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

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

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

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

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

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

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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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

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”

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

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

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

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

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

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 Web: www.weigend.com Phone: +1 650 906-5906