ASA conference Feb 2013

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?

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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

Source: Tenopir, C (2007). What does usage data tell us about our users? Online Information, London

& reading behaviour varies across disciplines ( Tenopir)

• 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?

So publishers can still lack in-depth understanding of:

It may be even worse ...

Geoff Bilder (2009) Brave Adventures: New Publishing Models for the �Now� World, SSP, Baltimore

Percentage of unique visitors that do not come from recognised sources (known IP ranges, authenticated, or registered)

• cost & complexity of finding out • intermediation – libraries and agents • less value in print world anyway

• but also, publishers may have thought they understood enough

Why was this?

RIN (2009) Patterns of information use and exchange: case studies of researchers in the life sciences

The wider information ecosystem is complex

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

•  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)

What's new

• 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

Deep log analysis (e.g. CIBER) offers one approach

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 ...

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

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and a marketing cliché ...

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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]

• 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

Designing products for info-overloaded users

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

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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

• 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: goals

• Large, detailed surveys • Factor analysis ➜ correlated,

differentiating statements

• Cluster analysis ➜ possible segmentations

• Test potential segmentations by interviewing

User segmentation: approach

OvidMD and ClinicalKey

Comprehensive? Trusted?

Fast?

Source: Wolters Kluwer; Elsevier

• 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?

What sort of questions might we answer (or try to)?

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

• 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

Some conclusions

@mrkwr mark@markwareconsulting.com www.markwareconsulting.com