What Makes an Effective Tableau KPI Dashboard?

Business heads often use KPI tracking dashboards that provide a quick overview of their company’s performance and well-being.

A KPI tracking dashboard collects, groups, organizes, and visualizes the company’s important metrics, either in a horizontal or vertical manner, providing a quick overview of business performance and expected growth.

An effective and visually engaging way of presenting the main figures in a dashboard is to build a KPI belt by combining text, visual cues, and icons. By using KPI dashboards, organizations can access their success indicators in real time and make informed decisions that bring closer to achieving long-term goals.

What is a KPI?

KPIs – key performance indicators are also known as performance metrics, performance ratios or business indicators.

Key Performance Indicator is a measurable value that demonstrates how effectively a company is achieving key business objectives.

Sales tracking dashboard gives a complete visual overview of the company’s sales performance year wise or quarter wise or month wise, including the number of new leads and the value of deals.

Example of some KPIs on sales dashboard:

  • number of new customers and leads
  • churn rate (how many people stop using the product or service)
  • Revenue growth rate
  • comparison to previous periods
  • most recent transactions
  • QTD (quarter to date) sales
  • Profit rate
  • State wise performance
  • Average revenue for each customer

Bringing It All Together with Dashboards and Stories

An essential element of Tableau’s value is delivered through dashboards. Well-designed dashboards are also visually engaging and draw the user in to play with the information, providing details-on-demand that enable the information consumer to understand what, who, when, where, how, and perhaps even why something has changed.

Best Practices to create a simple and effective dashboard to observe KPIs of sales performance

A well-framed KPI dashboard helps to instantly notice problem areas and tackle the problems.

The greatest value of a modern business dashboard lies in its ability to provide real-time information about a company’s or sale’s performance. As a result, business leaders, as well as project teams, are able to make informed and goal-oriented decisions, acting on actual data instead of gut feeling.

First, the choice of chart types in a dashboard should be correct to show KPIs more effectively.

Bad practices examples in a sales dashboard:

  • A sales report displaying 12 months of history for twenty products, 12 × 20 = 240 data points, does not help the information consumer see the trends and outliers as easily as a time-series chart of the same information.
  • The quality of the data won’t matter if the dashboard takes five minutes to load. 
  • The dashboard fails to convey important information quickly
  • The pie chart has too many slices, and performing precise comparisons of each product sub-category is difficult.
  • The cross-tab at the bottom requires that the user scroll to see all the data.

Now, we will focus on the best practices to create an effective dashboard to convey the most important sales information.

Tableau is designed to supply the appropriate graphics and chart types by default through an option “Show me”.

 I.  Choose the right chart types 

To show the sales performance, we can use the following charts to show the avg. sales, profits, losses and other measures etc.

  • Bar charts to compare numerical data across categories to show sales quantity, sales expense, sales revenue, top products and sales channel etc. This chart represents sales by region.















  • Line charts to illustrate sales trends in data over a period of time to show revenue trend, sales trend etc.

  • Highlight table allows us to apply conditional formatting, a color scheme in either a continuous or stepped array of colors from highest to lowest, to a view.






  • Scatter plots or scatter graphs to Investigating the relationship between different variables or to see the outliers in data. Example: sales vs profit













  • Histograms to see data distribution across groups or to display the shape of sales distribution


Advanced chart types:

  • Bullet graph to track progress in data against the goal or historical sales performance or pre-assigned thresholds to show sales above target or below target etc.
  • Dual-line chart (or dual-axis chart), is an extension of the line chart, allows for more than one measure to be represented with two different axis ranges. Example: revenue vs. expense
  • Pareto chart is most important chart in sales analysis. Pareto principle is also known as 80-20 rule i.e roughly 80% of the effects come from 20% of the causes.In sales analysis, this rule is used for detecting the 80% sales from 20% of the main products.













  • Box plots to display the distribution of data through their quartiles and to see the major outliers in data.















Tableau sales dashboard       

This is a Tableau dashboard made from charts mentioned in above chart types. This is an interactive dashboard where we can see sales information by trend, by region, by profit side and Top products etc.

II.  Use Actions to filter instead of Quick Filters

Using actions in place of Quick Filters provides a number of benefits. First, the dashboard will load more quickly. Using too many Quick Filters or trying to filter a very large dimension set can slow the load time because Tableau must scan the data to build the filters.

More is the number of quick filters, more will be the loading time.

  III.  Build cascading dashboard designs to improve load speed

By creating a series of four-panel, four cascading dashboards the load speed was improved dramatically and the understandability of the information presented was greatly enhanced.

The top-level dashboard provided a summary view, but included filter actions in each of the visualizations that allowed the executive to see data for different regions, products, and sales teams.

IV.  Remove all non-data-ink

Remove any text, lines, or shading that doesn’t provide actionable information.Remove redundant facts. Eliminate anything that doesn’t help the audience understand the story contained in the data.

V.  Create More Descriptive Titles for Each Data Pane

Adding more descriptive data object titles will make it easier for the audience to interpret the dashboard. For example:

  • Bullet Graph—Sales vs. Budget by Product
  • Sparkline—Sales Trend
  • Cross-tab—Summary by Product Type
  • Scatter Plot—Sales vs. Marketing Expense

 VI.  Ensure That Each Worksheet Object Fits Its Entire View

Change the graphs fit from Normal to Entire View so that no scrolling bar is shown in horizontal and vertical way and whole data shown once in a time.

VII.  Adding Dynamic Title Content

There is an option of dynamic content and titles in the dashboards of Tableau. We can have customized it in dynamic way so that on selecting any option from the filter, the title and content will change according to the selecting value. The reason for dynamic title is to express the current content. Example: if the title is “sales 2013” and on selecting year 2014 from filter, the title will have changed to “sales 2014”.

