Companies rely on Business Intelligence (BI) tools like Tableau to perform high-speed Data Analytics & Visualization. Tableau’s dynamic features enable companies to easily customize user management and gain insightful results. Companies prefer Tableau over other BI tools because of its simplicity and flexibility.
Tableau acts as a self-service analytics platform that allows users to implement its features without having any major technical background. One of such features is the Tableau Heatmap. A Heatmap is a type of Data Visualization that allows users to track and analyze the cursor(user) motion on their websites. This way businesses can understand the user’s likes & dislikes and optimize their Marketing Strategies accordingly.
This article will introduce you to Tableau along with its key features and explain the importance of using Tableau Heatmaps. Moreover, it will provide you with 4 easy steps to set up your Density Heatmaps in Tableau. Read along to understand the steps and limitations of Tableau’s unique form of Data Visualization!
Table of Contents
What is a Heatmap?
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A heatmap (or heat map) is a two-dimensional graphical representation approach that illustrates the magnitude of a phenomena as colour. The colour change may be via hue or intensity, providing the reader with apparent visual indications regarding how the occurrence is clustered or fluctuates across space.
They are crucial in determining what works and what doesn’t on a website or product page.
Heatmaps allow you to analyse your product’s performance and enhance user engagement and retention by experimenting with how various buttons and items are positioned on your website as you prioritise the jobs to be done that increase customer value.
Heatmap vs Highlight Tables
Heatmap is a form of visual representation of data used to represent one or more dimensions against 2 measures. For example, the measures can be color and size.
Whereas, Highlight table is a visual data representation method used to represent one or more dimensions against only one measure i.e, color.
What is Tableau?
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Tableau, launched in 2003 is an advanced software tool that contains robust Data Analytics & Visualization capabilities. This Business Intelligence(BI) tool is used by companies all over the globe to support data-driven decisions. Using Tableau, you can easily analyze your data for valuable insights and create elegant visualizations in the form of Reports, Graphs, Charts, etc. This tool is a great option if you wish to understand and measure the impact of your Marketing Strategies. Furthermore, Tableau’s user interface provides you with a simple organizing solution to analyze and manage your data.
Key Features of Tableau
The following features make Tableau an ideal Business Intelligence tool:
- Tableau supports numerous data source integrations and allows you to easily fetch data from Cloud-based Data Warehouses, External Spreadsheets, and many more sources with ease.
- Tableau has a rich collection of Data Visualization options that you can use according to business requirements.
- Tableau keeps your data safe and its privacy intact.
- Tableau comes in both desktop and mobile versions.
Tableau Heatmap and Its Importance
Tableau Heatmaps are a form of Data Visualization in which colored points on a chart represent user movement on a webpage. These marks show an increase in their density if more users are moving their cursor at that location on the web page. This implies that you can track your web pages and understand the likes and dislikes of your user with just a few clicks.
Heat maps are also useful when you have a huge data set containing overlapping values. Analyzing this data using Heatmaps can help you to recognize areas with greater density and discover insightful data trends. Furthermore, Tableau Heatmaps provides you with great flexibility when it comes to customization. You can even choose a color scheme and decide the size of your density data points seamlessly with Tableau. Depending on the type of analysis you wish to perform, this additional customization can add high value to the output of your visualization.
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Steps to Build a Tableau Heatmap
Now, as you have understood the importance of a Heatmap, this section will provide you with the steps to create your own Tableau Geographic Heat Map. The following steps will create a Density Heatmap in Tableau using a sample Sales dataset:
Step 1: Set Up the Measures for Rows & Columns
Since this section uses a Sales dataset, you can add a Profit Measure to the columns. Go to the list of Measures on the left side and select Profit. Next, navigate to the top bar and select the AVG (average) as the aggregation function for this Measure. This is shown in the below image.
Image Source: data-flair.trainingNext, you should add a Measure to the Rows section. This also requires you to select a Measure (Sales) from the left side list and add it to the Rows section, Moreover, you need to assign an aggregation function, and this blog will use AVG (Average).
Once you have assigned the required Measures, the following graph will be visible on your screen:
Image Source: data-flair.trainingStep 2: Add Dimensions to the Plot
Now on the left side of your screen, go to the Dimensions list, select State, and enter Detail in the box that opens up. As a result, your plot will have a group circle that represents various states on the plot corresponding to the average sales & average profit for each state. This is shown in the image below.
Image Source: data-flair.trainingStep 3: Convert your Plot into a Density Tableau Heatmap
Now, to convert your plot (formed in step 2) into a detailed Density Tableau Heatmap choose Density as the Shape of your plot. This way, the shape of your data points will change from circles to density points. This implies, your data points will follow a density gradient color scheme. Accordingly, regions with the most data points density will have red/orange color while the lesser dense area will be present in greenish-blue shades as shown below.
Image Source: data-flair.trainingTableau also allows you to choose a color scheme for your Heatmaps.
Step 4: Customize your Tableau Heatmap
You can easily customize your Tableau Heatmap by deciding its Colour Scheme, Intensity, Opacity, and Size. Right-click on the Colour card and choose the color scheme most suited for you. Similarly, you can also decide the Border Effects, Intensity, and Opacity of your Heatmap. Furthermore, you have the option to choose the size of Density Spots in your Heatmaps.
Image Source: data-flair.trainingIn this way, you can easily create a Tableau Heatmap. If you wish to extract more details from the data points, hover your cursor on the specific density point.
Limitations of Tableau Heatmap
You have understood the importance of Tableau Heatmaps and the steps to create them. However, Heatmaps are not suitable for every situation and come along with the following limitations:
- The Spatial Smoothing in Heatmaps can develop potentially misleading patterns. These Heatmaps contain density patterns that cover areas with no actual underlying values. Therefore new users should be careful while implementing Smoothning in their Tableau Heatmaps.
- Heatmaps are not suited for working with precise patterns. For instance, you can not use a Heatmap to analyze collision densities on road networks. In such a situation, the Heatmap’s density surface will have a ‘bulge’ in its spatial structure which will provide false information.
- Most web pages today are dynamic in nature. Implementing Heatmaps for such web pages will not work well and the aggregations of such a Heatmap won’t be reliable. Therefore, you can only use Heatmaps with static pages and only for analyzing the big picture.
Conclusion
This article introduced you to Tableau and discussed its key features. It also explained the importance of implementing Tableau Heatmaps. Furthermore, the article explained the 4 steps that you can use to create a Density Heatmap in Tableau. After reading this article, you can go and experiment with the Tableau Heatmaps and analyze your today.
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Share your views on connecting Tableau Heatmaps in the comments section!
Abhinav is a data science enthusiast who loves data analysis and writing technical content. He has authored numerous articles covering a wide array of subjects in data integration and infrastructure.