What is data visualization (with examples)?
What are the main goals of data visualization?
The main goals of data visualization are 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 interpreting data into comprehensive images designed to help drive decision-making, data visualization achieves several pivotal goals including:
- Showing trends, highlighting correlations or demonstrating comparisons between data over time, which is then used to drive decision-making and quantify the effectiveness of campaigns
- Identifying market share, also known as “share of voice,” which can then be used to better understand your competition and the effectiveness of campaigns
- Highlight pertinent data points amongst larger data sets to help you locate and pinpoint useful information such as the strengths and weaknesses of marketing campaigns across different channels
Data can be difficult to analyze in the form of numbers and spreadsheets, especially in large sets. With data visualization, complex datasets are transformed into a combination of comprehensible visuals and information.
What are the main 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 heatmaps. While the main types of data visualization each offer 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 graph: also called a column graph, these types of data visualization offer numerical values expressed in bars or rectangles of equal width. Bar graphs are used to expose large changes over time and easily summarize large data sets.
- 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 visually demonstrate trends detected in large datasets over the course of a marketing campaign.
- 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, efficiently and without needing to use two separate data visualizations to show trends or draw connections.
What is marketing data visualization?
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 better 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:
- Heatmaps: these types of data visualization are color-coded representations of data that are most useful for digital marketers to identify user behaviors and behavior flow across a website. Heatmaps visualize visitor behavior in the form of hot and cold spots, making it easier to identify the most useful webpages, 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 types of data visualization organize at least four stages of consequential data into an easy-to-comprehend image. Funnel charts easily organize the ways various inputs impact outputs and how it all influences 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.
Why is data visualization important in marketing?
Data visualization is important in marketing because it helps marketers communicate the meaning behind large amounts of complex data quickly, efficiently and in a way that makes it easily comprehended by stakeholders, clients and other departments.
For marketers, data visualization is also important because it offers a way to quantify the effectiveness of your marketing campaigns while ensuring your marketing budget is spent effectively and going towards the right areas. For example, marketing agencies use data visualization as a way to quantify a client’s return on investment or marketing ROI.
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 a 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 to 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 marketing teams around new campaigns and simplifying your internal decision-making processes.
What are data visualization techniques?
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.
Choosing which visualization technique to use depends on the variables 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 is the best software/tools for data visualization?
The best software for data visualization is easy to use, can handle reports with large data sets, and offer 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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