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Understanding how
researchers and practitioners
    use STM information
       Mark Ware @mrkwr
    ASA Conference, 26 Feb 2013
How data analytics and field
  research are transforming our
 understanding of researcher and
practitioner use of STM information
WHAT do we know
     about the ways STM
     information is used?


depositphotos.com
And HOW do we know it?
There may be better
      ways ...
Reading
 studies go
 back decades
 e.g. average
 numbers of readings
 have increased
 ( Tenopir) 


Source: Tenopir, C (2007). What does usage data tell us about our users? Online Information, London
Reading
 studies go
 back decades
 & reading
 behaviour varies
 across disciplines
 ( Tenopir) 

Source: Tenopir, C (2007). What does usage data tell us about our users? Online Information, London
So publishers can still lack
in-depth understanding of:
•  how researchers use content
•  how it integrates with other
  information
•  the context in which content used
•  which articles were used, by whom,
  where and when?
•  or which parts of articles were used?
It may be even worse ...




Percentage of unique visitors that do not come from recognised
sources (known IP ranges, authenticated, or registered)
Geoff Bilder (2009) Brave Adventures: New Publishing Models for the Now World, SSP, Baltimore
Why was this?

•  cost & complexity of finding out
•  intermediation – libraries and agents
•  less value in print world anyway
•  but also, publishers may have thought
  they understood enough
The wider information
                  ecosystem is complex




RIN (2009) Patterns of information use and exchange: case studies of researchers in the life sciences
Case studies can provide a fuller
            understanding of differences
                 between disciplines 

                     Humanities
                                                   Physical Sciences




RIN (2011) Collaborative yet independent: Information practices in the physical sciences
Large-scale surveys can provide
 insight, especially if repeated




 Inger/Gardner: How Readers Discover Content in Scholarly Journals (Renew, 2012)
 http://www.renewtraining.com/How-Readers-Discover-Content-in-Scholarly-Journals-summary-edition.pdf
What's new
•  lots of data!
•  near-real-time data collection
•  mobile devices = personal data
•  point-of-care use & similar
•  Big Data analytics
•  altmetrics – using data to measure impact
•  CRIS and research metrics/evaluation
•  and coming up, distributed annotation
  (Hypothes.is)
Deep log analysis (e.g.
         CIBER) offers one approach
             •  what they actually do (online), not
                    what they say or wish they do. E.g.:
             •  very little time reading in the digital
                    environment
             •  Only 1–3 pages viewed & >50% of all
                    visitors never come back
             •  PDFs downloaded, but saved rather
                    than read offline
Source: Nicholas & Clark (2012) Reading in the digital environment. Learned Publishing doi: 10.1087/20120203
More granular data on
reading history now possible
Eye-tracking testing to
     improve UX
Information overload
              may be a truism ...



depositphotos.com




                      Graph adapted from Gillam et al: The Healthcare Singularity and the Age of
                      Semantic Medicine. Chapter in The Fourth Paradigm (2009)
and a marketing cliché ...




depositphotos.com
Information abundance
      is a fact ... BUT
  What keeps us awake at night is not
that all this information will cause us to
have a mental breakdown but that we
are not getting enough of the
information that we need 

               —David Weinberger [my emphasis]
Designing products for
info-overloaded users
•  Data/Information pyramid: knowing-
  by-reducing 
   •  selective, or filtering out
•  Better filters – filtering forward
   •  surfacing relevant information, at
      the right time, in the right context
ASA conference Feb 2013
Workflow solutions
            •  Combining (filtered) content &
                     software tools, integrated with user
                     work/information environment
            •  Improved certainty and consistency
                     of decision making
            •  Enhanced of productivity
            •  Certainty in terms of compliance
depositphotos.com
Designing workflow
         solutions: contextual enquiry
             •  Combines multiple methods, e.g.
                         •  surveys
                         •  cluster / conjoint analysis
                         •  on-the-job observation
             •  Three minutes method (Thomson)
                         •  25–50 interviews per user
                         •  behaviour 3 mins before/after using the
                                information / service

