Marketing Gold:
the potential of data
Tony Hirst
Dept of Communication and Systems,
The Open University
Data today…
• Accountability and transparency
• Resource allocation
• (Service improvement)
• Context of
– Funding (accoun...
two flavours of
data
“Stats”
KPIs
Vanilla reports
(PDF docs)
KPIs
• Access and facilities (i.e # Average number of libraries per 1000
inhabitants)
• Collection (i.e # Average number o...
Blah
Blah blah blah blah, blah blah blah blah, blah,
blah blah, blah blah, blah blah, blah.
HhhhhhhHHHhhhhhuuuuuuuuummmmmm...
via Dave Pattern
@daveyp
“Raw” data
Transaction data
Attention data
Usage data
“Raw” data
(Spreadsheets)
((Linked Data))
Change behaviour based on error data
“Negative feedback, closed loop
control system”
BOTH sorts of data…
…can be used to make decisions
…can be “Actionable”
Who do you think
your competitors are,
and on what are they
competing?
How do
you
know?
Who do your
“customers” think your
competitors are, and
what do they think they
are competing on?
How do
you
know?
“Libraries are places
that minds like to be”
Starbucks/Café Nero
(Blockbuster), Lovefilm,
YouTube
Amazon, Audible
Google (search, scholar, books)
Facebook, Twitter
As far as Google is concerned,
your website is just largely
unstructured DATA
OU Library: College of Law referrals
Aggregated/averaged data may mislead
Means sometimes are(n’t)…
Segregation (i.e. segmentation) can be
a Good Thing
Data contains explicit and implicit structure
Geo-demographics
Networks, graphs, and trees
Custom search engines
around
“hashtag communities”
Can you cluster your data?
In the academic library,
discovery happens elsewhere
Should you
be an
influential
friend?
Friend of a …
• Friend
• Event
• Topic
• Activity
• Group
Data may contain signals
What data do you have?
• Collection data
• Usage data
• User (geo)demographics
• Occupancy/usage of physical space (and ho...
supervised learning
(desired output for given input)
Input
patterns
Output
Patterns
“recommendation
engine”
Desired output...
People who..
• Borrowed this, borrowed that
• Borrowed this, studied that
• Study this so might borrow that
• Know these p...
Book reserve and collection?
Public open data
data.gov.uk
How might you be able to make use of
other people’s data…
… and how might they be able to make
use of your data?
If a library is a place
to go to find out
about “local stuff”…
…how much do you
know about what
web services out
there, anywhere,
know about your
locale?
Hook-in to networks
• Help information flow
• Amplify, enrich and engage with others
Events: bookshops
Library talks…
…or contextually
amplify signing events
at local bookshops
Events: museums
Provide more information – draw on the
way interests flow through networks
“Maturity Models”
Gartner Maturity Model for Web Analytics
WebTrends DM3:
Digital Marketing Maturity Model
“Maturity model...
http://www.jiscinfonet.ac.uk/bi
blog.ouseful.info
@psychemedia
http://www.flickr.com/photos/psychemedia/ga
lleries/72157624594881902/
http://www.videopong.net/?action=show&qu
ery=playli...
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
Marketing Gold for Libraries - The Data Inside
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Marketing Gold for Libraries - The Data Inside

  1. 1. Marketing Gold: the potential of data Tony Hirst Dept of Communication and Systems, The Open University
  2. 2. Data today… • Accountability and transparency • Resource allocation • (Service improvement) • Context of – Funding (accounts) – Service delivery (stats) – User expectations (surveys)
  3. 3. two flavours of data
  4. 4. “Stats” KPIs Vanilla reports (PDF docs)
  5. 5. KPIs • Access and facilities (i.e # Average number of libraries per 1000 inhabitants) • Collection (i.e # Average number of volumes in public libraries per 1000 literate inhabitants) • Library use and users (i.e # Registered users in higher education libraries as a percentage of number of students) • Library staff (i.e # Average number of employees in public libraries) • Expenditure (i.e $ Expenditure on literature and information per inhabitant in public libraries) • Ellis, S., Heaney, M., Meunier, P., Poll. R. (2009), “Global Library Statistics”, IFLA Journal, Vol. 35 No. 2, pp. 123-130 – Via http://www.smartkpis.com/blog/2010/03/29/performance- measurement-and-kpi-selection-in-the-library-services-sector/ • But really via Google + MY search terms..
