The type of Excel chart you select for your analysis and reporting depends upon the type of data you want to analyze and report and what you want to do with the data:
Visualize data (make sense of data esp. big data)
Classify and categorize data
Find a relationship among data
Understand the composition of data
Understand the distribution of data
Understand the overlapping of data
Determine patterns and trends
Detect outliers and other anomalies in data
Predict future trends
Tell meaningful and engaging stories to decision-makers
What are the most popular Excel charts and graphs types?
Following are the most popular Excel charts and graphs:
#1 Use line charts when you want to show/focus on data trends (uptrend, downtrend, short term trend, sideways trend, long term), especially long term trends (i.e. changes over several months or years) between the values of the data series:
#2 Use line charts when you have too many data points to plot, and the use of a column or bar chart clutters the chart.
#3 Use a line chart instead of a clustered column chart if the order of categories is important:
#4 In a line graph, the x-axis would represent the categories, and the y-axis would represent the measurement values that would be represented periodically.
#5 A line graph should be used when you want to emphasize the changes for values for one variable that are represented on the vertical axis to the other variable represented on the horizontal axis.
#6 Line graphs are better than bar graphs when there are smaller changes.
#7 To read a line graph, first examine the two axes and understand the value points represented on the graph. The second thing is to figure out if there was a rise or fall in the data.
When to use a clustered column chart?
#1 Use a clustered column chart when you want to compare two to four data series. In other words, avoid using column charts if you have just one data series to plot:
Alternatively, avoid creating a column chart with more than four data series.
For example, the following chart contains just five data series, and it has already started looking cluttered:
The chart below contains 11 data series and is very difficult to read and understand:
If you want to create a column chart that contains a lot of data series, then you can try switching ‘row’ and ‘column’ of the chart and see whether it makes any difference:
For example, after switching the row and column of the chart (with 11 data series), it looks like the one below:
Though it still looks cluttered, this chart is much easier to read and understand.
#2 Although the clustered column chart looks like a simple column chart, it is slightly different. A simple column chart is used to represent only a single variable over the other variable, whereas clustered column charts represent multiple data variables.
#3 Use a clustered column chart when the data series you want to compare have the same unit of measurement. So avoid using column charts that compare data series with different units of measurement.
For example, in the chart below, ‘Sales’ and ‘ROI’ have different units of measurement.
The data series ‘Sales’ is of type number. Whereas the data series ‘ROI’ is of type percentage:
#4 Use a clustered column chart when the data series you want to compare are of comparable sizes. So if the values of one data series dwarf the values of the other data series, then do not use the column chart.
For example, in the chart below, the values of the data series ‘Website Traffic’ completely dwarf the values of the data series named ‘Transactions’:
#5 Use a clustered column chart when you want to show the maximum and minimum values of each data series you want to compare.
#6 Use a clustered column chart when you want to focus on short term trends (i.e. changes over days or weeks) and/or the order of categories is not important.
Breaking a clustered column chart
The chart below contains 11 data series and is very difficult to read and understand:
One method of making this chart easier to read and understand is breaking it into several smaller clustered column charts.
For example, you can create one column chart that compares the sales performance of various countries in January.
Create another column chart which just compares the sales performance of various countries in Feb and so on:
The rule of thumb is to avoid presenting too much data in one chart, regardless of the chart type.
A combination chart is simply a combination of two or more charts.
For example, the combination of a column chart with a line chart.
I use combination charts a lot, and I think you must know how to create them as they are very useful.
#1 Use a combination chart when you want to compare two or more data series that have different units of measurement:
#2 Use a combination chart when you want to compare two or more data series that are not of comparable sizes:
#3 Use a combination chart when you want to display different types of data in different ways that can be represented in the same chart. For example, line, bar and column charts can be used on the same chart.
When to use a stacked column chart?
Use a stacked column chart when you want to compare data series along with their composition, and the overall size of each data series is important:
When to use a 100% stacked column chart?
Use a 100% stacked column chart when you want to compare data series along with their composition but the overall size of each data series is not important:
This chart is used to show the percentage of multiple data series in stacked columns.
When to use a stacked area chart?
Use a stacked area chart when you want to show the trend of composition and emphasize the magnitude of change over time.
For example, the following stacked area chart shows the breakdown of website traffic:
When to use a bar chart?
#1 Use a bar chart whenever the axis labels are too long to fit in a column chart:
What are the different types of bar charts?
