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© people & data | www.weigend.com Andreas S. Weigend, Ph.D. 韦思岸教授
Predictive Analytics WorldSan Francisco, February 19, 2009
The Unrealized Power of Data
Andreas Weigendpeople & data
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Outline
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
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Business, Data, and Communication
1970’s “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
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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
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My behavior
•IMMI
Listening into your room
every 30 seconds,
for 10 seconds.
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Current trends
Market research
Combine surveys with click data
Assumption heavy Data rich model
Trans-action
Inter-action
s
Relation-ships
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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
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Wishlist
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Outline
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
Customer value
E-Business Me-Business
Who pays whom?
Applications to business: Marketing 2.0
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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”
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Conversations
Conversation / Communication
Between whom?
Company
Individuals
dow
nca
stin
g
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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
1
4.82
2.96
0.4
Non-NN 1-21 NN 1-21 NN 22 NN nottargeted
(0.28%)
(1.35%)
(0.83%)
(0.11%)
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Recommendations 2.0
•People
Friends 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
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Outline
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
Respect
How to do it: PHAME
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You want to be PHAME-ous!
•PHAME
Problem
Hypotheses
Action
Metrics
Experiments
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Summary
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
Web: www.weigend.com
Phone: +1 650 906-5906
