One of the more useful ways to graphically see the cumulative effects of something happening in your marketing data is the Pareto, or powerlaw chart. Creating these is easier than it sounds. I’ll assume you have access to spreadsheet software. For the purposes of this tutorial, I’ll use the free Google Docs web-based software.
First, you need data that can be modeled by a Pareto curve. Any time you want to measure the cumulative impact of something over a fixed period of time, you have the opportunity to use a Pareto curve. For example, you can download data about a particular campaign in Google Analytics, download data about how many times an email campaign was opened, download data about how many times a tweet was clicked. These would all be good candidates for Pareto curves.
Let’s take a look at a example tweet. Here’s the bit.ly basic clickthrough data for my tweet about the November 5 Leave Your Bank day.
Now let’s head to our sample spreadsheet. You can access it via this public URL.
You’ll want to set up the date, clicks from Bit.ly, the percentage of total, and the cumulative total columns as shown in the sheet.
Next, create a chart by however your software creates charts. For Google Docs, choose Insert > Chart. Highlight the first and last columns, make the chart as pretty as you like, and you’ve got yourself a Pareto Chart.
Want to add more than one Tweet? Just start another table and add it into the chart’s ranges.
By the way, does anyone think it’s strange that:
- These Pareto charts look nothing like normal powerlaw curves?
- These Pareto charts clearly indicate that the majority of the clickthrough action on these Tweets is not anywhere near the date or time that they were published?
As Tom Webster says, do your own work. In this case, by charting out the clickthrough activity on the 3 most recent blog posts I shared on Twitter, I see that not only is there no best time to tweet for me, but that the real action on a tweet happens long after I publish it, which means that timing of my tweets isn’t nearly as important as some other factor. That means it’s time to go dig deeper into the data and find out why.
I would strongly encourage you to play around with the data you have access to and see what light you can shine on it. There’s a good chance you’ll walk away with more questions than answers, but at least they’ll be better questions, right?
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