Beyond Counting DownloadsNew Methods for Capturing and Using Online Publication Traffic Introduction

Jake Zarnegar, Silverchair

Breakdown of Paid Circulation Revenue

Institutional SubscribersIndividual Subscribers

97%

3%

Source: 2011 AAP Industry Statistics for Professional and Scholarly (PSP) survey report, released Spring 2013.

Breakdown of Subscriber Use

Institutional AnonymousIdentified Individual

92.9%

7.1%

Source: Compilation of usage data from major STM publishers on the SCM6 platform.

First Law of User Identification:A User That Starts Anonymous Tends to Stay Anonymous

Breakdown of Total Content Use

Completely AnonymousInstitutional AnonymousIdentified Individual

72.6%

25.5%

1.8%

Source: Compilation of usage data from major STM publishers on the SCM6 platform.

Second Law of User Identification:The Conservation of Registration

What Is Being Lost?

“Knowing more about user behavior was another topic of common interest. Societies can feel disconnected from their members and libraries from their patrons as a result of the digital transition. There was a sense from many of our partners that human interaction – and the market intelligence that goes along with it – has been replaced by computers. ”

- Alice Meadows, Wiley: From the Scholarly Kitchen post http://scholarlykitchen.sspnet.org/2013/04/03/librarians-and-societies-and-publishers-oh-my/

“Until you make the unconscious conscious, it will direct your life and you will call it fate.” ― C.G. Jung

Essential Idea: The Construction of

Individual and Group Dossiers for Informed Decision Making

First Law of User Identification (Revised):A User That Starts Anonymous Tends to Stay Anonymous Unless Acted Upon

It’s not this easy:

But it’s not this bad, either:

“We know what you played, searched for, or rated, as well as the time, date, and device. We even track user interactions such as browsing or scrolling behavior.”

“We have more than 40 people hand-tagging TV shows and movies for us.”

“All that data is fed into several algorithms, each optimized for a different purpose.”

- Carlos Gomez-Uribe, VP Product Innovation, Netflixhttp://www.wired.com/underwire/2013/08/qq_netflix-algorithm/ (Wired Magazine, August 2013)

1. Detailed Web Analytics

2. Existing Content Knowledge

3. Informed, Personal Outcomes

“Testing has shown us that the predicted ratings aren’t actually super-useful, while what you’re actually playing is. We know that many ratings are aspirational rather than reflecting your daily activity.”

- Xavier Amatriain, Engineering Director, Netflixhttp://www.wired.com/underwire/2013/08/qq_netflix-algorithm/ (Wired Magazine, August 2013)

3 Threads to Follow Today

1. One Size Does Not Fit All

Each organization should determine what audience information they need to be most effective (what does a “complete” dossier of an individual or group look like?)

2. Don’t Start From Scratch

Every organization has pre-existing data points (that may be under-utilized in their online publication operations)

3. Still Room for Differentiation

Interpretation and use of analytics can result in widely different outcomes from the same data

Thank You!

Jake Zarnegar, Silverchair

jakez@silverchair.com