1 / 16

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. Q: Current bottleneck for you in your business? (Scarce vs abundant)? Historical perspective Business, Data and Communication Current trends

tiana
Download Presentation

The Unrealized Power of Data Andreas Weigend people & data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Predictive Analytics WorldSan Francisco, February 19, 2009 The Unrealized Power of DataAndreas Weigendpeople & data

  2. 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

  3. 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

  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.

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

  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 • 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 • 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 (1.35%) (0.83%) (0.28%) (0.11%)

  13. 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

  14. 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

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

  16. 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

More Related