Stop making common data reporting mistakes

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July 3, 2019
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January 4, 2020

Stop making common data reporting mistakes

The responsibility of boiling down the complexity into simpler stories is up to Marketing Analysts and, therefore it simply breaks my heart to see all the hard work dented by common and easily avoidable mistakes.

To begin with, let me share the two most common and annoying reporting mistakes of all the time. While we discuss them, keep in mind that they both are easily avoidable. So, in this year 2020, your resolution accomplishment rate should be 100%

Firstly, do not report percentages alone.

“Our innovative and improved digital campaign managed a 1005 increase in sign-ups.” By the issue, the information is almost utterly useless. Did the figures for subscribers increase from 100 to 200? Or from 10,000 to 20,000? Somewhat different situations, correct?

You are making a mistake, when you report percentages by themselves, without any criterions whatsoever. In fact, it proposes you’re missing even a basic grip over the data and the role it plays while making a decision. At worst, it makes you look sneaky, as though you have been trying to twist the data to articulate a more pleasing story.

So, in order to avoid all these disasters, just get married to the percentages with the most relevant raw numbers you can possibly find.

Now coming onto the second recommendation, ensure that the data you are reporting doesn’t have any misaligned altitudes. Wait, I know it is difficult to understand this in one go. Let me elaborate on this by giving you an example. When someone sends you an analysis, it usually contains a table of metrics. The column showing revenue has 12.3M, 3.5M, 145K, 2M, 12K, 674K. Almost all the numbers present represents a different altitude which means, the person who is reading the report will have to do some extra work to construe the whole data. So, to make it easier, you can show it as 12.3M, 3.5M, 0.15M, 2.0M, 0.01M, 0.67M.

This way everything is aligned at the same altitude, thus making the data simpler to compare. It also reduces the processing load.

Shall we consider another everyday example of misaligned altitude? As far as I know, we all watch YouTube videos from time to time. Considering the given numbers of liked and disliked on a video, we saw 15K people liked a video whereas 826 people disliked it. But if the likes and dislikes were expressed at the same altitudes- 14K and 0.7K- it would have given us a more clearer picture of the reality.

Keep in mind that the way you decide to present data has a big influence, gaining attention to one thing over another. So, the next time you share a campaign summary with your CMO, make sure that the altitudes of the data are well aligned.

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