Horizontal bar charts – Represent the data horizontally. The data categories are shown on the vertical axis, and data values are shown on the horizontal axis.
Vertical bar charts – Also called a column chart. It represents the numerical values represented in the vertical bars. These are used mainly to display age ranges and salary ranges.
Grouped bar charts – Grouped bar charts represent the different time period numbers that belong to a single category.
Stacked bar charts – It is a bar chart that represents the comparisons between categories of data but with the ability to compare and break down the data.
When to use a pie chart?
#1 Use a pie chart to show a 100% composition of data. In other words, the various pie slices you use must add up to 100%. This means do not create a pie chart where the various pie slices do not represent parts of the whole pie.
For example, the following pie chart is not a good representation of data composition as the two pie slices add up to 82% and not 100%:
#2 Use a pie chart to show the composition of data only when you have got one data series and less than five categories to plot.
For example, the following pie chart shows the breakdown of website traffic sources in the last month:
Here I have got only four categories (search traffic, referral traffic, direct traffic, and campaigns) to plot. So a pie chart is ideal for showing the breakdown.
However, if there were more than four categories to plot, like eight or ten categories, the pie chart would have become cluttered and hard to read.
For example, the following pie chart looks cluttered because it has got too many categories:
#3 Pie charts generally express the part to the whole relationship in your data. When your data is represented in ‘percentage’ or ‘part of’, then a pie chart best meets your needs.
#4 Use a pie chart to show data composition only when the pie slices are of comparable sizes. In other words, do not use a pie chart if the size of one pie slice completely dwarfs the size of the other pie slice(s):
#5 Order your pie slices so that as you look clockwise from top to bottom, the biggest pie slice comes first, followed by the second biggest pie slice and so on. This makes the pie chart easy to read:
These pie charts are made from the following data:
To create a pie chart where the biggest pie slice comes first, followed by the second biggest pie slice and so on.
I have sorted the data in decreasing order (from largest to smallest).
Best practices for using pie charts
Limit the number of pie slices
Always make sure to use a minimal number of slices when creating a pie chart. It is really difficult to read a large number of slices.
If you have more than five categories, it is recommended to use a different chart type.
Make sure all data adds up to 100%
Verify that the pie slices are valued at 100% when added up.
Include annotations: Include percentages and labels for your pie chart to make it easy to read.
Pie charts work best for 25%, 50%, 75% and 100%.
Don’t compare multiple pie charts.
Do not use multiple pie charts for comparison as the slice sizes are really difficult to compare side by side.
When to use a number chart?
If you want to visualize just one type of data and it contains a numeric value that does not fall in any range/interval, then use the number chart:
When to use a gauge chart (also known as a speedometer chart)?
If you want to visualize just one type of data and it contains a numeric value that falls in a range/interval, then use the gauge chart (also known as the speedometer chart):
A gauge chart should be used when you want to validate if your data falls in the acceptable range or not.
A gauge chart would generally have the maximum value as default and thresholds as low, medium and high, which indicates if the data is falling within the acceptable range.
Thresholds would be displayed in red, green and yellow for specified values.
When to use a scatter chart?
#1 Consider using a scatter chart when you want to analyze and report the relationship/correlation between two variables:
This chart shows that the relationship between the two variables (‘x’ and ‘y’) is linear. As the value of the variable ‘x’ increases, there is a corresponding increase in the value of the variable ‘y’.
#2 Create a scatter chart only when ten or more data points are on the horizontal axis. The more data points, the better it is for a scatter chart. Conversely, just a few data points (like five or six) are not good enough for creating a scatter chart.
#3 Use a scatter chart when you want to show ‘why’. For example: why revenue is correlated with average order value or why conversion rate is correlated with the number of transactions.
Use a histogram to show frequency distribution for quantitative data.
A histogram represents the visual representation of numerical data that falls within a specified range of values called ‘bins’. It looks exactly like a vertical graph.
Note: You would need to install the ‘Analysis ToolPak’ to create a histogram in Excel.
When to use an ‘actual vs target’ chart?
The chart below shows whether target sales were achieved in each quarter:
This chart is based on the following data table:
The ‘Actual vs target’ chart is a combination chart that requires some formatting.
You can’t insert this chart straightaway into your Excel spreadsheet.
Use this chart when you have got multiple goals, and you want to show progress towards each goal.
When to use a bullet chart?
The chart below shows the performance of sales in Quarter 4:
This chart is based on the following data table:
If the actual sales are between $0 to $240,000, sales performance is considered ‘Poor’.