Harrington & Tjan 2008 Transforming Strategy One Customer at a Time, Harvard Business Review
User segmentation
             •  We ask editors: Do you know the profile of
                     specific users? Who are you targeting? The
                     CHOs? The Male Social Glue influencers?
                     We ask: who is more valuable? Which
                     segment? 
             •  Our audience follows an 80-20 rule: 20% of
                     the audience is of high value to us. 80% cost
                     us more than the revenue they generate,
                     for example, if they watch many long
                     videos. 
Source: Outsell (2010) eMedia Organization Part III: Analytics-Wired Content www.outsellinc.com
User segmentation: goals

•  to identify differentiated segments
•  clear identifiable differences
•  representing real behaviour and/or
  attitudinal differences
•  allowing prediction of behaviour of
  future users
User segmentation: goals
•  to use data to identify differentiated
  segments
•  clear identifiable statistically
  significant differences
•  representing real behaviour and/or
  attitudinal differences
•  allowing statistically valid prediction
  of behaviour of future users
User segmentation:
        approach
•  Large, detailed surveys
•  Factor analysis ➜ correlated,
  differentiating statements
•  Cluster analysis ➜ possible
  segmentations
•  Test potential segmentations by
  interviewing
OvidMD and ClinicalKey




                                    Comprehensive?
                                       Trusted?
                                        Fast?



Source: Wolters Kluwer; Elsevier
What sort of questions might
  we answer (or try to)?
•  What are the different barriers potential
  users face?
•  Who are the potential customers for
  possible new services?
•  How do different market segments value
  different features, and how might these be
  grouped? 
•  What new products / services are missing
  from out portfolios?
Why should we bother?
             •  If your market is experiencing
                    discontinuity
             •  If you lack clear value propositions
             •  If you rely too heavily on channel
                    segmentation
             •  If you sense that you face new
                    customer demands and competition 
Harrington & Tjan 2008 Transforming Strategy One Customer at a Time, Harvard Business Review
Some conclusions
•  Analytics capabilities are now a core
  requirement
•  Opportunities to borrow from B2C
•  As content commoditises, new ways of
  adding value become critical
•  Content / Data are likely to be
  distributed across the web ➜ open for
  new entrants to create new services
@mrkwr
mark@markwareconsulting.com
www.markwareconsulting.com

