Marketing Gold for Libraries - The Data InsidePresentation Transcript
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 (accounts) Service delivery (stats) User expectations (surveys)
two flavours of data
“Stats”KPIsVanilla reports(PDF docs)
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..
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…
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?
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
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 Event Topic Activity Group Friend of a …
Data may contain signals
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?
Input patterns Output Patterns “recommendation engine” Desired output Actual output supervised learning(desired output for given input)
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
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?
Jon Udell’s elm city project
Hook-in to networks Help information flow Amplify, enrich and engage with others
Library talks… …or contextually amplify signing events at local bookshops Events: bookshops
Provide more information – draw on the way interests flow through networks Events: museums
“Maturity Models” Gartner Maturity Model for Web Analytics “Maturity models” WebTrends DM3: Digital Marketing Maturity Model