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How to analyze churn data and make data-driven decisions
The importance of customer feedback
How to improve customer experience
More Aggregation, More Assumptions of Causality
The same experiment was done, this time they developed bar charts, line charts, and scatterplots with three different levels of aggregation.
This time the type of chart had only a small effect on participants’ ratings of causality. Much more significant, however, was the effect of aggregation level. Across all different visualization styles, people saw aggregated data as more causal than less aggregated data.
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9 reads
MORE IDEAS ON THIS
Line Charts, Circles and Bars 📈📊
An experiment was done to find out whether these styles(bar charts, line charts, scatterplots, and plain text) differ in how "casual" they make the data appear to people.
4 correlations were chosen to display visually. 4 different visualizations for each scenario.
Each participant...
4
13 reads
Correlation isn't causation
According to new research from the Kellogg School, how data are visualized can significantly impact our interpretation of what we’re seeing sometimes for the worse. Our efforts to simplify charts by grouping data into smaller numbers of “buckets” (say, two bars on a chart as opposed to ten) seeme...
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21 reads
How to Visualize Responsibly
Be mindful when you aggregate, because the assumption people have may become a causal one. So think carefully about how you present your data and maybe iterate with your own design team or with your data analysts so people don’t misinterpret what you’re trying to say.
Even the most sophisti...
3
14 reads
Related collections
Other curated ideas on this topic:
Line Charts, Circles and Bars 📈📊
An experiment was done to find out whether these styles(bar charts, line charts, scatterplots, and plain text) differ in how "casual" they make the data appear to people.
4 correlations were chosen to display visually. 4 different visualizations for each scenario.
Each participant...
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