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As experiment in social data, we will use ustream today’s class, starting 2pm PDT, allowing people from both within the classroom and from the outside to participate in real time and Read the rest of this entry »

In 2009, more data will be generated by individuals than in the entire history of mankind through 2008. Information overload is more serious than ever. What are the implications for marketing?

Check out this article at http://blogs.harvardbusiness.org/now-new-next/2009/05/the-social-data-revolution.html

by Ray Bradford and Andreas Weigend. Ray Bradford, currently a student at Stanford University’s Graduate School of Business, is taking Data Mining and E-Business (Stats 252)

You’re working on that big project when momentum stalls at 9:06 PM and you find yourself on Facebook staring at the news feed.  You are confronted by a stream of updates from that melodramatic train wreck of a former high school classmate, whose friend request you accepted out of guilt last week.  You couldn’t care less Read the rest of this entry »

On the eve of the launch of Geoffrey Miller’s book “Spent: Sex, Evolution, and Consumer Behavior”, we dicuss the key role of openness, the importance of dialogue, and the true reason for advertising.

Geoffrey, a tenured professor at the University of New Mexico, combines an evolutionary framework with a data driven mindset to create insights about consumers. Openness, perhaps the least studied of the Big Five personality traits, and inherited to a large degree, is often underestimated. As we reflect on how our time as graduate students in Cognitive Psychology at Stanford has shaped us – Geoffrey worked with Roger Shepard and I with David Rumelhart — we discuss how marketers used to force-communicate customers, and got away with ignoring the deeply social characteristics of humans. Furthermore, Geoffrey explains, the traditional goal of advertising was to demonstrate to a few potential buyers the large symbolic significance of a product for the broad masses. Communication now having primarily become C2C, consumer to consumer, and C2W, consumer to world: what is the impact of the Social Data Revolution on advertising and consumer behavior?

This keynote at the Facebook Developer Garage shares insights from Amazon.com that are relevant for app and game developers. The event was organized on February 25, 2009 by kontagent, a San Francisco startup I am advising.

This keynote, given on February 19, 2009 at Predictive Analytics World shows how predictive models can benefit from the Social Data Revolution [pptx | mp3]

Abstract:Technology affords companies unprecedented opportunities to interact with customers and employees. In any of these interactions, data is created. Yet most firms neither capture nor fully utilize those data to impact their bottom line and strengthen relationships with their customers. Product recommendations and behavioral targeting are early examples of leveraging new sources of data to predict customer behavior and preferences. The next iteration of these interactions, for example mobile phones, empowers owners to access richer data and discover new opportunities – with the possible inclusion of location data that enables companies to predict mobility patterns for marketing and planning purposes. Learn from the former Chief Scientist of Amazon.com how to create a comprehensive data strategy through:

• Leveraging the data you’re already collecting, but not using
• Identifying data that you could and should be collecting
• Transforming data to next-generation predictive intelligence

This video, part of the Nokia IDEAS PROJECT, explains how to increase relevance on platforms including Twitter and Facebook

Transcript from video: Data Gathering Allows Platforms to Serve Customers Better
I think the key element of technology will be that we manage to convince people to give us data, which allows the relevance function of the system to serve them better. Don’t expect magical new plumbing. I always say that the 1990’s was the decade that given a set of data, what insights can I get, whereas the 2000’s is the decade that given a problem, what data can I get in order to solve my problem. Read the rest of this entry »

In this heated discussion recorded on a freezing trolley ride at the annual Monitor Talent meeting, Dan Ariely, Alaina Love, and Andreas Weigend debate the irrationalities in customer decision making:

• How can you “instrument the world” (Weigend) to capture these irrationalities via the plethora of digital traces users leave behind (”attention stream”, Weigend)?
• What are the underlying principles to design incentives (”The Contract”, Ariely) such that consumers share with you valuable information about their intentions?  (”Valuable to Whom?!?”, Love). And, to what degree do people actually know what they want?
• How can companies delight customers by helping them discover items they are genuinely interested in?
• And by capturing rich interactions in addition to mere transactions: How can “move upstream” (Weigend) to delight and truly support the decision making process of the customer?

Leave a comment and let us know what you think!

Yesterday alone, Facebook users issued 21 million friend requests. 17 million requests were accepted. So many new connections, and yet they’re all treated the same—what an oversimplification!

All Facebook links are created equal. But links can differ in strength—for example, a close friend versus a casual acquaintance. Links can be in different categories, like your boss versus a random hookup. And links can be asymmetric—Amy may think that Bob is a good friend, yet Bob may not trust Amy at all! The world is not a binary place.

Discovering Discovery: Don’t ask, Do tell

How can we use data to investigate these different properties of links? Today’s social networks do a lousy job of leveraging our existing data. Why do you need to manually confirm my friend request if we’re already calling, IM-ing, and emailing each other all the time? These data sources should be able to make a good guess about the strength and type of our relationship. Why not use existing data sources to propose better default responses?

If we give our networks a richer structure for our links and relationships, we will also be able to discover interesting facts about ourselves. Why is this important? By investigating implicit relations, we can gain insight into our relationships and how they work. For example, I might be surprised to find out that whenever I email my friend John, he always writes me back promptly whereas I always take 10 times longer to respond to him! Armed with this knowledge, I would ask my system to tell me to get my act together and crank out that response if I’m getting too delinquent.

