In this article I will show you how to select the best Excel Charts for Data Analysis, Presentation and Reporting within 15 minutes.  You will learn about the various excel charts types from column charts, bar charts, line charts, pie charts to stacked area charts.

The importance of data visualization

Data visualization is the presentation of data (both qualitative and quantitative data) in graphical format.

Through data visualization you can easily:

1. Visualize data (make sense of data, especially big data)
2. Classify and categorize data
3. Find a relationship among data
4. Understand the composition of data
5. Understand the distribution of data
6. Understand the overlapping of data
7. Determine patterns and trends
8. Detect outliers and other anomalies in data
9. Predict future trends
10. 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, then stakeholders may misinterpret your charts and take wrong decisions. Therefore selecting the right Excel chart is critically important.

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The anatomy of an Excel chart

In order to read an Excel chart, it is important that you 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’ and so on.

Data series – A data series is a set of related data points.

Data point – 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:

1. Primary Horizontal Axis:
2. Primary Vertical Axis
3. Secondary Vertical Axis
4. Primary Horizontal Axis Title
5. Primary Vertical Axis Title
6. Secondary Vertical Axis Title
7. Chart Title
8. Data Labels
9. Gridlines
10. Legend
11. Trendline

Following are examples of other chart elements:

Data Table with legend keys

Data Table with no legend keys

Error bars (Standard Error)

If you want to see the margin of error as a standard error amount than 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.

Error bars (Percentage)

If you want to see the margin of error as a percentage than use the percentage error bar.

Error bars (Standard Deviation)

If you want to see the margin of error as a standard deviation than use the standard deviation error bar.

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.

Primary Major Vertical Gridlines

Primary Minor Horizontal Gridlines

Primary Minor Vertical Gridlines

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.

Exponential Trendline

Use the exponential trendline if data values increase or decrease at increasingly higher rates.

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.

Moving Average Trendline

Use the moving average trendline if there is a lot of fluctuation in your data.

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.

How to add a chart to an Excel spreadsheet

In order to add a chart in 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 order 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 in 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

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 then 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 the change the colors used in your chart:

If you want to change the style/design of the chart then click on one the styles under the ‘Design’ tab:

How to change the chart type

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:

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:

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 data:

1. Visualize data (make sense of data esp. big data)
2. Classify and categorize data
3. Find a relationship among data
4. Understand the composition of data
5. Understand the distribution of data
6. Understand the overlapping of data
7. Determine patterns and trends
8. Detect outliers and other anomalies in data
9. Predict future trends
10. Tell meaningful and engaging stories to decision-makers

Most common data types

Following are the most common data types that can be visualized:

• Quantitative data (also known as interval/ratio data) is the data that can be measured. For example 10 customers, sales, ROI, weight, etc.
• Qualitative data is the data that can be classified/categorized but it can not be measured.  For example colors, satisfaction, rankings, etc.
• Discrete data – quantitative data with a finite number of values/observations. For example 5 customers, 17 points, 12 steps, etc.
• Continuous data – quantitative data with value / observation within a range/interval. For example sales in the last year.
• Nominal data – qualitative data that can not be put into a meaningful order (i.e. ranked). For example {Blue, Yellow, Green, Red, Black}
• 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}

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 that has got 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 which 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:

Now this chart, though still look cluttered, is much easier to read and understand.

#2 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:

#3 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’:

#4 Use a clustered column chart when you want to show the maximum and minimum values of each data series you want to compare.

#5 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 by breaking it into several smaller clustered column charts. For example, you can create one column chart which just 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 you use.

When to use a combination chart

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.

Following is a short video on creating a combination chart in excel:

#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:

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:

When to use a bar chart

#1 Use a bar chart whenever the axis labels are too long to fit in a column chart:

When to use a line chart

#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 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:

When to use a number chart

If you want to visualize just one type of data and it contains a numeric value which does not fall in any range/interval then use the number chart:

When to use a gauge chart (also known as speedometer chart)

If you want to visualize just one type of data and it contains a numeric value which falls in a range/interval then use the gauge chart (also known as speedometer chart):

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:

From this chart, we can conclude that the relationship between the two variables (‘x’ and ‘y’) is linear. What that means, 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 there are ten or more data points 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 data points) 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.

