Presented by Dr Kingshuk Banerjee from IBM at ISS Seminar: Analytics for Enhanced Customer Experience on 9 May at Institute of Systems Science, NUS.

1. © 2012 IBM Corporation
Marketing to the Segment of One
Trends and A Case Study
Kingshuk Banerjee, D.Sc.
Leader, Center-of-Competence, Business Analytics and Optimization
IBM Global Consulting Services

2. Trends
1. Describe, Predict and Prescribe for A Specific Customer
2. Micro-segmentation, Personalization and Next Best Action
3. Big Data Leverage
4. Mining the Unstructured
5. Realtime decision-making: People, Process and Technology

3. © 2012 IBM Corporation
Trend 1: Describe, Predict and Prescribe ...
Business
Impact
Heroics
Foundational
Competitive
Differentiating
Break-away
•Spreadsheets
•Extracts
•MDM
•Data Warehouses
•Data Governance
•Micro-Segmentation
•Pattern recognition
•View Consolidation
•Dashboards
•Mathematical Optimization
•Reinforced Learning
Descriptive
Customer 360 View
Prescriptive
Prescribe the
Optimized Action
Source: IBM; Davenport et al, “Analytics at Work”
Predictive
Predict the Behavior
... for A Specific Customer

4. © 2012 IBM Corporation
Trend 2: Micro-Segmentation, Personalization, NextBestAction
Driving a major Shift in Sales and Marketing Strategy
.. from selling “what I have” to focusing on “what YOU need”
Allocate
Optimized
Offer
CUSTOMER
Needs
ENTERPRISE
Objectives
Who am I ?
What do I need?
When do I buy?
Where do I buy?
Whom should I offer?
What should I offer?
When should I offer?
How should I offer?
• Demographics
• Purchases
• Interactions
• Preferences
• Purchase Cycle
• Propensity to Buy
• Purchase Drivers
• Purchase Triggers
• Purchase Affinity
• Activity Based
• Life Event Based
• Shopping Trip Types
• Channels / Devices
• Locations
• Occasions
Customer
Profile
Foundation
• Micro-
segmentation and
Personalization
Optimized
Marketing
Activities
using Mathematics
and Simulation
• Offer Allocation
based on Goal and
Constraints
• Offer Timing
• Channel Selection

5. © 2012 IBM Corporation5
e.g. Banking Customer
Multiple Manifestation of the same Individual
Behavioral data
- Orders
- Transactions
- Payment history
- Usage history
Descriptive data
- Attributes
- Characteristics
- Self-declared info
- (Geo)demographics
Attitudinal data
- Opinions
- Preferences
- Needs and Desires
Interaction data
- Email / chat transcripts
- Call center notes
- Web Click-streams
- In person dialogues
Who? What?
Why?How?
Consolidate
Data across Lines- of-Business
Analyze
Predict and Prescribe
Describe
customer holistically .. multiple dimensions .. 360 view
Care
Retain
Enhance
Bill
Collect
Sell
Cross Sell
/ Up Sell
Centralize
Data on Customer Interactions Across Channels
Retail Client --->
Small Business -
Wealth Management
Trend 2 (continued): Understanding the Customer, Good Practices
… must be in tandem with Societal Characteristics, Technology Adoption and Business Needs

6. © 2012 IBM Corporation
This Asset focuses on Long Term Gain
This asset is based on Reinforcement Learning and Constrained Markov Decision Process
framework - π (s,a,r)
(s) - Customer is in some "state" (his/her attributes) at any point in time
(a) - Enterprise's action will move customer into another state
(r) - Enterprise's goal is to take sequence of actions to guide customer's path to maximize
customer's lifetime value
Current marketing policy
Optimized marketing policy
Customer A’s path under…
Bargain
Hunter
Repeater
Loyal
Customer
Valuable
Customer
One Timer
Repeater
Defector Defector
Repeater
Loyal
Customer
Potentially
Valuable
Action A
Action B
Action C
Action E
Action D
Trend 2 (continued): An IBM Research Lab Asset Next Best Action

7. © 2012 IBM Corporation
Transactional &
Application Data
Machine Data Social Data
• POS / e-commerce
transactions
• Call detail records
• Utility meter readings
• RFID tag data
• Refinery sensors
• Web log data
• Tweets
• Blogs
• Social network
members / actions
Enterprise Content
• Emails
• Document images
• Video archives
Are you tapping into data beyond the traditional, structured sources?
Trend 3: Big Data Leverage

8. © 2012 IBM Corporation
Trend 4: Mining the Unstructured
How IBM Watson performs Natural
Language Processing in
Unstructured Data?

9. © 2012 IBM Corporation
Trend 5: Real-time Personalization
Step 1: Customer walks
into an Electronic store
to window-shop for smart
phone
Step 2: Customer ends
up buying a Smart Phone
gadget
Step 3: Pays by the
Bank Mobile App
Next Best Action
(NBA)
B2C Commerce
Platform
Step 4: Transaction event and spending
location are detected by the Bank Platform
Step 5: Based on past spending patterns, house-holding analytics,
current location and transaction details – NBA suggests best offer for
this customer from its eco-system partner, located nearby
Step 6: 15% discount on
a Luis Vuitton bag in an
outlet located in the same
plaza; offer valid for this
specific customer for
next 2 hours
Payment Gateway
A Use Case
A Credit Card Company generating Next Best Offer at the time of Purchase

10. A Case Study
Data-driven, Personalized Marketing
for an European Bank

11. Confidential Material
Not for Public Share

12. © 2012 IBM Corporation12
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