5 Data Visualization Tips for Better Marketing Reports

Business.com / Sales / Last Modified: February 22, 2017

Marketers need to be able to create compelling marketing reports. Here are some tips to help make your data tell a story.

As a marketer, it’s important to develop reports and updates that can be delivered to coworkers and company executives to keep everyone abreast of what’s happening in your department.

Unfortunately, boring spreadsheets and columns of numbers aren’t very effective storytellers.

The next time you need to create a report, consider utilizing robust data visualizations.

Give these five tips a try:

Don’t let anyone fool you. Creating beautiful, immersive data visualizations isn’t easy. However, thanks to an influx of new reporting tools, it’s entirely possible for any business, regardless of size or budget, to improve their reporting efforts.

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1. Choose the Right Visualization

Regardless of the project or desired outcome, it all starts with choosing the right visualization. Mess this up and you’ll be unable to recover down the road, but nail the decision and your audience will be much more receptive to the information you’re attempting to convey.

“We can’t stress enough the importance of the right choice of data visualizations,” says Agata Kwapien of datapine, a leading provider of data visualization and reporting solutions. “You can ruin everything with a missed chart. It’s important to understand what type of information you want to convey and choose a data visualization that is suited to the task.”

Here’s a quick look at some common types of charts and when they should be used:

  • Line charts. These are used when you want to display patterns of change. Since line charts are fairly common, people automatically understand how to view the data.
  • Pie charts. Another recognizable visualization, pie charts make a beautiful addition to any report. However, it should be noted that people have difficulty accurately comparing the sizes of different pie slices.
  • Scatterplots. When you need to show the relationship between two different quantitative measures, a scatterplot can be used.
  • Distribution charts. One of the preferred choices for showing the distribution of large amounts of data is the distribution chart. It’s easy to consume and can provide general takeaways without a lot of effort from the audience.

These are just a handful examples. There are literally dozens of other charts and visualizations you can use, so choose wisely.

2. Make Sure Each Visualization Answers a Question

Each and every visualization you use should answer a specific question. Unfortunately, this is a rule that very few people actually follow. As a result, their visualizations fail to perform, and they’re left wondering how such a beautiful display could fail at producing the desired results. Any time you begin creating a chart (or before you even choose a chart type, for that matter), ask yourself these three questions:

  • What problem am I attempting to solve/what question am I trying to answer?
  • What’s the overall message or takeaway I want to convey?
  • What metrics are needed in order to answer this question?

By asking these three questions, you should be able to dig deep enough to ensure your visualizations are actually solving specific problems or needs.

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3. Don’t Distort the Data

One of the biggest mistakes you can make when it comes to data visualization is distorting the data. Sometimes this happens on purpose, while other times it’s unintentional. Whether it’s accidently distorting the X- or Y-axis on a bar chart, or purposefully influencing the size of individual slices on a pie chart, nothing good will ever come from distorting the data.

Not only is data distortion unethical, but it also alters the actionability of the data. After all, if people are making important decisions based on the data you’re conveying, you risk leading them to the wrong conclusions. This could ultimately cost you your job, or reputation at the very least.

4. Guide Viewers With Cues and Colors

While you should never distort data, you can and should highlight specific data points. That’s the entire point of creating data visualizations. You’re supposed to transform cold, hard numbers into digestible bits of information, and color is one of your best options. Contrasting colors can be used to differentiate between two sets of data, whereas analogous colors are frequently used to relate different pieces of information.

You should also use directional cues to guide viewers. Things like arrows and lines can be leveraged to show viewers where important information is located and how it relates to other data points on the visualization.

5. Keep It Simple

When it comes to data visualizations, simple is always best. There is no circumstance in which complex visualizations are superior to simple ones. Unfortunately, this doesn’t stop people from trying to make visualizations more convoluted than they have to be.

As you develop visualizations, continually reference the KISS acronym, which stands for “Keep It Simple, Stupid.” Just because you have the ability to use some cool design trick doesn’t mean you should. Stick to the basics and you’ll discover that viewers are much less confused and more engaged.

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Put Your Data to Work

It takes time and effort to identify and gather data. Don’t compromise your investment by creating half-hearted visualizations that fail at conveying the meaning behind the numbers. When you have the right tools at your disposal, creating high-quality data visualizations suddenly becomes simple and worthwhile.

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