Facebook 1.0 has helped us create an intimate network of our 17,000 friends. Will Facebook 2.0 help us manage them?

Mind the Explicit, Mine the Implicit

What else can data tell us about the quality of our relationships? One way to use data is to figure out differential interest in budding relationships. It’s easy to do this by looking at communications patterns in email, for example—does one person spend hours crafting that perfect email, only to get a reply that took only a few minutes to write? Or has he suddenly acquired a brand new set of favorite books, movies, and music that just happens to match his new love interest? People leave rich traces on the web—we can discover much more about them than the data they explicitly give.

This is only possible if we can look at the user’s history. After all, we can only make inferences about our behavior if we have a past to compare it against. But this introduces new questions: how much would you pay to know how long Monty spent writing you that email? How much would you pay to keep your data private?

Trust Networks

Social networks are also great for learning about trust. Let’s say that I’m thinking of entering in a business deal with you, but I don’t know you too well. Should I trust you?

There’s an easy way to use the power of networks to answer this question. Let’s just look at all of your other connections: do they trust you? We can give people reputation scores by allowing users to rate their interactions with friends. To make the system even more powerful, we could allow users to link their reputations. To illustrate: let’s say I trust my friend Mike so much that I am willing to attach a trust coefficient of 0.9. This implies that if Mike’s rating goes up by 1, I should get a rating boost of 0.9. Conversely, if someone has a bad experience with Mike and downgrades his rating by 1, my rating will also go down by 0.9. Through the power of the community, reputation ratings would spread quickly. (What trust coefficient would you attach to the author of this post?)

Reward Content Generation

One of the best ways to engage users is to get them to understand how every bit of data they contribute will end up benefiting them. In the example of trust networks, people can improve their own reputations by linking themselves with others. In my previous post on communication, I talked about a system where providing feedback on an email’s relevance would directly benefit you in the future. Online social networks need to reward people to provide explicit data, too.

The Facebook Feed was a brilliant idea for surfacing relevant content created by friends. Ideally, the Feed would create a positive feedback loop: good content provided by friends would get high ratings, which would motivate them to post even more good content. However, an early system of allowing users to rate the submissions of their friends was poorly designed—only 21% of users used the feature. On a rainy day, April 15, 2008, Facebook turned off the feedback system. What a step backward! I wish Facebook instead had created a better machine learning system to reward its users to generate and surface good content.

Social networks based on mutually confirmed binary relations was Day One in evolution of social networks. Introducing, richer semantics, more expressive structures including trust coefficients are the beginning of Day Two. What will the second week bring?

In an early morning panel for the annual Fortune Brainstorm Tech, I hosted 3 visionaries, each of whom have deep insights about the evolving relationship between consumers, companies they interact with, and the data the consumers provide via these interactions.

Shoshanna Zuboff (Harvard Business School, and Author of “The Support Economy”), Esther Dyson (EDventures Holdings) Marc Singer (McKinsey & Company) provided a brilliant conversation, and their key ideas are captured below - hit the links below for the full transcript or listen to the complete audio file.

The Social Data Revolution (Andreas Weigend)

We have just undergone 2 data revolutions, the first where companies implicitly capture consumer data implicity to infer intent, and the second where consumers explicity express data about themselves. We are now moving into the age of the consumer data revolution, where people expect something in return for the data they provide to companies

The Support Economy (Shoshanna Zuboff)

The current model for capitalism has completely broken the trust between individuals and companies. In the next episode of capitalism, companies will thrive by having relationships with individuals, supporting them to live their lives the way they want to live them. That means economies of trust, not economies of scale. It means assets distributed around individuals, not concentrated inside organizations. It means values realized through connecting with the unmet needs of the individual, not value created inside the organization with the model of, “We make it, now how do we sell it to you?”

What Motivates Digital Exhibitionism? (Esther Dyson)

“At the attention thing yesterday, I was shocked to see presenters focusing merely on the attention people give to institutions and to products. I think people go online not to mainly give attention, nor to always buy products. They go online to get attention for themselves or for their ideas. And one big question is: are they trying to get attention for themselves, or for some idealized version of themselves?”

Bifocal Strategy For Companies (Marc Singer)

What we’re seeing organizations doing - a combination of a near term view of - “What is it that I can do with the data that is available today? What data are likely to become available over time?” and a longer term view - “What kind of a profile do I want to establish for my customers over time to be useful to them and do that in a way that I’m quite proud to expose to somebody over time?”

Exhibitionism, Sex Drive, DNA, and making yourself Immortal

Esther: I think digital exhibitionism’s like your sex drive, which is to put your DNA all over the place. And this is to put your digital DNA, your memes, your presence, everywhere. And so what you see now, moving from I’m on a single Facebook page, now I have these widgets and other things, so I’m present on other people’s pages.

Andreas:  So we could essentially say that there are several ways of making yourself immortal. One is to spread your DNA, which actually, as a principle, leads to a very different female and male behavior. Another one is to spread your digital DNA.”

Transcript:

Transcript of Panel Discussion

Audio File: