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Quantitative Information Architecture - Oz IA 2010
1. Information Architecture AnalyticsHow Data can drive IA DecisionsOz IA 2010Samantha Starmer@samanthastarmer http://www.flickr.com/photos/pinksherbet/3041510366
2. Remember IAs are like Robin Hood http://www.flickr.com/photos/magia3e/4330518973/in/pool-explainia
4. Quick and Dirty Introduction http://www.flickr.com/photos/dizzygirl/3865507559/
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).
10. 2 great tastes that taste great together… http://www.flickr.com/photos/dearbarbz365
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 clothingyou 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
15. Quantitative analysis can help you get your way. http://www.flickr.com/photos/jumerphotography/3393883065
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17. REI: Traffic to Find Out Tab3.3% of total Global Navigation Of that 3.3% Local REI Events = 49.3% Expert Advice =16% REI Outdoor School =13.3% Volunteering = 7.7% REI Adventures = 5.2% REI & Families = 4.6% REI & Youth = 3.9%
24. Pathing AnalysisDoes your IA fit your users’ mental models?Top entry pagesTop exit pagesWhere people are converting http://www.flickr.com/photos/sludgeulper/3422227134
25. Customer SatisfactionGood entry for Marketing people and IA to start working togetherLongitudinal trendsJust one measure; don’t use by itself! http://www.flickr.com/photos/seandreilinger/983222564/
26. Predictive ModelingPatterns 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
30. Things to think about What is being measured? Why should we measure it? How can we measure it? What should we do to improve the experience driving this metric? How should we measure and analyze this metric going forward?
31. Bounce Rate 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
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)