If the actual sales are between $240,000 to $300,000 ($240,000 + $60,000) then it is considered ‘Fair’.
If the actual sales are between $300,000 to $360,000 ($240,000 + $60,000 + $60,000) then it is considered ‘Good’.
If the actual sales are between $360,000 to $400,000 ($240,000 + $60,000 + $60,000 + $40,000) then it is considered ‘Excellent’.
A bullet chart is a combination chart (though it looks like a single bar chart) that is used to show progress towards a single goal using a range of predefined qualitative and quantitative parameters.
You can’t insert this chart straightaway into your Excel spreadsheet, and it is also quite tricky to create.
If you have multiple goals and want to show progress towards each goal, then use the ‘Actual vs target’ chart.
But if you have only one goal and want to show progress towards this goal (using both qualitative and quantitative data), then use the bullet chart.
A bullet chart can be a vertical bar chart or a horizontal bar chart.
The choice of vertical or horizontal alignment depends on the space available to use for data visualization.
When to use a funnel chart?
Funnel charts visually represent the progressive reduction of data from one phase to another phase.
The first stage is usually referred to as the intake stage.
The chart below shows different stages of the purchase funnel and how users moved from one stage to the next:
This chart is based on the following data table:
As the name suggests, the funnel chart is used for funnel visualization. It is perfect for showing lead funnels and sales funnels.
A funnel chart provides visual pictures of the stages in the process.
For example, let us take an example of an ecommerce business. Funnel charts can be used to understand how many users have added the products to the cart, provided shipping details and completed the purchase.
It also provides us with information about users who have abandoned the cart.
This chart is available in MS Excel (2016 and above). You just need to select your data table and insert the ‘Funnel’ chart.
Funnel charts are mostly used for the sales process and to identify potential problems.
A funnel consists of a higher value, called the head, and the lower part, which is referred to as the neck.
When to use a Venn diagram?
Use a Venn diagram to show the overlapping of data.
The multi-channel conversion visualizer chart used in Google Analytics to visualize multi-channel attribution is a Venn diagram:
In web analytics, we can use a Venn diagram to determine whether or not a website has got attribution problems.
If there is little to no overlap between two or more marketing channels, the website doesn’t have attribution issues.
If there is a good amount of overlap, the website has attribution issues. You should seriously consider taking multi-channel attribution into account while analyzing and interpreting the performance of marketing campaigns.
The tool I have used to create this Venn diagram is Venny.
Note: You can create a Venn diagram in Excel. Check out this tutorial on the Microsoft Office website: Create a Venn diagram.
What are the different types of Venn diagrams?
Two set diagrams – Two circles with overlapping properties. This is the most common and simplest form of the Venn diagram used to compare two metrics or variables.
Three set diagrams – This lets you visualize the relationship between three subjects rather than two variables.
Four set diagram– This comprises four circles with overlapping properties. It is a bit more complex than the normal Venn diagram, and each circle represents the different aspect of the data and their comparison to other variables.
Throughout this article, I have talked about the charts that should be used.
However, there are some charts that should be avoided for reporting purposes unless your target audience is as data-savvy as you.
Following are those charts:
#1 Charts to avoid > Treemap
#2 Charts to avoid > Waterfall chart
#3 Charts to avoid > Radar chart
#4 Charts to avoid > Bubble chart
The reason you should avoid reporting data via these charts to your clients is simple.
Most people have no idea what you are trying to communicate via these charts.
Use these charts only when your target audience is as data-savvy as you.
How to change the chart type in Excel?
MS Excel allows you to change the chart type.
For example, you can convert a clustered column chart into a stacked column chart. Or you can convert a column chart into a bar chart.
For example, let’s convert the following column chart into a bar chart:
Follow the steps below to change the chart type in excel:
Step-1: Open MS Excel and navigate to the spreadsheet which contains the chart you want to edit
Step-2: Select the chart, and then from the ”Design’ tab, click on the ‘Change Chart Type’ button:
You will now see the ‘Change Chart Type’ dialog box like the one below:
Step-3: Click on ‘Bar’ (from the left-hand navigation) and then click on the ‘OK’ button:
Excel will now change your column chart into a bar chart:
What is data visualization in Excel?
Data visualization is the presentation of data (both qualitative and quantitative data) in graphical format.
In Excel, charts and graphs are used to make a visual representation of data.
What are the benefits of data visualization
Through data visualization you can easily:
Visualize data (make sense of data, especially big data)
Classify and categorize data
Find a relationship among data
Understand the composition of data
Understand the distribution of data
Understand the overlapping of data
Determine patterns and trends
Detect outliers and other anomalies in data
Predict future trends
Tell meaningful and engaging stories to decision-makers
Data presentation is a very important skill for an optimizer (marketer, analyst). In fact, it is so valuable that LinkedIn lists it as one of the top skills that can get you hired.
Excel charts are commonly used for data visualization and presentation. But selecting the right Excel chart is always a challenge.
If you use an incorrect Excel chart for your analysis, you may misinterpret data and make the wrong business and marketing decisions.
If you use an incorrect Excel chart for your presentation, stakeholders may misinterpret your charts and take wrong decisions.
Therefore selecting the right Excel chart is critically important.
What are the most common data types that can be visualized?
Following are the most common data types that can be visualized:
#1 Quantitative data (also known as interval/ratio data) is the data that can be measured. For example, ten customers, sales, ROI, weight, etc.
#2 Qualitative data can be classified/categorized but cannot be measured. For example, colors, satisfaction, rankings, etc.
#3 Discrete data – quantitative data with a finite number of values/observations. For example, five customers, 17 points, 12 steps, etc.
#4 Continuous data – quantitative data with value / observation within a range/interval. For example sales in the last year.
#5 Nominal data – qualitative data that cannot be put into a meaningful order (i.e. ranked). For example {Blue, Yellow, Green, Red, Black}
#6 Ordinal data – qualitative data that can be put into a meaningful order (i.e. ranked). For example, {very satisfied, satisfied, unsatisfied, very unsatisfied} or {strong dislike, dislike, neutral, like, strong like}.
The anatomy of an Excel chart
To read an Excel chart, you must understand the various components of the chart.
Consider the following data table in Excel:
This data table has got five variables: ‘Month’, ‘Sales’, ‘Cost’, ‘Profit’, and ‘ROI’:
This data table is made up of categories and data series:
Categories – Here, the first category is ‘Jan’, the second category is ‘Feb’, the third category is ‘Mar’, etc.
Data series – A data series is a set of related data points.
Data points – A data point represents an individual unit of data. 10, 20, 30, 40, etc., are examples of data points. In the context of charts, a data point represents a mark on a chart:
Consider the following Excel chart, which is made from the data table mentioned earlier:
This chart is made up of the following chart elements:
Primary Horizontal Axis:
Primary Vertical Axis
Secondary Vertical Axis
Primary Horizontal Axis Title
Primary Vertical Axis Title
Secondary Vertical Axis Title
Chart Title
Data Labels
Gridlines
Legend
Trendline
In Excel, categories are plotted on the horizontal axis and data series are plotted on the vertical axis:
From the chart above, we can conclude the following:
Months are plotted on the primary horizontal axis.
Sales, cost, and profit are plotted on the primary vertical axis.
ROI is plotted on the secondary vertical axis.
Following are examples of other Excel chart elements:
Data Table with legend keys
Data Table with no legend keys
Error bars (Standard Error)
Error bars (Percentage)
Error bars (Standard Deviation)
Primary Major Horizontal Gridlines
Primary Major Vertical Gridlines
Primary Minor Horizontal Gridlines
Primary Minor Vertical Gridlines
Linear Trendline
Exponential Trendline
Linear Forecast Trendline
Moving Average Trendline
#1 Data table with legend keys
#2 Data table with no legend keys
#3 Error bars (standard error)
If you want to see the margin of error as a standard error amount, then use the standard error bar.
About Error Bar
An error bar is a line through a point on a graph, parallel to one of the axes, which can help you see margins of error at a glance.
#4 Error bars (percentage)
If you want to see the margin of error as a percentage, use the percentage error bar.
#5 Error bars (standard deviation)
If you want to see the margin of error as a standard deviation, then use the standard deviation error bar.
#6 Primary major horizontal gridlines
About Chart Gridlines
Chart gridlines are the faint lines that appear on the plot area of a chart. They are used to make the data in a chart that displays axes easier to read. They can appear both horizontal and vertical.
#7 Primary major vertical gridlines
#8 Primary minor horizontal gridlines
#9 Primary minor vertical gridlines
#10 Linear trendline
Use the Linear trendline if your data set is linear (resembles a straight line) and the data values are increasing or decreasing at a steady rate.
