User Behavior Metrics: Identifying Patterns and Improving Experiences Across Services Angie Thorpe, Digital User Experience Librarian, Library, Indiana University Kokomo

1. USER BEHAVIOR
METRICS
Identifying Patterns and Improving
Experiences Across Services
Angie Thorpe | Digital User Experience Librarian | Indiana University Kokomo | atthorpe@iuk.edu

2. Observations
• Uncover users’ needs
→ Influence behaviors
• Identify purpose of
resource:
• Starting point vs. destination
• Informational vs. evaluative
• Different goals, different
metrics
Image credit: https://www.flickr.com/photos/coolinsights/19483059400/

3. Action plan
5. Implement a disciplined quality program.
4. Integrate measurement tools.
3. Monitor a select number of key metrics.
▪ Key Performance Indicators
2. Connect your objectives with your metrics.
1. Focus on internal.
Source: Moore, D. (2004). Five strategies for creating meaningful performance metrics. Retrieved from
http://www.aboutcustomerservice.info/index.php?pg=articles-meaningful-performance-metrics

4. Select metrics
Website
• Source
• Entrance page
• Page depth
• Time of visit
• Technology
• Exit page
E-Resources
• Page depth
• Time of visit
• Technology
• Session duration
• Search queries

5. Key metrics & objectives
Metrics
• Source
• Entrance page
• Page depth
• Time of visit
• Technology
• Session duration
• Search queries
• Exit page
Objectives
• Information literacy
• Establish undergrad program
• Develop online learning plan
• Remodel library space
• Revise service model
• Strategic human resources
planning
• Plan for changes in
technology

6. Key metrics ↦ objectives
Metrics Objectives
• Information literacy
• Establish undergrad program
• Develop online learning plan
• Remodel library space
• Revise service model
• Strategic human resources
planning
• Plan for changes in
technology
• Source
• Entrance page
• Page depth
• Time of visit
• Technology
• Session duration
• Search queries
• Exit page

7. Applying key metrics to strategic goals
Metrics Objectives
• Information literacy
• Establish undergrad program
• Develop online learning plan
• Remodel library space
• Revise service model
• Strategic human resources
planning
• Plan for changes in
technology
• Source
• Entrance page
• Page depth
• Time of visit
• Technology
• Session duration
• Search queries
• Exit page

8. SPACES
Image credit: https://pixabay.com/en/library-quiet-education-school-979896/

9. Website: Devices
86.0%
9.8%
4.2%
79.5%
17.9%
2.6%
0%
20%
40%
60%
80%
100%
desktop mobile tablet
2014 2015

10. EDS: Devices
97.4%
2.3% 0.2%
86.0%
9.8%
4.2%
79.5%
17.9%
2.6%
0%
20%
40%
60%
80%
100%
desktop mobile tablet
2013 2014 2015

11. Website: Browser
31.1%
28.5%
23.2%
15.6%
0.0% 1.6%
46.4%
22.6%
15.6%
12.3%
2.0%
1.0%
0%
20%
40%
60%
Chrome Internet
Explorer
Firefox Safari Edge Other*
2014 2015
*Includes but is not limited to: Amazon Silk, Android Browser, IE with Chrome Frame, Maxthon, Opera, and Safari (in-app)

12. EDS: Browser
23.7%
41.8%
20.1%
13.9%
0.5%
34.0%
32.5%
18.8%
14.1%
0.6%
50.6%
22.3%
12.2% 11.4%
3.5%
0%
20%
40%
60%
Chrome Internet
Explorer
Firefox Safari Other*
2013 2014 2015
*Includes but is not limited to: Amazon Silk, Android Browser, IE with Chrome Frame, Maxthon, Opera, and Safari (in-app)

13. A BRIEF HISTORY OF TIME
Image credit: https://flic.kr/p/8ys6Hs

14. Website use: Day of week
11.7%
19.2% 19.8%
21.5%
13.6%
7.8%
6.4%
8.0%
18.0%
20.4% 20.4%
18.1%
9.7%
5.5%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Sun Mon Tue Wed Thu Fri Sat
2014 2015

15. Website use: Time of day
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
12:00
AM
2:00
AM
4:00
AM
6:00
AM
8:00
AM
10:00
AM
12:00
PM
2:00
PM
4:00
PM
6:00
PM
8:00
PM
10:00
PM
2014 2015