  6. 6. Blah Blah blah blah blah, blah blah blah blah, blah, blah blah, blah blah, blah blah, blah. HhhhhhhHHHhhhhhuuuuuuuuummmmmmm. Blah blah blah, blah, blah blah blah, blah, blah, blah, and up by blah, and down by bleurghh, and blah blah, blah blah, blah blah, bah! Whatever…
  7. 7. via Dave Pattern @daveyp
  8. 8. “Raw” data Transaction data Attention data Usage data
  9. 9. “Raw” data (Spreadsheets) ((Linked Data))
  10. 10. Change behaviour based on error data
  11. 11. “Negative feedback, closed loop control system”
  12. 12. BOTH sorts of data… …can be used to make decisions …can be “Actionable”
  13. 13. Who do you think your competitors are, and on what are they competing?
  14. 14. How do you know?
  15. 15. Who do your “customers” think your competitors are, and what do they think they are competing on?
  16. 16. How do you know?
  17. 17. “Libraries are places that minds like to be”
  18. 18. Starbucks/Café Nero (Blockbuster), Lovefilm, YouTube Amazon, Audible Google (search, scholar, books) Facebook, Twitter
  19. 19. As far as Google is concerned, your website is just largely unstructured DATA
  20. 20. OU Library: College of Law referrals
  21. 21. Aggregated/averaged data may mislead
  22. 22. Means sometimes are(n’t)…
  23. 23. Segregation (i.e. segmentation) can be a Good Thing
  24. 24. Data contains explicit and implicit structure
  25. 25. Geo-demographics
  26. 26. Networks, graphs, and trees
  27. 27. Custom search engines around “hashtag communities”
  28. 28. Can you cluster your data?
  29. 29. In the academic library, discovery happens elsewhere
  30. 30. Should you be an influential friend?
  31. 31. Friend of a … • Friend • Event • Topic • Activity • Group
  32. 32. Data may contain signals
  33. 33. What data do you have? • Collection data • Usage data • User (geo)demographics • Occupancy/usage of physical space (and how is the space used?) • What journals are being photocopied? • What books are referred to but not borrowed? • What requests/searches aren’t being fulfilled?
  34. 34. supervised learning (desired output for given input) Input patterns Output Patterns “recommendation engine” Desired output Actual output
  35. 35. People who.. • Borrowed this, borrowed that • Borrowed this, studied that • Study this so might borrow that • Know these people who all borrowed that • Are in this group of people, who tend to borrow the same thing at around the same time, or just before (or after) another group
  36. 36. Book reserve and collection?
  37. 37. Public open data data.gov.uk
  38. 38. How might you be able to make use of other people’s data… … and how might they be able to make use of your data?
  39. 39. If a library is a place to go to find out about “local stuff”…
  40. 40. …how much do you know about what web services out there, anywhere, know about your locale?
  41. 41. Hook-in to networks • Help information flow • Amplify, enrich and engage with others
  42. 42. Events: bookshops Library talks… …or contextually amplify signing events at local bookshops
  43. 43. Events: museums Provide more information – draw on the way interests flow through networks
  44. 44. “Maturity Models” Gartner Maturity Model for Web Analytics WebTrends DM3: Digital Marketing Maturity Model “Maturity models”
  45. 45. http://www.jiscinfonet.ac.uk/bi
  46. 46. blog.ouseful.info @psychemedia
  47. 47. http://www.flickr.com/photos/psychemedia/ga lleries/72157624594881902/ http://www.videopong.net/?action=show&qu ery=playlist&id=106
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