Life is too short for spending too much of it on marketing data analytics.
Whenever you’re analyzing data, you want to make sure you know where you want to go.
Otherwise, you will likely end up with just another dashboard that you’ll never use.
Here are 4 mistakes you shouldn’t make when dealing with data analytics:
1. Asking Bad Questions
Analyzing marketing data starts by asking the right questions.
And yes there are good and bad questions you can ask your data.
A good question helps you better understand your funnel and your overall business.
The good questions start by understanding the overall impact on your business of whatever you’re measuring.
The good questions should come first.
2. Too Many Charts & Tables
Ben Shneiderman, a computer scientist and specialist of data visualization said:
“Overview first, zoom & filter and details on-demand”.
This basically means why use many charts on your dashboard when you can use just one.
We’re always tempted to make it too complicated.
One great way to simplify your dashboard is by using breakdown dimensions and control filters in your data visualization tool.
3. Not Having a Tracking Plan
Any analytics project should start with a tracking plan.
And your tracking plan should derive from the business questions you want to be answered (see 1).
A tracking plan can be just a simple sheet listing:
- The metrics (what you want to be measured)
- The dimensions (a dimension is usually qualitative and helps you categorize data)
- The sources (where the data will come from)
4. Not Asking The “So-What” Question
The “So-What” question in other words:
“What’s this information’s impact on my business?”
“Is this information actionable in any way?”
In this example, traffic via social today or brand mentions today do not pass the “so-what” question test.
On the other hand, sign-ups via social can answer more critical questions about your business.
5. Bonus Mistake Not To Make
If you are sharing your data analytics report, make sure to include a recommendation and action plan directly.
Even the most well-built dashboard in the world needs interpretation and context.
So if you pulled the data, you might as well, tell your audience what to think about it. :)
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