1. The Unrealized Power of Data Andreas Weigend people & data
Predictive Analytics World San Francisco, February 19, 2009
The Unrealized Power of Data Andreas Weigend people & data

2. Outline Historical perspective Current trends
Q: Current bottleneck for you in your business? (Scarce vs abundant)?
Historical perspective
Business, Data and Communication
Current trends
From Transaction Economics to Relationship Economics
The Customer Data Revolution: Shift in Customer Expectations
Implications: From CRM to CMR
Customer Managed Relationships
Applications to business: Marketing 2.0
Why predictive analytics: Relevance
How to do it: PHAME
Problem – Hypotheses – Action – Metrics - Experiments

3. Business, Data, and Communication
“Experts” learn a language the computer understands
Digitizing back office
10M people
1980’s
Front office interacts with back office
100M people
1990’s
Customers interact with firm
Search: 1bn people poking at stuff
2000’s
1bn people poking at stuff
100M people producing stuff
Peer-production and collaboration
Customers interact with customers
Now
Discovery in addition to search
Serendipity: Discover what not searched
People in addition to pages
Social commerce
Mobile in addition to PC, and paper)
Continuous partial attention
Model current situation plus history
Sensing
60k blog posts / hour
Me - business

4. Amount of data Overall : About 100GB per person on the planet
Doubling every 1-2 years
Mainly user generated
Example: Youtube
15 hours of video uploaded every minute
Example: Flash
1bn installs

5. My behavior IMMI Listening into your room every 30 seconds,
for 10 seconds.
Shower: long shower, short shower

6. Current trends Relation-ships Inter-actions Trans-action
Market research
Combine surveys with click data
Assumption heavy  Data rich model
Trans-action
Inter-actions
Relation-ships

7. The Customer Data Revolution
1. Sniffing the digital exhaust
Mainly implicit data, some explicit data
What is new? More data sources, esp. location data
2. Individuals talk about themselves
Mainly explicit contributions
3. Individuals reveal relationships with others
Directed, asymmetrical, multidimensional (not binary!)
The Customer Data Revolution: Shifting expectations
Attitude of individuals to their information
Economics of data

8. Wishlist

9. Outline Historical perspective Current trends
Business, Data and Communication
Current trends
From Transaction Economics to Relationship Economics
The Customer Data Revolution: Shift in Customer Expectations
Implications: From CRM to CMR
Customer Managed Relationships
Customer value
E-Business  Me-Business
Who pays whom?
Applications to business: Marketing 2.0

10. Marketing 2.0 Broadcast  1:1 Marketing? Social marketing
Implications for predictive analytics: redefining CLV
Intrinsic / individual
External / network component
Applications to business
Amazon’s “Share the Love”

11. Conversations Conversation / Communication Company downcasting
Between whom?
Company
downcasting
Individuals

12. Leverage the social graph
Example: New communications service
US phone company with deep experience with targeted marketing
Sophisticated segmentation models based on experience, intuition, and data
e.g., demographic, geographic, loyalty data
Hill, S., F. Provost., and C. Volinsky. Network-based Marketing: Identifying likely adopters via consumer networks. Statistical Science 21 (2) 256–276, 2006 .
Response increases by a factor of 4.82 by marketing to nearest neighbors (NN)
From 0.28% based on segmentation, to 1.35% based on social graph
(0.28%)
(1.35%)
(0.83%)
(0.11%)
Add

13. Recommendations 2.0 People Data Friends Clicks Peers
Specific people you know
Viral marketing
Peers
Fans (G-star)
Experts
Fashion bloggers
Data
Clicks
Purchases
Forward, tell a friend
Relationship
Annotate
Attention
Search
Intention
Location
Situation
Product data

14. Outline Historical perspective Current trends
Business, Data and Communication
Current trends
From Transaction Economics to Relationship Economics
The Customer Data Revolution: Shift in Customer Expectations
Implications: From CRM to CMR
Customer Managed Relationships
Applications to business: Marketing 2.0
Why predictive analytics: Relevance
Respect
How to do it: PHAME

15. You want to be PHAME-ous!
Problem
Hypotheses
Action
Metrics
Experiments
Phishing

16. Summary Historical perspective Current trends
Business, Data and Communication
Current trends
From Transaction Economics to Relationship Economics
The Customer Data Revolution: Shift in Customer Expectations
Implications: From CRM to CMR (Customer Managed Relationships)
Applications to business: Marketing 2.0
Why predictive analytics: Relevance
How to do it: PHAME
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