rws social media 2010 v5 - scaled...

1 Rob Spencer, Social Media and Marketing, Boston, May 2010


contact information Dr. Robin W. Spencer [email protected]

Steve Street

Mark Turrell, Tim Woods

Anne Rogers, Kurt Detloff

Jon Bidwell

Howard Smith

Mike Hatrick, Dave Wootten

Employer

Partner

colleagues sharing data

Many Thanks

Outline

Rob Spencer, Social Media and Marketing, Boston, May 2010 3

How

Who

Why Why are we using social media?

Who is our audience?

How should we proceed?

Bonafides and Context

Rob Spencer, Social Media and Marketing, Boston, May 2010 4 4

•  World’s largest pharmaceutical company

•  $7 billion annual R&D

•  Tightly focused, managed research teams

•  Success depends on mastering the intrinsic complexity, uncertainty, and rapid change of the underlying science*

* Gary Pisano (2006) Science Business

•  Scientist & manager, 29 years in R&D

•  Past 5 years leading innovation initiative

•  Retired, 2nd career developing collaborative intelligence systems me

Outline


How

Who

Why are we using social media ? Why

Why we’re using social media


Our goals ARE large-scale, flexible participation inside a large corporation, for

•  necessities: saving time & money •  opportunities: new products & services •  processes: scaled, efficient decisions & allocation •  matching specific needs with specific knowledge

Our goals ARE NOT •  connecting to “friends” •  marketing retail products (books, music, clothing,...)

Goals affect what we’re looking for


For external marketing, we seek the average.

For internal problem solving, we usually seek specifics.

Goals affect what we’re looking for


We know this works. It’s just another medium, we’re figuring it out.

Social media methods work here too – but with some variations.


Confusion or disagreement about goals is the major reason why projects fail.

Get this straight up-front! Watch out for tambourine people...

Outline


How

Who is our audience ?

Why

Who

Digression for Marketing: a Tom Peters Rant


source: US Census data, at http://www.census.gov/prod/2005pubs/c2kbr-36.pdf http://manyeyes.alphaworks.ibm.com/manyeyes/datasets/

Why are you focusing on Millenials ?

History & Anthropology Lesson

Rob Spencer, Social Media and Marketing, Boston, May 2010 12 sources: employed scientists and engineers with PhD degrees, 2006, National Science Foundation,

http://www.nsf.gov/statistics/; M Gladwell, Outliers

+ + =

Half of those with advanced degrees in science and engineering are over age 50. They will soon leave faster than they can be replaced. If you need these skills, target them now.

Outline


Why

Who

How How should we proceed, based on huge amounts of data and experience?


The “Idea Farm” is an all-company collaborative innovation and problem-solving system.

We ran 30-50 campaigns every year.

This approach is known to be effective and sustainable.

Pfizer experience


? ?

Diverge-Converge Model

The diverge-converge model is centuries old, proven effective, and scales perfectly with electronic media.

The classic example is the Longitude Act of 1714; see Dava Sobel’s book Longitude


The best projects give participants options and use the right tools at the right time.

Collaboration Technologies

meetings

on line

mobile

http://chubbsocialmedia.imaginatik.com

The Long Tail


There is a Long Tail of large scale collaboration behavior.

It matters because the results are non-obvious, universal, and now predictable.

Chris Anderson’s book tells the story for retail books and music. His observations, conclusions, and advice apply directly to collaborative innovation.

This is Mr Darcy with his long tail.

Classic vs Long Tail Statistics


What’s the average height of people?

What’s the average number of ideas from

our employees?

ideas per person

how

ofte

n it

occu

rs

•  It’s nothing like a bell curve.

•  “Average” makes no sense.

•  Some people enter no ideas, some enter hundreds. The range is huge.

inches of height per person

how

ofte

n it

occu

rs

•  It’s a bell curve.

•  “Average” is a useful description.

•  The range of values is only a factor of 2.

Log-log graphs reveal the tail


linear graph

Log-log plots show the tail clearly.

A straight line on a log-log graph has the form y = Cx-b , a power law.

