2. Customer Experience Optimization
Charles Schwab: Optimized Customer Experiences with Big Data and Oracle Real-Time Decisions [CON5352]
Customer Experience Optimization
Andy Welch, Principal Architect Charles Schwab
Rich Masi, Partner NewVantage Partners
Joe Khazen, Director, Real Time Decisions
October 1, 2014
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4. Agenda 1
Overview of Real Time Decisions Charles Schwab-RTD and BigData Use Case Future Plans Q & A 2 3 4

5. Creating Great experiences is the Imperative
ATM
“meet me, and engage me”
“delight me, and guide me”
PORTAL
KIOSKS
BRANCH
SOCIAL
“know me, and wow me”
“understand me, and reward me”
MOBILE
Call Center
WEB

6. What We Hear from Customers
Leverage data
Optimize
Experience
Adapt quickly

7. Oracle’s Customer Experience Portfolio
In Store
Contact Center
Social
Field Service
Direct Sales
Mobile
Channel Sales
Cloud Platform
Services
CX for Marketing
CX for
Commerce
CX for
Sales
CX for
Service
Social
Platform
Services
Web
Real Time Decisions (RTD)
Common Hardware Systems Infrastructure

8. Optimize the Customer Experience with RTD
Optimal & Personalized Customer AND Business Centric Recommendations
KPI Arbitration
Eligibility Rules & Models
Offers/NBAs
Flexible Way to Make Decisions
Single decision engine supporting a consistent customer experience across all channels
Easily Integrated into Existing Applications
Goals, Rules, Models, Optimization, Arbitration
Automated Self Learning
Incrementally builds Analytical models for Learning and Decisions
Analytical Adaptive Decisions
Quantifiable Results
Quantifiable and Measureable Lift on Each Project
Various Test and Control Scenarios

9. Exceptional Customer Experience
Real Time Decisions
Real time contextual data σ +
Historical data +
Predictive Modeling =
Personalized recommendations, offers & actions
Self-Learning Loop
Speaker Notes:
Target Audience
Relevant external sources eg Social Media

10. Oracle Customer Experience
Optimize Every Decision
Service
Service treatments optimization
Customer retention programs
Call center optimization
Risk and fraud analysis enhancement
Next best action
Sales
Customer Acquisition
Cross Sell/Upsell
Personalization
Offer Optimization
Next best action
Marketing
Customer experience optimization
Themes, Colors, Navigation
Next best offer
A/B and multi-variant testing
Content personalization
Oracle Confidential – Internal/Restricted/Highly Restricted

11. Optimized Customer Experiences with Big Data and Oracle Real-Time Decisions
Andy Welch
Principal Architect, Charles Schwab
Rich Masi,
Partner, NewVantage Partners

12. Why Oracle Real-time Decisions
Goal: Optimize customer experience by delivering relevant content
Oracle RTD was selected because it best met functional needs for a world-class real-time decision management environment
World-Class Decision Management
Scope of Decisions
Technology Foundation
Marketing Operations
Across a channel experience
Across channels
Across a customer lifecycle
Speed
Context sensitivity
Scalability
Marketing velocity
Goal management
Analytical ideation
Key Functional Architecture Component Areas .
Open architecture to support real-time communication with channels
Decision management business interface to manage a decision set and decision strategy
Decision service that returns an optimized result to channel requests in real-time
Learning service that is updated in real-time based upon channel actions
Open architecture to support integration with data systems including a big data platform
Business insight platform for reporting and analytics 12

13. RTD at Charles Schwab
Initial use of RTD to support content in real-estate across hundreds of Schwab.com pages
Supports millions of optimized content requests per day
Meets very tight response time SLA
More than double the response rate versus legacy approach

14. RTD High Level Flow Login Pages CMS RTD Decision Service
login sends start session informant to RTD with a customer identifier when the user has been successfully authenticated
RTD sources the customer profile from the big data cluster using the customer identifier though a custom integration and establishes RTD session
RTD sources choice history from RTD database
Page visit sends Advisor request for optimized content to RTD.
RTD decision service processes business rules, predictive analytics and decisions
RTD returns an ordered list of content ids to page
Web page calls content system to render content
A content response sends a response informant to RTD
RTD updates session profile
RTD logs choice history and learnings
Learning service processes learning records 7 1 4 6 8
RTD
Decision Service
Learning Service 5
Big Data Cluster
RTD Session 9 2 11 3 10
RTD Database

15. Enterprise Decision Management
Created a decision management taxonomy that maps to business stakeholders management practices.
Designed this structure to support:
Enterprise level goal management practices
Multivariate testing at a content level
Multiple channels
Many placements (pages)
Many slots (page positions) per placement with varying number of items returned per slot
Many slot types (content types) for placements
Decision strategy testing within and across placements and slots
A learning graph based upon content, user and placement metadata dimensions

16. Initial Test Design BAU Random Group Business Rules Group
Visitors are placed in a group in real-time based upon random assignment which is persistent for future visits.
Response rate for Business Rules Group is significantly greater than the Business as Usual (BAU) Random Group
Response rate for Rules + Analytics Group is more than double the Business as Usual (BAU) Random Group response rate
Group membership and real-time likelihood score are written to table with each event
Business can add other groups or change definition of current groups through a business interface
BAU Random Group
Business Rules Group
Rules + Analytics Group
Random 5% of population
Random 15% of population
Random 80% of population
Random selection of BAU banners
Random selection within business rules
Response likelihood selection within business rules

17. Determine Group Membership Process Decision Strategy
Decision Processing
Determine Group Membership
Eligibility Rules
Score Content
Process Decision Strategy
BAU Random Group
No eligibility rules
Assign random score
Arbitrate based upon random score
Group membership, rules, and decision strategy are all configurable through a business interface
Business Rules Group
Filter for content eligibility
Assign random score
Arbitrate based upon random score
Rules and Analytics Group
Filter for content eligibility
Calculate likelihood of response
Arbitrate based upon likelihood score

18. Multivariate Testing
Rolling out multivariate testing practices to promote continuous campaign improvement
Based upon a proven practice developed over 15+ years of real-time marketing program optimization
Practice identifies program areas with the most opportunity for improvement and aligns marketing levers with improvement areas
As a continuous champion-challenger optimization strategy, the value received from these practices compound rapidly and are typically are the largest value driver for programs

19. Learning Graph Channel
Created a learning graph by configuring RTD to learn on metadata of content, placement, and user info
All presentation and response events are written to predictive analytics that build a response profile of higher order elements up and across the learning graph
All analytics partitioned by channel to allow for multi-channel rollout
General Subject
Placement
User Device Type
Detailed Subject
Slot
User OS
Category
Slot Type
User Application
Content

20. RTD / Big Data Integration
Developed custom big data integration with RTD.
Big Data Cluster has a robust customer profile information for millions of customers.
Creates customer profiles from multiple data sources and application-specific definitions.
Significantly outperforms benchmark database retrieval
Data Service
RTD
Java
GetProfile
RTD Session
Decision Service
Learning Service
Data Cluster
RTD Database

21. Highly Available and Scalable Environment
Web Page
Highly Available: Multiple Active/Active Data Centers
Scalable: Multiple servers running Decision Services in each location with learning service on separate instance
DR DB Replica

22. Next Steps Decision strategy testing Multivariate testing
Rollout to additional placements/slots
Enhance data model
Expand channels
Presented Count