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Quantitative Information Architecture - Oz IA 2010
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.
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Quantitative Information Architecture - Oz IA 2010
- 1. Information Architecture Analytics HowData can drive IA Decisions Oz IA 2010 Samantha Starmer @samanthastarmer http://www.flickr.com/photos/pinksherbet/3041510366
- 2. Remember IAs arelike Robin Hoodhttp://www.flickr.com/photos/magia3e/4330518973/in/pool-explainia
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- 5. QUANTITATIVE: type of informationbased in quantities or else quantifiable data (objective properties) —as opposed to qualitative information which deals with apparent qualities (subjective properties).
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- 8. can help understandwhat people actually do versus what they say they do...
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- 10. 2 great tastesthat 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 youhave probably already done some form of quantitative analysis http://www.flickr.com/photos/pierre_tourigny/367078204/
- 13. Content types have a weak correlation (avg20%). Activities have a strong correlation (avg 70%). Our old friend Card sorting
- 14. Why should Icare?
- 15. Quantitative analysis can help youget your way. http://www.flickr.com/photos/jumerphotography/3393883065
- 17. REI: Traffic toFind 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%
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- 20. Quantitative analysis can help youmake money http://www.flickr.com/photos/doug_from_the_uk/4763046344
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- 22. Quantitative analysis can make youpopular http://www.flickr.com/photos/ajcgn/4625293839
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- 24. Pathing Analysis Does yourIA 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 entryfor 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 ofcustomer behavior can allow you to model relevant experiences http://www.flickr.com/photos/carlcoxstudios/4335795974
- 27. Don’t Measure justto Measure http://www.flickr.com/photos/deartistzwei/2371025926
- 28. Before you providethe 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 thinkabout 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 isbeing 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? • Startsmall • 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)
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