This document discusses how quantitative analytics can help drive information architecture (IA) decisions. It provides examples of the types of metrics that can be measured, such as traffic to different sections of a website, and how these metrics can be used to understand user behavior and improve the user experience. Quantitative data is presented as complementing, not replacing, qualitative research methods. The document advocates starting analytics efforts by clearly defining business questions and goals in order to focus measurement efforts and ensure the collected data will provide actionable insights.

1. Information Architecture Analytics
How Data can drive IA Decisions
Oz IA 2010
Samantha Starmer
@samanthastarmer
http://www.flickr.com/photos/pinksherbet/3041510366

2. Remember IAs are like
Robin Hoodhttp://www.flickr.com/photos/magia3e/4330518973/in/pool-explainia

3. http://www.flickr.com/photos/dearbarbz365
http://www.flickr.com/photos/magia3e/4330518973/in/pool-explainia/
Others talking about analytics

4. http://www.flickr.com/photos/dizzygirl/3865507559/
Quick and Dirty Introduction

5. QUANTITATIVE: type of
information based in quantities or
else quantifiable data (objective
properties) —as opposed to
qualitative information which deals
with apparent qualities (subjective
properties).

6. huh?

7. QUANTITATIVE = Measurable
http://www.flickr.com/photos/iliahi/2606645766/

8. can help understand what
people actually do versus what
they say they do...

9. quant vs. qual?

10. 2 great tastes that taste
great together…
http://www.flickr.com/photos/dearba

11. Quant AND Qual
• Do people research online and purchase in
store?
• Are there categories where this happens in
more often?
• Are there different research needs depending
upon customer?
• At category level what are the differences that
are important to getting a customer to buy?
(e.g. Is zoom of differing importance for a
power bar vs. a bike?)

12. Wolf in Sheep’s
clothing
you have probably
already done some
form of
quantitative
analysis
http://www.flickr.com/photos/pierre_tourigny/367078204/

13. Content
types have
a weak
correlation
(avg 20%).
Activities
have a
strong
correlation
(avg 70%).
Our old friend Card sorting

14. Why should I care?

15. Quantitative
analysis can help
you get your way.
http://www.flickr.com/photos/jumerphotography/3393883065

17. REI: Traffic to Find Out Tab
3.3% of total Global Navigation
Of that 3.3%
1. Local REI Events = 49.3%
2. Expert Advice =16%
3. REI Outdoor School =13.3%
4. Volunteering = 7.7%
5. REI Adventures = 5.2%
6. REI & Families = 4.6%
7. REI & Youth = 3.9%

18. Quantitative
analysis can
stop fights
http://www.flickr.com/photos/clover_1/2633241274

20. Quantitative
analysis can help
you make money
http://www.flickr.com/photos/doug_from_the_uk/4763046344

21. Multi-variant Testing

22. Quantitative
analysis can make
you popular
http://www.flickr.com/photos/ajcgn/4625293839

23. Error pages

24. Pathing Analysis
Does your IA fit your users’
mental models?
Top entry pages
Top exit pages
Where people are converting
http://www.flickr.com/photos/sludgeulper/3422227134

25. Customer Satisfaction
Good entry for Marketing
people and IA to start
working together
Longitudinal trends
Just one measure; don’t use
by itself!
http://www.flickr.com/photos/seandreilinger/983222564/

26. Predictive Modeling
Patterns of customer
behavior can allow you to
model relevant
experiences
http://www.flickr.com/photos/carlcoxstudios/4335795974

27. Don’t Measure just to Measure
http://www.flickr.com/photos/deartistzwei/2371025926

28. Before you provide the data, ask
the requestor what is the business
question they are trying to answer.
Then fulfill that need.
http://www.kaushik.net/avinash/#ixzz11eZAg2nM

29. Getting started
1. What?
2. Why?
3. How?
4. What next?
5. What later?

30. Things to think about
1. What is being measured?
2. Why should we measure it?
3. How can we measure it?
4. What should we do to improve the
experience driving this metric?
5. How should we measure and analyze this
metric going forward?

31. 1. What is being measured?
2. Why should we measure it?
3. How can we measure it?
4. What should we do to
improve the experience ?
5. How should we measure and
analyze this going forward?
http://www.flickr.com/photos/seandreilinger/180088193
Bounce Rate

32. Now what?
• Start small
• Many tools free or cheap
• Consider hiring someone who can focus on
analytics – it is a skill set
• Try to avoid bias; even the act of measuring
can be influenced
• Synthesize quantitative analysis with
qualitative (the what and the why)

33. Learn more…
http://www.flickr.com/photos/hndrk/1305751743/

34. Avinash Kaushik:

35. Thank you!
sstarme@rei.com
@samanthastarmer