© people & data | www. weigend. com Andreas S. Weigend, Ph. D. 韦思岸 教授 SPECIES Noordwijk, 03 February 2009 The Customer Data Revolution Andreas S. Weigend, PhD Stanford University

This is superseded by Canalys

3 © people & data | | | | 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 © people & data | | | | 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 © people & data | | | | Phishing PHAME Problem Hypotheses Action Metrics Experiments

6 © people & data | | | | 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 © people & data | | | | 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 © people & data | | | | Conversation / Communication Between whom? Company Individuals downcasting

9 © people & data | | | | % of Americans say they distrust big companies Only 5% say companies do right by their consumers 14% believe information passed to them by government and advertising 4% of Americans trust their HMOs (managed healthcare) 7% trust pharma 9% trust car manufacturers 12% trust telcos Shoshana Zuboff, July 2008 Q: What are the sources of influence, for recommendations?

10 © people & data | | | | % of people believe information passed to them by friends and family 80% of all consumer decisions are influenced by social recommendations 89% of consumers recommend products or services that they like to others. Social recommendations are… … 9 times as effective as advertising in converting unfavorable or neutral pre- dispositions into positive attitudes … 4 times as effective in influencing consumer to switch brands Context RecipientContent Social recommendations work well because context, content, and targeted individual (recipient) are chosen by a friend

11 © people & data | | | | 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, 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%)

12 © people & data | | | | 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

13 © people & data | | | | 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… … vs 100M 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

14 © people & data | | | | Myth: Company is in charge 1.Owns customer 2.Owns product 3.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

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

18 © people & data | | | | 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

19 © people & data | | | | Call 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

20 © people & data | | | | 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

21 © people & data | | | | Adobe Flash installed on approx 1 billion connected computers and mobile devices

24 © people & data | | | | 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)

25 © people & data | | | | 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)

26 © people & data | | | | 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

27 © people & data | | | | What are the implications for you? Do experiments: Measure interactions in an instrumented world Example: MrTweet Long-term focus: relationship focus Trans- action Inter- actions Relation- ships

28 © people & data | | | | Thanks to: Kimmo Alkio Antti Reijonen 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: