1. Marketing Gold:
the potential of data
Tony Hirst
Dept of Communication and Systems,
The Open University

2. Data today…
• Accountability and transparency
• Resource allocation
• (Service improvement)
• Context of
– Funding (accounts)
– Service delivery (stats)
– User expectations (surveys)

4. two flavours of
data

6. “Stats”
KPIs
Vanilla reports
(PDF docs)

7. 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..

8. 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…

9. via Dave Pattern
@daveyp

10. “Raw” data
Transaction data
Attention data
Usage data

11. “Raw” data
(Spreadsheets)
((Linked Data))

12. Change behaviour based on error data

13. “Negative feedback, closed loop
control system”

14. BOTH sorts of data…
…can be used to make decisions
…can be “Actionable”

16. Who do you think
your competitors are,
and on what are they
competing?

17. How do
you
know?

18. Who do your
“customers” think your
competitors are, and
what do they think they
are competing on?

19. How do
you
know?

20. “Libraries are places
that minds like to be”

21. Starbucks/Café Nero
(Blockbuster), Lovefilm,
YouTube
Amazon, Audible
Google (search, scholar, books)
Facebook, Twitter

28. As far as Google is concerned,
your website is just largely
unstructured DATA

31. OU Library: College of Law referrals

32. Aggregated/averaged data may mislead

33. Means sometimes are(n’t)…

34. Segregation (i.e. segmentation) can be
a Good Thing

37. Data contains explicit and implicit structure

38. Geo-demographics

41. Networks, graphs, and trees

44. Custom search engines
around
“hashtag communities”

46. Can you cluster your data?

49. In the academic library,
discovery happens elsewhere

50. Should you
be an
influential
friend?

51. Friend of a …
• Friend
• Event
• Topic
• Activity
• Group

53. Data may contain signals

56. 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?

57. supervised learning
(desired output for given input)
Input
patterns
Output
Patterns
“recommendation
engine”
Desired output
Actual output

60. 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

62. Book reserve and collection?

63. Public open data
data.gov.uk

66. 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?

67. If a library is a place
to go to find out
about “local stuff”…

68. …how much do you
know about what
web services out
there, anywhere,
know about your
locale?

70. Hook-in to networks
• Help information flow
• Amplify, enrich and engage with others

71. Events: bookshops
Library talks…
…or contextually
amplify signing events
at local bookshops

72. Events: museums
Provide more information – draw on the
way interests flow through networks

75. “Maturity Models”
Gartner Maturity Model for Web Analytics
WebTrends DM3:
Digital Marketing Maturity Model
“Maturity models”

76. http://www.jiscinfonet.ac.uk/bi

77. blog.ouseful.info
@psychemedia

78. http://www.flickr.com/photos/psychemedia/ga
lleries/72157624594881902/
http://www.videopong.net/?action=show&qu
ery=playlist&id=106