Many businesses have survey data somewhere, waiting for better analysis. Using a survey containing ratings from 1 to 10, this analytical view correlates ratings of overall satisfaction, firm expertise, and likelihood to recommend for several customer segments. Each circle represents a segment defined by the combination of industry, job function, gender, and product. Size corresponds to the number of customers in that segment.
source: https://www.tableau.com/solutions/gallery/survey-satisfaction#phblP0m4Ai1Psl8F.99
Explore the dashboard above to answer the following questions:
- What variables are being displayed in the graphs above? What changes occur when choosing a different industry or job function?
- Does there seem to be a correlation between the "Length of Being Customer" and the rating scores for Recommendations, Satisfaction, and Expertise across all industries?
- Which bucket of customers has the largest sample size?
- For which industry do the most outliers exist?
- How does the "Length of Being a Customer" affect the ratings across all industries?
1. Evaluating on how easy to buy, how easy to use and how reliable. The line and dots on the chart changes.
ReplyDelete2. Customers report to be more satisfied and familiar with the products, due to the line shifts.
3. Don’t know how to find that.
4. Maybe retail…
5. Customers report to be more satisfied and familiar with the products, due to the line shifts.
To tell the truth, I can’t really understand how this graph works. After I read your questions and playing with the graph, I still cannot figure out, but I do realize that the creator want to summarize the survey data on the customers’ feedback. It would be great if can provide some hint on how to find outliers. By just clicking on each industry category is not very efficient way to find out, since the design for the “choice bar” is not friendly. The end-user questions have some connection with the visualization, but I think the 2nd and 5th is quite same. The visualization, as your evaluation said, does has many different dimensions for readers to draw conclusion, but it ways too massy and difficult for the layman. However the color and scale of the bubble-size give some kind of thought.
What variables are being displayed in the graphs above? What changes occur when choosing a different industry or job function?
ReplyDeleteLevel of purchasing, level of use, reliability, satisfaction, recommendations, expertise.
The graph changes and pointed to the choice of selection.
Does there seem to be a correlation between the "Length of Being Customer" and the rating scores for Recommendations, Satisfaction, and Expertise across all industries?
Length of being customer control those crosses in all industries.
Which bucket of customers has the most significant sample size?
Looks like its males in the accounting industry.
For which industry do the most outliers exist?
Finance, and service.
How does the "Length of Being a Customer" affect the ratings across all industries?
Length of being customer control the ratings in all industries.
The graphs contain more information than visual explanations. I would need more detail dataset and charts to determine the purpose of what author what to show. The nine graphs are a little abstract, but it is a good experience reading 3x3 graphing charts.
Questions are very critical and give some help on directing reader. I like the last question which points out the interrelation between the growth of lines and scatterplots. Thanks, Sam.
Hmm it appears like your website ate my first comment (it was super long) so I guess I'll just sum it up what I had written and say, I'm thoroughly enjoying your blog. I as well am an aspiring blog blogger but I'm still new to everything. Do you have any points for novice blog writers? I'd certainly appreciate it. cv2 puttext
ReplyDelete