Jan 29, 2023
The rate at which data is presented daily is alarming 😨.
Data has become a gold mine for every business across all industries. Yet, just because you have a means of tracking data doesn't mean that the data can help you.
There is more than just data tracking that needs to be done to convert the raw information into actionable insights to optimize your marketing campaigns (unless you are using Gretel).
A research report revealed that 95% of companies struggle to manage unstructured data. Marketing departments suffer from it too.
Companies don't have the means to process data and convert it into valuable information to make the right decisions. Here's an article explaining the importance of data-driven marketing.
Luckily you can visualize and break down the technical elements of data into a simple message with tools like Gretel📈. This needs to be the first step to being able to analyze marketing data.
Data visualization is the process of breaking down complex sets of data into simple messages. Stakeholders can use it to make development decisions.
95% of companies struggle to manage unstructured data.
Visual presentations based on marketing data make it easier to communicate with non-technical audiences. This will help you to better understand the insights received by Gretel. It's also key for further marketing data analysis. Finally, it will help to analyze patterns, boost sales, and make creative decisions (based on alphaservesp).
When we present visual data, readers can point out patterns, trends, outliers, and critical points. Instead of employing complex analytics, data visualization tools help you maneuver the process.
Creating visual presentations of your marketing data makes it easier for you to communicate with a non-technical audience.
This article outlines different methods marketing professionals care using to visualize data, understand campaigns, and focus marketing efforts ✏️. Let's get started!
Line graphs are one of the most popular ways to analyze and visualize your marketing data. They are some of the simplest and most beneficial charts to serve a broad range of your data needs.
A line graph is great when you want to showcase change over a particular period of time.
On a line graph, the x-axis shows the data range, while the y-axis shows the kind of measurement that you are tracking.
Ensure that every data point is represented using a solid line instead of a dashed or dotted line. Apart from making lines appear solid, use different colors to outline your message.
The graph should make proper use of legends or labels. Ensure your values can be easily measured so you can draw vital conclusions from the data presented.
A line chart is a good example of when to showcase progress over time. This can be calculated from the hours worked and the cost of revenue, which can be used to measure the ROI (return on investment) and its evolution.
A scatter plot, also known as a scattergram, is used when you want to find relationships in your data variables.
The data values are displayed on both the X and Y axes. They showcase similarities between different data points and point out any existing outliers.
When analyzing related data, you can find other elements such as trends and patterns.
When using a scatter plot, marketers need to have several data points for every variable (Check the top 5 data points for marketers).
After plotting your data, you can analyze any points of data correlation that are depicted. When data points move upward, ranging from left to right, there is a positive relationship in the data. If you can't locate a consistent pattern across the data, chances are correlation is minimal.
When using a scatter plot, ensure that you choose metrics that have a robust impact on other values. Whenever you see a pattern in your marketing data, continue to analyze it to find any other relationship.
Keep in mind that trends in your data do not result in causalities.
Given that scatter plots can help identify data correlations, you can use them to investigate problems in your marketing data.
A column chat is one of the most popular ways to visualize technical data. Also known as a column graph, is the simplest option you can use to deliver messages in the form of data.
The chart is used when you want to compare the number of subjects present within a specific category.
The data is outlined on both the X and Y axes, depending on the nature of the metrics you intend to compare. You can also switch the bars depending on the nature of the data you intend to visualize.
When you want to compare multiple data sets using a column chart, you can stack the columns on top of one another to accommodate as much data as possible.
Using a column chart ensures that it is well labeled or colored. Choose a numbered scale that is easy to read and understand when interpreting the message on the chart.
A column chart is the best data visualization model you can use when you want to visualize the difference between various types of data and the kinds of changes that occur within a particular timeframe. For example, if you want to compare the conversion rates of different audiences.
If you want some of the most unique visualization charts, matrix diagrams are the best! They play a significant role when we want to understand the relationship between one set of data and another. It has the capability to compare various groups of data within a comprehensive data category.
With matrix diagrams, you can find out how different groups of data influence one another and interact.
Matrix diagrams are much clearer than other visualization tools, and they allow you to make more accurate decisions that can propel your business to greater heights.
To avoid getting confused, ensure that you assign a symbol to every data group to help you track the right numbers.
The matrix diagram is the best option when you want to detect causation. For example, in a digital marketing campaign. It can identify the root problem, and display many solutions you can put into practice for better results.
This is the best data visualization option when you want to share information with a huge group of people.
For example, in a global presentation showing your marketing analytics. A pie chart resembles a circle that has multiple slices that represent different data components.
It is used to display the components that make up the whole thing under discussion.
The chart works well when your data is broken down into slices and displayed in percentages.
The advantage of pie charts is that you don't need to dig deep inside the data to uncover insights. All the information is displayed at first glance.
When using the chart, ensure that all your data components add up to 100%. Ensure that every data segment is well labeled to avoid confusion during interpretation.
Apart from labeling the chart, ensure that every data slice is highlighted with a different color to make it unique. Limit the number of pieces you outline since it can get congested, making it difficult to interpret.
The chart works well when allocating resources within your marketing campaigns or identifying the conversions from your marketing channels.
Data visualization is an easy process that you can invest in when you want to extract insights from data.
It can help to make key development decisions or support explaining your analytics. But, it can only work if you understand some of the best visualization tools to use. It all depends on the nature of the data you intend to visualize.
The visualization tool you choose depends on the nature of your data and the goal you intend to achieve.
Once you understand the best visualization tool that reflects the nature of your data, you will be better positioned to analyze marketing data. And better break down the complexity of these metrics.
The visualization tools outlined in this article are designed to analyze your data points. Uncovering significant insights required during decision-making, helping you to complement your marketing data analysis techniques.
Here are some of the top digital marketing tools to help you start visualizing your data!
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