As shown yesterday, the US and Canada have very similar reported expectations for future purchases based on online reviews etc. In fact, in most cases the difference between their reports is under 5%. However, I noticed one interesting trend. It seems Canadians are consistently more likely to report a “Somewhat unlikely” prediction. Confusing? I think so too and suspect that as Americans we are uncomfortable with the terminology “Somewhat unlikely” and therefore avoid selecting that option. At least this is one theory. I suspect this is not significant data, but more “likely” a linguistic issue.
The figure below shows the difference between Canadian and US reporting, in EVERY category, more Canadians reported “Somewhat unlikely”. With the exception of the TRAVEL and ELECTRONICS categories, the “Somewhat unlikely” response (lightest green) differed more than any other.
Difference of Canadian and US Response to forecasted purchases based on online media by category
Conclusion: American’s are somewhat less likely than Canadians to select “Somewhat unlikely” for predictions.
I started looking at the data for The Economist-Nielsen Data Visualization Challenge. It includes survey responses from 30+ countries for questions pertaining to consumer confidence.
Regarding the role of social media and use of internet reviews, the following question was asked in 14 categories: “In the next year, how likely are you to make a purchase based on social media websites/online product reviews for each of the following products/services?” Valid responses were ‘Very likely’,’Somewhat likely’,’Somewhat unlikely’,’Not at all likely’.
The categories are abbreviated in the graphic below which shows that the North American countries surveyed (US and Canada) track very closely in response.
Likelihood of Purchase in Next Year Based on Social Media or Online Review
Stay tuned till tomorrow and I will let you know why I think this data is amusing.
I reworked the previous data using python’s CSV library, which was embarassingly easy and saved much time over dealing with escaped quotes and commas by hand. I also used matplotlib for the first time. For the most part I like it since I can leverage my knowledge of MATLAB plotting. It is also really nice to use list comprehensions to generate labels.
It only took a moment to find the documentation for custom labels (if you already know ‘xticks’ it makes it easy to find!), b is the starting value for each range and the following labels the binned data in the bar graph.
pyplot.xticks(b,[str(bi)+’-‘ +str(bi+10) for bi in b], rotation =30)