ASA conference Feb 2013
Upcoming SlideShare
Loading in...5
×
 

ASA conference Feb 2013

on

  • 631 views

My presentation given at the Association of Subscription Agents annual conference, Feb 2013. ...

My presentation given at the Association of Subscription Agents annual conference, Feb 2013.

It was titled Understanding how researchers and practitioners use STM information, but the specific theme was understanding how to design information products and services for researchs and practitioners against a background of information abundance (aka information overload).

Statistics

Views

Total Views
631
Slideshare-icon Views on SlideShare
455
Embed Views
176

Actions

Likes
0
Downloads
13
Comments
0

5 Embeds 176

http://mrkwr.wordpress.com 108
http://www.markwareconsulting.com 56
http://feeds.feedburner.com 10
http://translate.googleusercontent.com 1
http://www.newsblur.com 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    ASA conference Feb 2013 ASA conference Feb 2013 Presentation Transcript

    • Understanding howresearchers 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 andpractitioner 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 lackin-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 recognisedsources (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 complexRIN (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 SciencesRIN (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
    • Whats 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 offlineSource: Nicholas & Clark (2012) Reading in the digital environment. Learned Publishing doi: 10.1087/20120203
    • More granular data onreading 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 notthat all this information will cause us tohave a mental breakdown but that weare not getting enough of theinformation that we need —David Weinberger [my emphasis]
    • Designing products forinfo-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
    • 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 compliancedepositphotos.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 / serviceHarrington & 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
    • @mrkwrmark@markwareconsulting.comwww.markwareconsulting.com