Of all the persuasion tools content marketers have in their toolkit, data is one of the strongest.
According to a 2014 study by the Cornell Food and Brand Lab, people were 40% more likely to believe a drug would work if they learned about it through a chart instead of only reading text.
See what I did there? 😉
But data is also tricky.
It’s easy to see patterns where there aren’t any, or to skew the presentation to make your point. And if that happens, even unintentionally, it can undermine the trust you’ve built with your audience.
So, how can smart content marketers learn to use data effectively and responsibly, in a way that builds trust instead of eroding it?
To be clear, learning to notice and correct your own bias in interpreting and presenting data can take a lifetime.
But fortunately, there are guidelines that can keep you on the right track, so you can learn to share persuasive data effectively and responsibly.
Let’s look at four of them.
1. Question the source
If you’re sharing results from a study (or any kind of data gathered by someone else), it’s important to think critically about whether you trust the findings before sharing them with your audience.
More often than not, looking at who conducted a study or gathered data and why can reveal important clues about its credibility.
If you’re citing a study and have access to its full text, look at its methodology and use your common sense — do you think their experiment tested what they set out to test?
Another strategy to help check the credibility of a study is to use a backlink checker to see who else has cited the study, and what they had to say about it.
Gathering more information about your source and reading what others had to say about it — and, more importantly, who said those things — can help you decide whether or not that information can be trusted.
2. Be clear about where data came from
If you’re sharing someone else’s data, this one is easy.
Generally, it’s sufficient to link to the source where you found the data. After all, you’re a content marketer, not a researcher, so there’s no need for MLA style.
But what if you’re sharing data you collected?
Maybe you’re using your own website data or social media data to show the efficacy of different marketing strategies, or you’re sharing your own data from a customer survey.
In this case, it’s important to consider the limitations of your data.
A survey with only 20 responses might reveal something interesting, but it’s difficult to draw hard conclusions from so few answers.
That doesn’t mean you can’t share what you learned from that data; it just means you need to be clear that the information only came from 20 respondents.
3. Remember that correlation doesn’t equal causation
This is the golden rule of data analysis, and one you’ve probably heard before.
The crux of it is: just because two trends seem to be related, it doesn’t mean they are.
That doesn’t mean that correlation is never useful.
Often, correlation can clue you in that something might be happening between two variables. But you have to stay open to the possibility that it’s just a coincidence.
For example, if you get more traffic at a given time of day, there’s likely something happening to influence your traffic at that time. But that correlation doesn’t tell you what.
You won’t know that until you ask more questions.
It’s perfectly valid to cite a correlation in your content marketing, but don’t make the mistake of confirming a connection that may or may not be there.
4. Use good chart etiquette
Charts can be powerful persuasive tools.
When used responsibly, they can make a key point much faster and clearer than trying to explain it with text.
But, they have to be used carefully.
Here are a few good rules of thumb that I’ve learned over the years:
Keep it simple
What am I trying to communicate?
Then, eliminate anything that doesn’t actively help you make your point — including elements that are just redundant.
For example, what point do you think I’m trying to make with this chart?
If you said, “I don’t know,” that’s a valid answer.
There’s some good information in that chart, but you, as a reader, have no idea what I’m trying to communicate with it.
In this next chart, I’ve eliminated the open rate data and the superfluous label, so we can focus on exactly one point: Email 5 had the best click-through rate.
Don’t ask your reader to look for insights — that’s your job.
Present the data so your point is immediately clear.
Beware the scale
Scales are one of the easiest ways for a chart to become misleading.
Your safest bet is to start your Y-axis at zero, since this helps keep everything in perspective.
In this first chart, Email 6 looks awful.
But upon closer inspection …
Hey, that Y-axis starts at 25%!
With the appropriate scale, we can see that, yes, Email 6 did have the lowest open rate, but it wasn’t as terrible as the first scale made it seem.
Use meaningful labels and colors
In a data visualization, every element should have meaning, otherwise it gets in the way of what you’re trying to communicate.
Make sure that when you choose different labels, styles, or colors, they help clarify your point instead of being redundant or useless.
In this first chart, each email is a different color, which is not only hard to look at, but also essentially meaningless.
Yes, there are six different emails, but we already know that because there are six bars — the different colors are redundant.
Instead, use one accent color to draw attention to what you want the reader to focus on.
Everyone needs data literacy
Data used to be the domain of scientists and academics. Not anymore.
Now that everyone has access to more data than ever before, data literacy is no longer a niche concern.
Being able to interpret and use data responsibly and effectively can improve your content marketing, yes.
But also, when you take the extra step to think through the data you read and share, you’re doing your part to improve how our society uses data overall.
So, the next time you find yourself citing a study or creating a graph to use in your content marketing, take some time to think critically about the data and how you’re sharing it.
Asking yourself a few critical questions can help you evolve from a passive data-sharer to a source your audience will trust.