VIII. Trend Lines and Reference Lines

Visualizing granular data sometimes results in random-looking plots. Trend lines help to interpret the data by fitting a straight or curved line that best represents the pattern contained within detailed data plots.

Reference lines help to compare the actual plot against targets or to create statistical analyses of the deviation contained in the plot; or the range of values based on fixed or calculated numbers

 IX. Using Maps to Improve Insight

Seeing the data displayed on a map can provide new insight. If Internet connection is not available, Tableau allows to change to locally-rendered offline maps. If the data includes geographic information, we can create a map visualization in less than five seconds in Tableau.

This map represents sales by state.Red color is representing –ve numbers and green +ve numbers here.


   X.  Developing an Ad Hoc Analysis Environment

Tableau facilitates ad hoc analysis by three ways:

  • Generating new data with forecasts
  • Designing flexible views using parameters
  • Changing or creating designs in Tableau Server

 XI.  Using filters wisely

Filters generally improve performance in Tableau. For example, when using a dimension filter to view only the West region, a query is passed to the underlying data source, resulting in returned information for only that region. We can see the sales performance of the particular region in the dashboard.

By reducing the amount of data returned, performance improves.

 Enhance visualizations using colors, labels etc

      I.  Using colors:

Color is a vital way of understanding and categorizing what we see. We can use color to tell a story about the data, to categorize, order, and display quantity. Color can help us to distinguish between the dimensions.

Bright colors pop at us, and light colors recede into the background. We can use color to focus attention on the most relevant parts of the data visualization. We choose color to highlight some elements over others, and use it to convey a message.

Red is used to denote smaller values, and blue or green is used to denote higher values. Red is often seen as a warning color to show the loss or any –ve number whereas blue or green is seen as a positive result to show the profit and other positive values.

Without colors:


With colors:


II. Using Labels:

We can use mark labels to call out marks of interest or more commonly to label the view to make it more understandable. With the help of data labels, readers of the graph are able to read the exact value of the data point instead of speculating about the values by gauging the heights of bars or sizes of shapes. 

In Tableau, we can turn on mark labels for marks, selected marks, highlighted marks, minimum and maximum values, or only the line ends. We can also turn on mark labels for individual marks.

 Without labels:


With labels:

 Using Tableau to enhance KPI values

The user-friendly interface allows non-technical users to quickly and easily create customized dashboards to provide insight to a broad spectrum of business information. Tableau can connect to nearly any data repository, ranging from MS Excel to Hadoop clusters.

As mentioned above, using colors and labels, we can enhance visualization and enhance KPI values. There are some more ways by which we can enhance the values especially with tableau features.

I. Allow for Interactivity

Playing, exploring, and experimenting with the charts is what keeps users engaged. Interactive dashboards enable the audiences to perform basic analytical tasks such as filtering the views, drilling down and examining underlying data – all with little training.

II.  Custom shapes to show KPIs

Tableau shapes and controls can be found in the marks card to the right of the visualization window. There are plenty of options built into Tableau that can be found in the shape palette.

Custom shapes are very powerful when telling a story with visualizations in dashboards and reports. We can create unlimited shape combinations to show mark points and create custom formatting. Below is an example that how can we represent the sales or profit values with a symbolic presentation.



Here green arrow shows good sales progress and red arrows shows fall in Year over Year Sales by Category

III.   Creating calculated fields

There is an option of creating calculated field in Tableau that establishes the threshold that demarcates success from failure. Calculated fields can be used to create new dimensions such as segments, or new measures such as ratios. 

There are many reasons to use the calculated fields functionality in Tableau. Here are just a few:

  1. Segmentation of data in new ways on the fly
  2. Adding new dimension or measure before making it a permanent field in the underlying data
  3. Filtering out unwanted results for better analyses
  4. Using the power of parameters, putting choice in the hands of end users
  5. Calculating ratios across many different variables in Tableau, saving valuable database processing and storage resources

IV.  Data-Driven Alerts

With version 10.3, Tableau has introduced a very useful feature: Data-Driven Alerts.

We may want to use alerts to notify users or to remind that a certain filter is on and want to be alerted somehow if performance is ever higher or lower than expected. Adding alerts to the dashboards helps reduce the time to insight and elicit action that helps in business that is the primary goal of analytics.

This is an example of a data driven alert that we can set while displaying a dashboard or worksheet.

In Tableau Server dashboard, we can set up automatic mail notifications to a set of recipients when a certain value reaches a specific threshold.


For an enterprise, a dashboard is a visual tool to help team members to track, monitor, and analyze the information about the organization in order to make decisions to support its current and future prosperity.

A key feature of sales dashboards in Tableau is their interactivity. Dashboards are not simply a set of reports on a page; they should tell a story about the business when they are put together. In order to facilitate the decision-making process, interactivity is an important part of assisting the decision-maker to get to the heart of the analysis as quickly as possible.

Author Bio:

This article was contributed by Perceptive Analytics. Neeru Gupta, Chaitanya Sagar, Prudhvi Sai Ram and Saneesh Veetil contributed to this article.Perceptive Analytics provides data analytics, data visualization, business intelligence and reporting services to e-commerce, retail, healthcare and pharmaceutical industries. Our client roster includes Fortune 500 and NYSE listed companies in the USA and India.

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About the Author: Rajeev Pandey

I am a multidisciplinary designer working in data visualization, interaction design and innovation. Expertise in developing Tableau, Web focus based visualization and reporting applications. I have a passion for analyzing, dissecting, and manipulating data sets as well as, building beautiful dashboard. Naturally talented in communicating between technology and business needs. Diverse and experienced in plenty of different domains .I am quick learner who can absorb new ideas and can communicate clearly and effectively.I love creativity and enjoy experimenting with various technologies.

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