Data visualization is a powerful tool for research and analysis. It can drastically improve the speed and accuracy of your data collection, and create transparency in your results. Data visualizations help you turn numbers into information, then use that higher-level data to make decisions and take action.
What data visualizations are and their uses
Data visualizations are any informational graphic that uses data to represent information. This includes anything from simple bar or line graphs, to complex interactive applications. Some of the most common usages for data visualization include:
- Explaining numerical trends in data over time, such as sales reports or population growth.
- Evaluating current research findings to inform future data collection and analysis
- Highlighting key performance indicators in a company, such as monthly active users on social media, or bounce rate on a website.
- Making difficult or complex processes, such as financial reporting or budget allocation, more understandable to the average person.
Types of data visualizations used for marketing purposes
There are many different types of data visualizations, some of which are more useful for marketers than others. Here are the 5 most common types of data visualization used to gather marketing insights:
1. Bar chart
A bar chart is the most common type of data visualization used in marketing. It is helpful for showing comparisons between items, often over time. For example, you may want to use a bar chart to show monthly product sales on different e-commerce sites, or compare phone models with different operating systems.
2. Line chart
A line chart is similar to a bar chart, but it shows how one item changes over time. For example, you might use a line chart to show sales for five different products over five years or five various marketing campaigns with 5 product variations.
A histogram is a type of bar chart used to show the distribution of data within a specific range. For example, you might use a histogram to highlight different age groups of social media platforms or product categories based on revenue.
4. Pie Chart
A pie chart helps show shares, percentages, and proportions. For example, you could use a pie chart to show different product combinations or marketing channels and their respective revenue shares.
5. Scatter plot
A scatter plot helps show the relationship between two variables. For example, you can use a scatter plot to highlight different product sales over the years compared to different marketing efforts.
Analyzing Marketing Data Using Data Visualizations
The function of data visualization is to use statistical data to create an image that gives the viewer a better understanding of what the data means.
Here are the steps to take when analyzing marketing data using the data visualizations:
Define the goals for your data visualization
Go through your available data and choose which metrics to include in your document. It’s important to use all of your information, but it’s also essential not to overwhelm the viewer with too much information. Try to stick with 5-7 key performance indicators or metrics that can be displayed on one page.
Determine the best tool for your needs
Once you’ve chosen 5-7 metrics to include in your data visualization, you should consider which tools to use for creating the actual visualizations. There are many options on the market, including PowerBI, Tableau Public, Sankey Diagram Generator and Qlikview. Each tool has its strengths and weaknesses; for example, Power BI is great for business users who want to create simple data visualizations on their own. On the other hand, Qlikview offers more complex analytics tools that may be better for experienced users.
Identify what data you need to make your goals a reality.
To succeed, you need all of the necessary data for your data visualization. Once you have the data, choosing the best tool should be relatively easy. To find this data, your next step is to follow it through your business processes. Look for anything that might contain helpful information about the metrics you identified in the previous steps.
Conceptualize how your visualizations will look
Before you begin creating your data visualization, it’s essential to conceptualize how the document will look. These visualizations are compelling only if they allow the viewer to understand what is being presented quickly and easily. Each marketing department may need different types of information included in its blog post, so make sure to include anything that you think might be relevant.
Structure your data for visualization
After you’ve chosen the type of data to include in your visualizations, it’s time to arrange it in a way that makes sense. Don’t let the data you choose leave any pertinent information, especially if it’s related to a metric you won’t display.
Create your data visualization
Now that you’ve chosen a tool, identified what to include in your visualizations, and conceptualized how you think they should look, it’s time actually to create the document. This part of the process is essentially an extension of the conceptualization step. Make sure everything looks as it should and use clear language so that your readers can understand what is being presented.
Select an appropriate data visualization type
Not all data visualizations are made equal. For example, a 5-point Likert scale is more effective than charts when measuring customer satisfaction. Make sure you choose the right data visualization type to meet your goals.
Edit and publish your data visualization
If you’re satisfied with the data visualization you’ve created, then you need to make sure that others can read it as well. This part of the process doesn’t differ much from completing a standard blog post; simply edit to ensure grammar and clarity and post it to your blog.
Choosing the proper data visualization can go a long way when it comes to analyzing marketing data. It is also important to remember that these visualizations are only helpful if they’re used correctly.
Marketing managers often have difficulty deciding how to present their marketing data, but using a 5 point likert scale is one of the best ways to do so.