When to use a pie chart

#1 Use a pie chart when you want to show a 100% composition of data. In other words, the various pie slices you use must add up to 100%. What that 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 to show the breakdown. However, if there were more than four categories to plot, like eight or ten categories, then 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 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):

#4 Order your pie slices in such a way 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:

In order 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).

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 histogram

Use a histogram to show frequency distribution for quantitative data:

Note: You would need to install the ‘Analysis ToolPak’ in order to create a histogram in Excel.

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 actually a Venn diagram:

In the context of 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 then the website doesn’t have attribution issues. If there is a good amount of overlap then the website has got attribution issues and you should seriously consider taking multi-channel attribution into account while analyzing and interpreting the performance of marketing campaigns.

To learn more about attribution modelling read this article: Beginners Guide to Google Analytics Attribution Modelling

Another great use of Venn diagrams is in visualizing the backlinks overlaps between websites:

The tool that I have used to create this Venn diagram is known as Venny.

Note: You can create a Venn diagram in Excel. Check out this tutorial on the Microsoft Office website: Create a Venn diagram

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 then 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) which 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 you want to show progress towards each goal then use the ‘Actual vs. target’ chart. But if you have only one goal and you want to show progress towards this goal (by using both qualitative and quantitative data) then use the bullet chart.

A bullet chart can be a vertical bar chart or 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

The chart below shows different stages of the purchase funnel and how user 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 funnel and sales funnels. This chart is available in MS Excel (2016 and above). You just need to select your data table and then insert the ‘Funnel’ chart.

Charts to avoid for reporting purposes

Throughout this article, I have talked about the charts that should be used. But there are some charts which should be avoided for reporting purposes unless your target audience is as data-savvy as you.  Following are those charts:

Treemap

Waterfall chart

Radar chart

Bubble chart

The reason you should be avoiding reporting data via these charts to your clients is simple. The majority of 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.

Best practices for designing column and line charts

#1 Start the ‘Y’axis value at zero

When you do not start the ‘Y’ axis value of a chart at zero, the chart does not accurately reflect the size of the variables (in case of column charts) and trend (in case of line charts). For example, the following column chart amplifies changes because the ‘y’ axis value starts at 440 instead of 0:

Following is the correct column chart:

Another example. The following line chart amplifies the growth of Facebook fans because the ‘y’ axis value starts at 2500 instead of 0:

Following is the correct line chart:

#2 Do not use line chart (to create trends) if you have less than eight data points

When you create a line chart with a few data points, the trend that you see can be very misleading. For example, the following line chart just contain two data points and as a result, it makes the growth look phenomenal:

For a line chart, the more data points the better.

#3 Do not hide the scale on the ‘y’ axis of a column/line chart

When you hide the scale of the ‘y’ axis, your chart won’t accurately reflect the size of the variables (in case of column charts) and trend (in case of line charts). Without any scale on the y-axis, there is no way of knowing where the y-axis starts. When you use such charts it creates doubt on your analysis.

#4 Add context to your chart

Different people analyze and interpret the same chart differently. It all depends upon the context in which they analyze and interpret the chart. No matter what chart you select, some people will always find a way to misinterpret your chart. Therefore it is critical that you provide context with your chart in the form of written commentary and describe exactly the intent of your chart.

First present the context, then the insight, and then the chart to support your insight. In this way, you are giving clues to your chart reader regarding how to read your chart. For example:

Frequently asked questions about Excel charts

Other articles on data analysis and reporting

• Making Good Marketing Decisions Despite of Faulty Analytics Data
• Ten tips to analyse data trends in Google Analytics
• 21 Secrets to Becoming a Champion in Data Reporting
• How to become champion in data reporting via Storytelling