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ASA conference Feb 2013

  • 1. Understanding how researchers and practitioners use STM information Mark Ware @mrkwr ASA Conference, 26 Feb 2013
  • 2. How data analytics and field research are transforming our understanding of researcher and practitioner use of STM information
  • 3. WHAT do we know about the ways STM information is used? depositphotos.com
  • 4. And HOW do we know it?
  • 5. There may be better ways ...
  • 6. Reading studies go back decades e.g. average numbers of readings have increased ( Tenopir) Source: Tenopir, C (2007). What does usage data tell us about our users? Online Information, London
  • 7. Reading studies go back decades & reading behaviour varies across disciplines ( Tenopir) Source: Tenopir, C (2007). What does usage data tell us about our users? Online Information, London
  • 8. So publishers can still lack in-depth understanding of: •  how researchers use content •  how it integrates with other information •  the context in which content used •  which articles were used, by whom, where and when? •  or which parts of articles were used?
  • 9. It may be even worse ... Percentage of unique visitors that do not come from recognised sources (known IP ranges, authenticated, or registered) Geoff Bilder (2009) Brave Adventures: New Publishing Models for the Now World, SSP, Baltimore
  • 10. Why was this? •  cost & complexity of finding out •  intermediation – libraries and agents •  less value in print world anyway •  but also, publishers may have thought they understood enough
  • 11. The wider information ecosystem is complex RIN (2009) Patterns of information use and exchange: case studies of researchers in the life sciences
  • 12. Case studies can provide a fuller understanding of differences between disciplines Humanities Physical Sciences RIN (2011) Collaborative yet independent: Information practices in the physical sciences
  • 13. Large-scale surveys can provide insight, especially if repeated Inger/Gardner: How Readers Discover Content in Scholarly Journals (Renew, 2012) http://www.renewtraining.com/How-Readers-Discover-Content-in-Scholarly-Journals-summary-edition.pdf
  • 14. What's new •  lots of data! •  near-real-time data collection •  mobile devices = personal data •  point-of-care use & similar •  Big Data analytics •  altmetrics – using data to measure impact •  CRIS and research metrics/evaluation •  and coming up, distributed annotation (Hypothes.is)
  • 15. Deep log analysis (e.g. CIBER) offers one approach •  what they actually do (online), not what they say or wish they do. E.g.: •  very little time reading in the digital environment •  Only 1–3 pages viewed & >50% of all visitors never come back •  PDFs downloaded, but saved rather than read offline Source: Nicholas & Clark (2012) Reading in the digital environment. Learned Publishing doi: 10.1087/20120203
  • 16. More granular data on reading history now possible
  • 18. Information overload may be a truism ... depositphotos.com Graph adapted from Gillam et al: The Healthcare Singularity and the Age of Semantic Medicine. Chapter in The Fourth Paradigm (2009)
  • 19. and a marketing cliché ... depositphotos.com
  • 20. Information abundance is a fact ... BUT What keeps us awake at night is not that all this information will cause us to have a mental breakdown but that we are not getting enough of the information that we need —David Weinberger [my emphasis]
  • 21. Designing products for info-overloaded users •  Data/Information pyramid: knowing- by-reducing •  selective, or filtering out •  Better filters – filtering forward •  surfacing relevant information, at the right time, in the right context
  • 23. Workflow solutions •  Combining (filtered) content & software tools, integrated with user work/information environment •  Improved certainty and consistency of decision making •  Enhanced of productivity •  Certainty in terms of compliance depositphotos.com
  • 24. Designing workflow solutions: contextual enquiry •  Combines multiple methods, e.g. •  surveys •  cluster / conjoint analysis •  on-the-job observation •  Three minutes method (Thomson) •  25–50 interviews per user •  behaviour 3 mins before/after using the information / service Harrington & Tjan 2008 Transforming Strategy One Customer at a Time, Harvard Business Review
  • 25. User segmentation •  We ask editors: Do you know the profile of specific users? Who are you targeting? The CHOs? The Male Social Glue influencers? We ask: who is more valuable? Which segment? •  Our audience follows an 80-20 rule: 20% of the audience is of high value to us. 80% cost us more than the revenue they generate, for example, if they watch many long videos. Source: Outsell (2010) eMedia Organization Part III: Analytics-Wired Content www.outsellinc.com
  • 26. User segmentation: goals •  to identify differentiated segments •  clear identifiable differences •  representing real behaviour and/or attitudinal differences •  allowing prediction of behaviour of future users
  • 27. User segmentation: goals •  to use data to identify differentiated segments •  clear identifiable statistically significant differences •  representing real behaviour and/or attitudinal differences •  allowing statistically valid prediction of behaviour of future users
  • 28. User segmentation: approach •  Large, detailed surveys •  Factor analysis ➜ correlated, differentiating statements •  Cluster analysis ➜ possible segmentations •  Test potential segmentations by interviewing
  • 29. OvidMD and ClinicalKey Comprehensive? Trusted? Fast? Source: Wolters Kluwer; Elsevier
  • 30. What sort of questions might we answer (or try to)? •  What are the different barriers potential users face? •  Who are the potential customers for possible new services? •  How do different market segments value different features, and how might these be grouped? •  What new products / services are missing from out portfolios?
  • 31. Why should we bother? •  If your market is experiencing discontinuity •  If you lack clear value propositions •  If you rely too heavily on channel segmentation •  If you sense that you face new customer demands and competition Harrington & Tjan 2008 Transforming Strategy One Customer at a Time, Harvard Business Review
  • 32. Some conclusions •  Analytics capabilities are now a core requirement •  Opportunities to borrow from B2C •  As content commoditises, new ways of adding value become critical •  Content / Data are likely to be distributed across the web ➜ open for new entrants to create new services