About Trendlines
The trendlines are used to graphically display trends in data. A trend is a movement in a particular direction.
A trend can be short (or seasonal), intermediate, or long term. Longer the trend more significant it is. For example, a 3 months trend is not as significant as 3 years trend.
#11 Exponential trendline
Use the exponential trendline if data values increase or decrease at increasingly higher rates.
#12 Linear forecast trendline
Use the Linear forecast trendline if your data set is linear (resembles a straight line), the data values are increasing or decreasing at a steady rate, and you want to forecast the data.
#13 Moving average trendline
Use the moving average trendline if there is a lot of fluctuation in your data.
How to add a chart to an Excel spreadsheet?
To add a chart to an Excel spreadsheet, follow the steps below:
Step-1: Open MS Excel and navigate to the spreadsheet, which contains the data table you want to use for creating a chart.
Step-2: Select data for the chart:
Step-3: Click on the ‘Insert’ tab:
Step-4: Click on the ‘Recommended Charts’ button:
Step-5: Select the chart you want to use from the ‘Insert chart’ dialog box and then click on the ‘ok’ button:
You should now be able to see the chosen chart inserted in your spreadsheet:
How to add, change, or remove a chart element in Excel?
To add, change or remove a chart element in Excel (2013 or above), follow the steps below:
Step-1: Open MS Excel and navigate to the spreadsheet which contains the chart you want to edit.
Step-2: Select the chart, and then from the ”Design’ tab, click on the ‘Add Chart Element‘ drop-down menu:
Step-3: Select the chart element you want to add, change or remove from one of the drop-down menus.
For example, if you want to add a data table to your chart, then click on ‘Data Table‘ > ‘With legend Keys‘:
You can now see your chart along with the data table:
How to add a trendline to a chart in excel?
Follow the steps below:
Step-1: Open MS Excel and navigate to the spreadsheet which contains the chart you want to edit.
Step-2: Select the chart, and then from the ”Design’ tab, click on the ‘Add Chart Element‘ drop-down menu.
Step-3: Click on the ‘Trendline’ drop-down menu and select the type of trendline you want to add to your chart.
How to change the color or style of a chart?
Step-1: Open MS Excel and navigate to the spreadsheet which contains the chart you want to edit.
Step-2: Select the chart, and then from the ”Design’ tab, click on the ‘Change Colors‘ drop-down menu to change the colours used in your chart:
If you want to change the style/design of the chart, then click on one of the styles under the ‘Design’ tab:
How to build data visualizations in Excel?
You can build a data visualization in excel through the following charts and graphs:
Frequently asked questions about types of charts in Excel
What is data visualization?
Data visualization is the presentation of data (both qualitative and quantitative data) in graphical format. Through data visualization you can easily: make sense of data (especially big data), classify and categorize data, find relationships among data, understand the composition of data, understand the distribution of data, understand the overlapping of data, determine patterns and trends, detect outliers and other anomalies in data, predict future trends and tell meaningful and engaging stories to decision-makers.
Why is it important to use the correct Excel chart?
If you use an incorrect Excel chart for your analysis, you may misinterpret data and make the wrong business and marketing decisions. If you use an incorrect Excel chart for your presentation, stakeholders may misinterpret your charts and take wrong decisions. Therefore selecting the right Excel chart is critically important.
What is a data series?
A data series is a set of related data points.
What is a data point?
A data point represents an individual unit of data. 10, 20, 30, 40, etc., are examples of data points. In the context of charts, a data point represents a mark on a chart.
What should be the criteria for selecting an Excel chart?
The type of Excel chart you select for your analysis and reporting should depend upon the type of data you want to analyse and report and what you want to do with the data. Do you want to classify and categorize data or find relationships among data or understand the composition, distribution or overlapping of data?
What is quantitative data?
Quantitative data (also known as interval/ratio data) is the data that can be measured. For example, ten customers, sales, ROI, weight etc.
What is qualitative data?
Qualitative data can be classified/categorized but cannot be measured, for example, colours, satisfaction, rankings, etc.
What is discrete data?
It is quantitative data with a finite number of values/observations. For example, five customers, 17 points, 12 steps etc.
What is continuous data?
It is quantitative data with value/observation within a range/interval. For example, sales in the last year.
What is nominal data?
Nominal data is qualitative data that can not be put into a meaningful order (i.e. ranked). For example {Blue, Yellow, Green, Red, Black}
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