I graph these like Zipf, with the axes are flipped vs the Pareto convention. Slopes here = b = 1/(α-1)

log-log

Pfizer Idea Farm 2006-2009 20,000 entries from 4000 authors in >200 campaigns

Long Tails are everywhere


•  book and music sales

• word usage

•  internet hits

•  salaries

•  avalanches

• wildfires

M. E. J. Newman (2006) Power laws, Pareto distributions and Zipf’s law, http://arxiv.org/abs/cond-mat/0412004v3

Four Companies, One Phenomenon


Each dataset is a single campaign at a different company.

Lines all have α=3.0


These are huge datasets over 3-4 years and thousands of people each.

These very different businesses show identical behavior patterns.

Cargill and Pfizer

Long Tails in Public Forums


M. E. J. Newman, Power laws, Pareto distributions and Zipf’s law, at http://arxiv.org/abs/cond-mat/0412004v3, 2004.

D. Wilkinson, Strong regularities in online peer production, in Proceedings of the 2008 ACM Conference on E-Commerce, Chicago, IL, July 2008, at http://www.hpl.hp.com/research/scl/papers/regularities/

Long tails (power laws) are the norm for large scale voluntary contributions.

Wikipedia and Digg

The Value of a Contribution


The value* of an entry from a top contributor is the same as that from a rare contributor.

The huge number of singleton authors puts their value far ahead of the top contributors.

* Value was measured for multiple large internal challenges, assigned by rankings of the sponsoring business unit.

Consequence #1


Most of your contributions and value come from the occasional contributors.

from authors of 1 idea

of 2 ideas

of 3

Do not bias your system by quantity or “reputation”; you may exclude your best contributors.

Consequence #2

Rob Spencer, Social Media and Marketing, Boston, May 2010 26 sources: Twitter: Heil and Piskorski, Harvard Business School October 2009; Digg, Wikipedia: Wilkinson, Hewlett-Packard labs; other, this work

What percent of people put in 80% of the content? 80%

Twitter tweets 5%

Digg votes

9%

comments, star votes

21- 28%

Wikipedia edits

46%

corporate ideas

58- 68%

info

rmat

ion

cont

ent

The simpler the task, the less representative the results.

Dirty Secrets of 5 Star Voting


•  it’s biased

•  it’s compressed

•  followers drive to extremes

•  difficulty drives balance

How public opinion forms, Fang Wu and Bernardo A. Huberman, Social Computing Lab, HP Labs, Palo Alto, CA 94304, http://www.hpl.hp.com/research/scl/papers/howopinions/wine.pdf

Is the crowd’s wisdom biased? A quantitative assessment of three online communities, Vassilis Kostakos, at http://arxiv.org/pdf/0909.0237

“...when people observe previous opinions ... they tend to follow the trend. As a result ... extreme views get reinforced and become increasingly more extreme.

“... where expressing a view is costly, like when writing a book review...people will tend to ... offset the current view by presenting a differing one.”

“This analysis has shown that the wisdom of the crowd is indeed biased.”

Analysis of Amazon: 380,000 reviews of 21,000 books by 134,000 people

Amazon average is 4.4 out of 5

People take different roles


Ideators Commenters

Voters

46%

25%

7% 8%

4% 5%

5%

Percents are of people (not entries); combined results from 8700 people, 34,000 entries, 20 campaigns, 7 companies

Lurkers = 2.5x all

others combined


These observations are pervasive and robust, you can’t

change them.

Does it matter?

Do you want to scale up creative collaboration in an

unbiased, representative way?

We can do better !


Know your history, your data, your options

What this talk has tried to convey !

We can do better !



automated community-of-interest perception

real-time analysis of representation

real-time analysis of participation and actions

Great tools make it easy to watch your events and put it in context.

We can do better !



There are many tactics to have your cake (large scale, open collaboration) and eat it too (with balanced representation and low bias).

•  identity control •  quantity control •  length minima •  pay-to-play •  professional moderation •  decision market rating •  rate-the-raters •  pairwise evaluation

Join me for a Think Tank on Collaborative

Evaluation Boston, June, hosted by

Swap business cards, or contact me at [email protected]

Take Home


•  Know your goals. Do you seek the average or the outliers?

•  Adapt the message and medium to your audience. Not the other way ‘round

•  Half your content comes from people who contribute just once. Recognize quality, not quantity.

•  Don’t dumb it down; welcome length and richness. Beware of bias-by-twits.

•  Do better: know your history, your data, and your options. Join me for a workshop.


Questions ?

rws social media 2010 v5 - scaled...

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