Takeaways from Andreas Weigend’s The Art Of Social Data
Jul 2012 24

What’s Social Data Revolution?

I am sure most of us understand what social data mean and refer to, how about Social Data Revolution?

According to Andreas Weigend’s 2009 article in Harvard Business Review:

Social Data Revolution is the shift in human communication patterns towards increased personal information sharing and its related implications, made possible by the rise of social networks in early 2000s. This phenomenon has resulted in the accumulation of unprecedented amounts of public data.

 

The Art of Social Data

Less than a fortnight ago, I had the privilege to attend and listen to the man himself, Andreas Weigend on the topic – The Art of Social Data.

“The Art of Social Data” provided great refreshing insights as well as an alternate perspectives to how search campaigns can improve.

Let’s proceed to the key takeaways shall we?

Key Takeaway #1 – Marketing through Social Data / Graph.

Andreas touched on three sets of data:

– Implicit Data
– Explicit Data
– Social Data

Implicit data tells us that the consumer has a job, but not that he is a marketing manager.
Implicit data tells us that the consumer has a house, but did not state his exact address.
Implicit data tells us that the consumer listens to music, but did not single out Maroon 5 as his favorite band.

The latter parts of the 3 sentences above illustrates explicit data – “explicitly” (pardon the pun) telling us the information we really want to know.

For social data, we would need to look at the term social graph or simply – the global mapping of everybody and how they’re related. Both implicit and explicit data can be obtained from search engines and social medias, but to truly market products / services, we would need to look at social data.

Previously the Chief Scientist at Amazon.com, naturally Andreas Wiegend cited the very successful example of Amazon.com. If you ever shopped at Amazon.com, I am sure the images below will ring a bell to you:

Amazon - Buy After Viewing

Amazon - Customers Who Bought This Also Bought

Amazon loves making recommendations. Relevant recommendations had been know to increase sales, but can they be improved further?

Enters Amazon’s “Share The Love” initiative, using social data to recommend products. Simply put social recommendations.

John buys a book, checked out. On checking out, John enters the email addresses of his friends whom he thinks might enjoy the way. By entering the email addresses, John and friends get 10% off their purchases.

Andreas cited AT&T as another example. Instead of using the standard marketing segmentation to market a new phone product, AT&T experimented with connection data instead – who called who. Adoption rate went up by more than 1% in the experiment. 1% is not a lot you will say but consider the previous adoption rate of 0.35% to the increased adoption rate of 1.38? Now that’s quite a healthy increase!

Ponder: Can social graph be utilized to improve search engine marketing campaigns?

 

Key Takeaway #2 – The PHAME Framework

Problem, Hypothesis, Actions, Metrics and Experiment made up the PHAME framework.

So what is the PHAME framework? We start with a Problem, come with creative Hypothesis, then suggest different Actions, defining a rich set Metrics, and then run Experiments to get the best solutions.

You can learn more about the PHAME framework via Andreas’ YouTube videos.

Ponder: The PHAME framework reminded me vividly of A/B or Multi Variant Testing. This can be a framework which can be used (with a little tweak) on landing page optimizations.

 

Misc. Takeaways

1. Context is king; no longer content. | Not only in social but this is true for search as well, with mobile searches soaring at an unbelievable rate.

2. The ABC of Social Marketing – Approval, Belonging and Conversation.

3. The 4Cs of Social Marketing – Content, Context, Connection and Conversation.

4. Interest graph (brought up by an audience on the floor) – Linking of connections based on common interest.

 

More to ponder:

1. Are decisions to be event driven or batch driven?

2. Would social graph or interest graph serves marketing efforts better?

 

I hope this article benefit to you the way Andreas seminar did to me.

Would love to hear your opinion and get into a good discussion!




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