Digestible data is useful. If it cannot be translated into useful information, then it's of no good. But how can you mine data in the first place? Well, there are some ways — you can opt for the 5 point Likert scale system during your market survey. Regardless of the primary purpose of harvesting data, you need to make it relatable to your audience — and your audience could be internal stakeholders or the customers you're serving.
To make your data relatable, you must have a good grasp of effective data visualization techniques. Whether you're a newbie or an expert in the marketing industry, here is how to effectively visualize data in marketing.
Humans are visual creatures — and it's a good thing. You've got to know how to take advantage of it. To do that, you've got to use compelling visuals to communicate your ideas to your audience. Regardless of your level of expertise in the industry, here are some data visualization techniques to help you get started.
You must know the right visualization to use for the representation of a data set. The wrong visualization tool could confuse and mislead the reader.
There are lots of data out there today. To get the juice, you've got to know how to turn such data into actionable insight. Before using any data visualization tool, here are some questions to ask.
Your primary objective should be to attain some specific goals. If there are lots of irrelevant information on your visuals, then you would likely confuse the reader.
For instance, if you're reporting your Facebook ad campaign performance to some high-class executives, vanity metrics such as likes and reach are irrelevant. You should focus more on actionable metrics like website traffic, return on ad spend, and generated leads.
The right fonts and colors are essential to the aesthetic appeal of your visuals, but it does more than that — good fonts and colors help you to emphasize points, categorize information, distinguish between data points, and illustrate progression.
While making a comparison, use varying gradients of a single color to illustrate continuous data. Furthermore, you can enhance the readability of your visuals by two or three font types. Anything above three font types will likely confuse your audience.
For effective data visualization, you must avoid distorting or falsely representing your data. Your data has to be accurately represented — the colors, shapes, and everything in-between has to communicate the information to your audience.
Your visualization has to be functional, and pretty much broadcast the narrative you have — that's why you've got to give meaning and life to the data.
As a general rule of thumb, you should contextualize your visuals. Contextualization adds clarity and value to your data. It also helps your audience gain value from your data visualization.
Here are some ways of using context to boost your visualization.
If you're like most people, you may feel the need to add some fancy elements to your visuals. Well, perish that thought. If such elements have zero value and don't add much to your visuals, then you are better without them.
In the visualization world, fancy elements that do not add much value are known as decoration. And decorations are something you can do without — it may complicate things by confusing your audience.
To get the most out of your visuals, you've got to create compelling data visuals that are curiosity-provoking, intriguing, and draw your audience into your world — the world of data.
If your data visuals do not offer any sense of discovery, wonder, information, or substance, then you've done no work.
To lessen the distraction your reader may experience, you've got to avoid using lots of decorative fonts, colors, irrelevant colors, or other forms of fancy elements.
In the world of visuals, less is more — always keep it simple and easy to understand.
There is nothing wrong with using manual tools, but manual tools are not efficient, and you are prone to errors if you're using these tools. To get the best out of your visualization, you've got to use automated tools.
Imagine the work that goes into compiling hundreds and thousands of data points, and translating these data points into compelling visuals — that's hard work, and doing it manually won't cut it.
To become more efficient with your work, you've got to leverage automated tools. Automated tools can be used for a wide range of tasks like creating compelling reports, producing marketing and sales materials, and lots of other things.
There is a lot of work that goes into effective data visualization in marketing. You've got to learn good techniques that would help you create compelling visuals. But first, you've got to mine your data — and there are various techniques for mining data. One of which is using 5 points Likert Scale in market research.
Moving on, you've got to represent your data using compelling data visuals. Speaking of compelling data visuals, you've got to go through the six techniques outlined in this guide for effective data visualization in marketing.
Now you know the various data visualization techniques, which of these techniques appeals more to you?