Marketing data visualization consists of taking data from marketing campaigns and displaying the information in an accessible format, such as a chart or graph, so that trends and patterns in the data can be identified.
To present this information to marketing teams or clients, visuals can replace datasets and reports so that the audience can more quickly understand how their campaigns are performing.
For example, charts may be used to compare campaign performance, while graphs can show which marketing approach has been the most effective for the quarter. Today, two of the most popular types of marketing data visualization are:
- Heat maps: color-coded representations of data that are most useful for digital marketers to identify user behaviors and journeys across a website. Heat maps visualize visitor behavior in the form of hot and cold spots, making it easier to identify the most useful web pages, spot landing page trends, improve UI or UX, and measure the effectiveness of digital marketing campaigns on certain pages of your website.
- Funnel charts: these organize several stages of consequential data into an easy-to-comprehend image. Funnel charts easily organize the ways various inputs impact outputs and how they all influence the final results of a campaign. Funnel charts are most commonly used for charting the marketing or sales funnel but are also ideal for visualizing pain points across the buyer’s journey and can be used to help improve your shopping cart workflow.
What are the Main Goals of Data Visualization?
The main goal of data visualization is to extract and translate large amounts of complex information into a visual context, such as a graph or chart, and make this information easier to comprehend or interpret.
Beyond translating data into comprehensive images designed to help drive decision-making, data visualization achieves several pivotal goals, including:
- Continuous goal tracking, or “scorecarding,” lets you know where you stand and how you are tracking toward various business objectives
- Capturing the interest of an audience with less data literacy and finding a common language to discuss complex concepts
- Quickly identifying pertinent data points amongst larger data sets to help you locate and pinpoint useful information in a sea of noise
Data can be difficult to analyze in the form of numbers and spreadsheets. Data visualization transforms complex datasets into digestible representations of this information.
Types of data visualization
The main types of data visualization include charts, graphs, and maps in the form of line charts, bar graphs, tree charts, dual-axis charts, mind maps, funnel charts, and heat maps. While each of these offers a different approach to organizing large quantities of complex information into visuals, all of them are designed to make large datasets easier to present, understand, and interpret.
Examples of the main types of data visualization include:
- Bar graphs: also called a column graph, these offer numerical values expressed in bars or rectangles of equal width. Bar graphs are used to quickly understand differences in quantity without the need to refer to specific numbers or figures.
- Line charts: these types of data visualization involve connecting plotted data points with lines to show trends over time and compare different data points. Line charts are useful whenever you’re continuously tracking data and need to demonstrate trends detected in large datasets visually.
- Dual-axis charts: these types of data visualization are used to show comparisons and offer an easy way to see the relationships or trends between datasets. Dual-axis charts combine visual elements such as those of a bar graph and line chart to compare sets of data accurately and efficiently and without needing to use two separate data visualizations to show trends or draw connections between variables with different units or scales.
What Makes a Good Data Visualization?
A good data visualization is made up of large amounts of intricate and complex data presented in an easily digestible visual format. There are two markers of good data visualization:
- The ability to show complex data connections in a clear and concise way. This means graphs and charts have clear headings and keys, providing simplified analysis of large amounts of complex information.
- The ability to make the audience quickly understand the information and identify outcomes or make predictions based on the data being interpreted. Good data visuals provide an easy-to-comprehend measure so marketing teams can quickly evaluate their progress and move forward with new campaigns or strategies.
An effective, quality data visualization will play a key role in areas such as building trust with stakeholders, organizing and motivating your teams around new campaigns, and simplifying your internal decision-making processes.
Techniques in Data Visualization
Data visualization techniques are the methods in which data is interpreted, consolidated, visualized, and shared with your audience. These techniques can involve creating charts, plotting data points, mapping information, or diagraming workflows or behaviors.
How to Choose a Visualization Technique
Choosing which visualization technique to use depends on the variables and insights that need to be visualized and shared with your audience. While graphs can compare different variables and show trends over the course of time, charts can provide a quick and simple snapshot analysis that saves the reader from having to interpret the data and draw conclusions or make predictions based on the data themselves.
What are the Best Tools/Software for Data Visualization?
The best software for data visualization is easy to use, can handle reports with large data sets, and offers a wide array of visualization styles so you can find the best way to present gatherings to your audience.
Google Sheets, Excel, Tableau, FusionChars, and Infogram are all examples of simple data visualization tools that are capable of translating datasets and different data points into clear and concise visualizations.
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