In order to know what to fix, we need to understand the relationships between our metrics. We have so many to choose from. How do we make sense of any of them?
If we don’t understand the relationships between our metrics, we spend time fixing things that aren’t broken or ignoring the things that are.
Building an understanding
How do we simplify? How do we analyze in a coherent way?
Three buckets of analytics
We categorize our metrics in one of these three buckets, then run analyses to understand how each bucket is performing relative to the goal we’re trying to achieve.
Example of Twitter analytics top to bottom
I exported all my Twitter analytics, plus Google Analytics data for Twitter traffic and Twitter-sourced goal completions. Once exported, I put everything in a spreadsheet, then ran a correlation matrix in the free, open-source R software to understand how the variables related.
In my example, I found that my awareness and engagement buckets were fine. I saw no relationship between those two buckets and my action bucket, which is really bad.
Knowing this, I now know what I have to fix. Awareness and engagement are strong, so I keep doing what I’m doing there, but I add more asking, more advertising, more pulling from the audience to boost action.
I won’t go all-in on asking alone; I still have to provide more value than I take. But the data indicates I’m out of balance.
Do the same for all your marketing analytics and metrics to understand what’s working and what isn’t.
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