The contrasting attributes of each quadrant will mean you’ll want to approach each one differently. With your volume metric on the X-axis and your rate metric on the Y-axis, you can create the quadrants by adding lines for the average for each metric (or some other reasonable benchmark). Once you create your scatterplot graph, you can then divide up the scatterplot into four quadrants to form a two-by-two matrix. Wasting time on optimizing a poor-performing landing page that only receives 19 sessions per month most likely isn't going to move the dial.
Depending on how many values are in a particular report, you might need to set a minimum threshold or cutoff based on your volume metric (only pages with more than “x” sessions) because you want to focus on items that when optimized will have a material impact on your business. Regardless of what type of data you’re analyzing, you will want to focus on a volume metric (page views, sessions, users, searches, etc.) and a rate/ratio metric (conversion rate, bounce rate, revenue per session, etc.). Scatter-plot analysis can be useful for any web analytics report where you have a high volume of different results (high cardinality)-keywords, internal search terms, pages, products, etc. For example, a data table can be unwieldy if you’re analyzing your external keywords which have an extremely long tail (thousands of search phrases). However, if you need to dig deeper than the top 50 results, it can be difficult to pinpoint potential areas of focus when you’re staring at a much larger data set. When you’re analyzing and comparing a limited set of values (e.g., browser types, top 25 pages), a tabular report may be all you require. In web analytics, we frequently use data tables to analyze and identify problems or opportunities in our data.
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