The document focuses on methods of analyzing visitor data and the importance of balancing quantitative analytics with human intuition. It discusses statistical significance, testing hypotheses, and the use of content analysis to derive conclusions from visitor behavior across different languages and tours. The approach emphasizes iterative testing, such as A/B testing, to inform changes and improve visitor engagement.

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16. What is the context?
Dont forget human intuition.

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98. ANALYSIS: ISSUES TO CONSIDER
● How many data points is statistically
significant?
● What time period is sufficient for analysis
then action?
● All hypotheses are dangerous
● How should we test our theses?
● How to balance #s with politics?

100. ANALYZE: Content
● # of Visitors / tour / language
● # of Visitors / stop / language
● Visitors / tour (floor / tour type)
● Visitors / language
● Most listened vs. least stops
● # of canceled Stops / tour / language
● Detailed activity / tour (canceled vs. completion)
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101. IN-APP A/B TESTING
● Comparing (2) tours (Temp/Perm) at the same site:
○ # Visitors / tour / language
○ # Visitors / stop / language
○ Most vs. least listened stops
What content conclusions can be made when the most vs. least
listened to stops vary significantly by language?

102. ● Time on-the-ground qualitative research with data analytics
analysis
● Draw conclusions from comparisons of qual research + analysis
● Propose changes to test hypotheses (duration, content,
speaker/tone, placement, UI, design, relationship to other content,
etc.)
● An iterative approach: A/B test on a select # of devices
● Collect, analyze, share again!

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