The Customer Data Revolution Andreas S. Weigend, PhD Stanford University

1. SPECIESNoordwijk, 03 February 2009 The Customer Data RevolutionAndreas S. Weigend, PhDStanford University

2. This is superseded by Canalys

3. SPECIESS • SPECIESS: What do we do? • Service • Partner • Ecosystem • Conference to • Increase • End-customer • Sales and • Satisfaction • What would Amazon.com do? • What do I do? • Teach, Consult, Speak • People and Data • Marketing 2.0: People have changed! • Impact of changing economics of communication: Individuals, society, and business • Attitude to information access has dramatically changed. Contribution • Business: You enable future of work: Single person, fraction of a person WWAD

4. How can you get business insights? • Myth: At the beginning, there is data. And then you create actionable insights • Analysis is good to help us create hypothesis • WWAD? • Measurement and data-centric culture? • Yeah. But more importantly: • Experiment centric culture • Instead: Ideas (“Hypothesis”)  Actions  Experiment  Data • Reverse the direction? • Data  Ideas … Ideas  Data • Iterate (“f-word”) • Institutional learning, not random experiments WWAD

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

6. You want to be customer centric • Yeah, yawn, but what does that really mean? • To support customers in their decision making • Where is your organization? • Mini case: Vodafone Incredulous • [Video] • Metrics gone wrong

7. Customer centricity: Help people make decisions • Cost saving phone trees • “Government award” • WWAD? Play with reversing the direction of information flow • OLD: What can the ISP do for the user? • NEW: What can the customer do for the ISP? • Customers do want to communicate with the company!! • Not just be communicated at! • What are their expectations? • It should be as easy as hitting up their friends, and they should listen! WWAD

8. Conversations • Conversation / Communication • Between whom? Company downcasting Individuals

9. 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%)

10. 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

11. A data revolution, not a software revolution • Mapping companies did not realize that users can add value… • Example: NAVTEQ • acquired by Nokia for USD 8.6B in Oct 2007 • spent USD 300M to reach breakeven • 1000 employees driving with GPS… • … vs100M GPS-enabled Nokia phones alone sold in 2008 • …vs Amazon.com realizing early on that users can add value • E.g., by reviewing books • … vs Google enabling external developers to build services using company’s data • Q: When will airlines, banks etc follow? WWAD

12. We-Business • Myth: Company is in charge • Owns customer • Owns product • Owns brand • WWAD? Customer supported customer support • getsatisfaction.com: “Customer service is the new marketing” • Platform where everybody can contribute • Smart relevance functions use user data and decide what bubbles up • If some reseller is really good, they will soon be on top because people constantly give implicit (clicks) and explicit feedback • All: Reputation systems, incentive to not give positive feedback after positive experiences with suppliers • Opening up • Layers WWAD

13. We-Business

15. Customer Network Value • MYTH: Unique users matter • From newspaper copies sold • Focus on engagement (from pages / browsers / people) • WWAD? What actually matters is • what they do (1st order), and • what they tell others (2nd order) • Strong and weak virality • Communication and connectivity • Incentives • Example: Amazon Share the Love • Phone company case / Recommendations WWAD

16. Marketing 2.0 • OLD: The 4 Ps of marketing • Product • Placement • Pricing • Promotion • NEW: Ecosystem Marketing • Design interactions with and between your customers empowering them to create value • For themselves • For other customers, and thus: • For your firm • Other terms • Feedback Marketing • Conversational Marketing

17. Where is the conversation? • Call 800-4-SCHWAB • Where are the conversations • With other customers • With customer service • With “the brand” • WWAD? Design the ecosystem such that the whole is more than the sum of its parts WWAD

18. Reputation. Relevance. Respect. • Reputation is based on sequence of interactions • Reputation is a shortcut in decision making • Relevance • Reputation • Respect • Instrument the world: Design system for interactions Create an ever growing pool of data which creates value for you and your customers

19. My camera and microphone • Adobe Flash installed on approx 1 billion connected computers and mobile devices

22. What really has changed? • Attitude of consumers / customers to information they are creating and sharing • Both about themselves… • Hopes, dreams, and fears • Knowingly and unknowingly • and their relationships • Tagging someone in a picture • Need for Data Strategy • Example: Dopplr • Amazon Wishlist • But just data isn’t all: Focus on experiments • Algorithms have reached ceiling • Data now key differentiator • (incl company learning, richer data to power recs for others)

23. Summary (1 of 2): Me-Business • User focus (“E  Me”) • User is at the center of Web 2.0(not the company) • CMR (Customer Managed Relationships) or VRM (Vendor Relationship Management) (not CRM, Customer Relationship Management) • System engineered for feedback (“Instrument the world”) • System engineered to improve over time by leveraging user data (not deteriorating over time) • Network effects (“Viral marketing”) • Demand-side economies of scale (not only supply-side economies of scale) • Data strategy (“Users create value”) • Google Maps: Make it easy for outsider to use and enrich the data (not increase security) • Why? Spreading memes and genes • Belonging, immortality, self-interest (e.g., file sharing sites)

24. Summary (2 of 2): The Customer Data Revolution • 1. Sniffing the digital exhaust • Mainly implicit data, some explicit data • What is new? More data sources, esp. location data • 100GB per person on the planet • 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

25. Discussion • What are the implications for you? • Do experiments: Measure interactions in an instrumented world • Example: MrTweet • Long-term focus: relationship focus Relation-ships Inter-actions Trans-action

26. Thanks • Thanks to: • Kimmo Alkio • AnttiReijonen • Mari Huhtiniemi • … and all of my friends in the F-Secure Family! • Plus: • Ted Shelton (The Conversation Group) • Mingyeow Ng (just follow MrTweet on twitter!) • More information: www.weigend.com