16. EDS use: Day of week
10.9%
16.0%
20.3% 20.3%
17.1%
8.4%
6.9%
11.7%
19.2%
19.8%
21.5%
13.6%
7.8%
6.4%
10.4%
16.7%
19.4%
21.1%
15.4%
10.9%
6.1%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Sun Mon Tue Wed Thu Fri Sat
2013 2014 2015

17. EDS use: Time of day
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
12:00
AM
2:00
AM
4:00
AM
6:00
AM
8:00
AM
10:00
AM
12:00
PM
2:00
PM
4:00
PM
6:00
PM
8:00
PM
10:00
PM
2014 2015

18. BEYOND THE BEATEN PATH
Image credit: https://flic.kr/p/dHoDFr

19. Website: Starting points
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
2014 2015
*Includes but is not limited to: discovery service, library catalog, link resolver, Facebook, Yellow Pages, and e-resource platforms

20. Website: Landing pages
• Nearly 95% visits start on 3 pages:
• Homepage (90.8%)
• Library Hours and Service Desks (2.8%)
• Library Support Services (1.0%)
• Library homepage is 2nd most popular landing page on
university website
• #1 = University homepage
• People are searching for us, too:
• “library”
• “interlibrary loan”
• “hours”

21. Website: Page depth
46.9%
22.5%
11.5%
7.3%
3.6%
2.2% 1.2% 1.1%
73.9%
12.7%
5.8%
2.5% 1.5% 0.0% 0.0% 0.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
1 2 3 4 5 6 7 8
2014 2015

22. EDS: Page depth
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
1.4%
1.6%
2 6 3 5 7 9 4 8 18 12 10 13 20
2014 2015

23. Website: Exits
• Around 94% visitors leave library website from only 7
pages:
• Homepage (82.3%)
• Library Hours & Service Desks (3.6%)
• Library Faculty & Staff (2.9%)
• Library Resource Tutorials (1.7%)
• Library Instruction Guides and Handouts (1.5%)
• Ask A Librarian (1.1%)
• Library Support Services (1.0%)
• 1 in 5 of these visitors did not begin on library web page

24. MERGING EXPERIENCES
Image credit: "Mosaicr seagull" by The original uploader was J2thawiki at English Wikipedia - Transferred from en.wikipedia to Commons.. Licensed under
CC BY-SA 2.5 via Commons - https://commons.wikimedia.org/wiki/File:Mosaicr_seagull.jpg#/media/File:Mosaicr_seagull.jpg

25. Behavior patterns ↦ Advocacy
• Space
• Collections
• Computers
• Service desk(s)
• Service Model
• Does usage ↦ library hours? (Or vice versa?)
• Communication channels
• Technology
• WiFi
• Browser compatibility
• Mobile devices at circulation (?)

26. Q&A

27. Further reading
Arendt, J., & Wagner, C. (2010). Beyond description: Converting web site usage statistics into concrete site
improvement ideas. Journal of Web Librarianship, 4(1), 37-54. http://dx.doi.org/10.1080/19322900903547414
Clifton, B. (2010). Advanced web metrics with Google analytics, 2nd ed. Indianapolis, IN: Wiley.
Cohen, R., & Thorpe, A. (2015). Discovering user behavior: Applying usage statistics to shape frontline services. Serials
Librarian, 69(1), 29-46. http://dx.doi.org/10.1080/0361526X.2015.1040194
Fagan, J. C. (2014). The suitability of web analytics key performance indicators in the academic library environment.
Journal of Academic Librarianship, 40(1), 25-34. http://dx.doi.org/10.1016/j.acalib.2013.06.005
Janyk, R. (2014). Augmenting discovery data and analytics to enhance library services. Insights, 27(3), 262-268.
http://dx.doi.org/10.1629/2048-7754.166
Marek, K. (2011). Using web analytics in the library. Library Technology Reports, 47(5), 5-54.
http://dx.doi.org/http://dx.doi.org/10.5860/ltr.47n5
Turner, S. J. (2010). Website statistics 2.0: Using Google Analytics to measure library website effectiveness. Technical
Services Quarterly, 27(3), 261-278. http://dx.doi.org/10.1080/07317131003765910