geographical
hierarchies etc.
Create visualizations
using analytics layer,
and graphic libraries
Utilize HANA engine
and R libraries or
algorithms to analyze
Create customized R
scripts to generate
views and interfaces to
identify root cause,
solutions, correlation,
risks, trends etc.
Stored in DB including
HANA
Integrated and
accessed using PMML
or BI tools (reports
and dashboards)
Predictive Analytics Process
Analytics Challenges
Big data – sorting, filtering, analyzing, storing large quantities of data at high speeds in real-time
Choosing the most suitable algorithm/model and calibrating the model to fit the business and situation
Complex data visualizations - try new chart types that are available – bi-directional bar chart etc.
Determining the appropriate data visualization techniques. Design and provide intuitive and interactive visualization for users to play with (Dashboards using Xcelsius, Adobe Flex)
Data Errors - Be aware that transactional data could have errors and potentially lead you to wrong conclusions. Good Data leads to good model and vice versa
For Analysts
Predictive modeling techniques
Building your own model using statistical modeling - Most Common
methods to find patterns in data include Decision Trees, Linear and
Logistic regression, Clustering
Integrating with custom built solutions that have in-built predictive
analysis – Faster turnaround, Lower Cost
Techniques
We help clients perform analyses
specific for their business, by offering pre-built, easy to integrate, automated solutions
We deploy statistical models to provide on-demand insights
We build forecasting dashboards to
drive improved user experience and adoption
Customized solutions for specific industry vertical
Our Predictive Analytics Solutions
Our SAP HANA Predictive Applications
• Sales forecasting
• Load demand forecasting
• Text Analytics
• Demand driven scheduling
• Price optimization
• Campaign management
• Profitability Analysis
• Precision Marketing
• Customer Usage Analytics
• Risk Management
• Customer Engagement Intelligence
We have expertise in creating
actionable segmentations (customer,
product), building complex predictive
and optimization models including
Attrition, Cross-sell, Queuing, Risk and
Demand Forecasting with high
predictive accuracy
Data to Insights - How Traditional Process works?
Data sources Data Integration Data Marts
Pre
sen
tati
on
To
ol
Business Analysts
Decision Makers
Report Consumers
Integrated Data Warehouse and BI
Dat
a A
nal
ytic
s End to End Data Analytics Solution
6-8 months 6-8 months 6-8 months
Building data and delivering insights at the same time
Enriched and Intelligent Data
Feedback Process (Update Analytical Models)
Our Integrated Solution
Our Analytical
Engine
Analysts and Business Users
Decision Makers
Analysts Business Users Decision Makers
Analytical Data Synchronization
Data sources Data Integration Data Marts
Integrated Data Warehouse and BI
Pre
sen
tati
on
To
ol
Our Proprietary Analytical Engine for faster turnaround
Our Analytical Engine
Data sources
Growth
Targeted audience
WebI
Dashboards
Upside potential Explorer
Attrition Rate
Pricing Strategy
Mobility
An automated end-to-end solution with up-to-date predictive analytics embedded within data layer
Data
Integration
Logic
Report
Calculations &
Business
Rules Data
Quality
Logic
Data
Security
Logic
Multiple
Regression
Survival
Segmentation
Probabilistic
Model
Analytics Engine Highlights
Analytical Engine has the R model that runs with HANA to deliver real-
time model outputs available as part of operational BI
Predictive models to solve high impact business problems across
multiple industry verticals
Retail
“Attrition and Price” model - platform for customer
analytics and measurement
HealthCare
“Readmission Risk” model - a decision
support system
Finance
Re-pricing alert using “upside modeling” using regression and decision
trees
Showcase
APPENDIX
Healthcare Solution
Meaningful Use Reporting
Predictive model for reducing readmissions with tailored post discharge
Surgical case scheduling model to optimize offline scheduling
Insurance Solution
Agent profiling using Decision trees
Agent segmentation using peer benchmarking and agent performance traits
Focused cross-sell lead alerts based on life events
Targeted lead lists for agents for higher close ratios
a b
e
f
c g
Measure and enable agents leading to improved recruiting
and profitable growth
Finance Solution
Holistic customer centric pricing solution to meet
business performance goals with:
Attrition modeling
“Re-pricing and upside report “ that provides optimal pricing
“Cross-sell product report” that drives relevancy and stickiness
Statistical modeling to evaluate risk profile and potential upside
Attrition Modeling
Price Sensitive Bands
Identify Target Margin
• Applied decision tree
(boosted and logistic
regression modeling)
to score merchants
• Separate models
developed for each
industry
• Predicted AUC
values 0.8 & higher
• Merchants scored
based on their
likelihood to attrite
in the next month
• Used attrition score
as an indicator to
determine price
sensitivity
• Created H/M/L Price
sensitive bands
based on the
attrition scores and
cut off values
• Targeted pricing
strategy matrix (3x3)
based on price
sensitivity and
upside potential
• Calculated target
margin based on
maximum allowable
increase before
merchants switches
towards higher risk
(i.e. price sensitivity)
• Developed an
updated pricing
strategy grid (3x18)
(to achieve the target)
based on combination
of price sensitivity,
upside and
penetration of
products
Attrition Alerts
Re-Pricing Alerts
Actionable Reports - Overview
Segment Benchmarks
Re-Pricing Alert
Attrition Alert
Cross-Sell Alert
THANK YOU FOR PARTICIPATING
For ongoing education on this area of focus, visit ASUG.com
Sundar Dittakavi, SAP COE Lead HANA & Analytics
DMI – Big Data insights [email protected]