10 Data Analytics Charts: Types and Use Cases [+ Data Visualization Examples]

You can’t do anything with data without data analytics charts.

They help you visualize product analytics data, make sense of it, and come up with the right decisions for your business.

However, there are many types of data charts, and learning when to use each can become confusing (and sometimes a matter of preference). So, in this guide, we’ll go through each one, explain their varieties, and discuss when are the best instances to use them.

TL;DR

• Bar charts illustrate categorical data with horizontal bars where the lengths of the bars are proportional to the values they represent. Its types include simple bar charts, grouped bar charts, and stacked bar charts.
• Column charts display information in vertical bars where the lengths of the bars correspond to the magnitudes they represent. There are clustered column charts and stacked column charts.
• Line charts, as the name suggests, use lines to connect individual data values and map a graph. It can be represented as a single-line chart or multiline chart.
• Funnel charts illustrate the progressive reduction of data as it passes through multiple phases, which is helpful for mapping the sales funnel or the customer journey. It can be either vertical or horizontal.
• Pie charts reflect the proportional representation of different categories within a whole. They are called “pie” charts because of their round shape, which is divided into “slices” or sectors representing different segments. It can be a basic pie, an exploded pie chart, or a doughnut chart.
• Heatmaps use color-coded systems to represent the magnitude of data points across a web page. It can track user’s eyes, clicks, mouse movements, scrolling patterns, rage clicks, and overall engagement with your app.
• Cohort tables are a versatile tool to visualize and track the behavior of different user groups—or cohorts—over time. And they can either be time-based or behavioral, depending on the metric you’re comparing.
• A dot chart, or dot plot, places individual data points on a simple scale without any adjustments. It can vary between basic dot charts, Cleveland dot charts, and quantile dot charts.
• Bubble charts use circles or “bubbles” to represent the magnitude of a data point, helping you to visualize three dimensions of data in one plot. It can either be in 3D or scattered.
• A stacked area chart is a multilayered graphic that accumulates data points from multiple categories to create a cumulative effect, with each layer representing a different category. It can be illustrated as a basic stacked chart or as a 100% stacked chart to represent percentages.
• Since you’ll need a product analytics tool to visualize your data like this, why not book a Userpilot demo?

#1 Bar charts

Bar charts illustrate categorical data with horizontal bars where the lengths of the bars are proportional to the values they represent. It makes comparing different categories trivially easy, quick, and efficient for data comparison (and decision-making).

Types of bar chart

There are many ways to organize data in bar charts, here are three types:

• Simple Bar Chart: The most simple type of chart for comparing different groups. Each bar represents a single category, and their length corresponds to its value.
• Grouped Bar Chart: This is used to compare the values of different sub-groups of your main categories. It displays the sub-categories by groups for easy comparison. For example, to compare how different teams performed at different tasks.
• Stacked Bar Chart: Stacked bar charts “stack” sub-group bars on top of each other, making it simple to see the contribution of each sub-group to a total value. It’s useful when surveying, for example, companies from different industries and comparing how many respondents from each industry chose a specific response.

When to use bar charts

Bar charts are especially useful for:

• Visually comparing the quantities or values of different categories.
• Displaying the distribution of data within sub-groups and categories.
• Comparing values across a single dimension and multiple categories.
• Studying the performance of different segments in your sample.

Bar chart example

An example of a bar chat use case could be to showcase your product’s daily usage and compare the popularity of your main features—understanding the percentage of users who are engaging with your product.

#2 Column charts

Column charts are just like bar charts, with the important difference being that they display information in vertical bars. Here, the lengths of the bars correspond to the magnitudes they represent as well. It can be used to compare different data sets and track changes in values over time, as well as spot trends across time and data groups.

Types of column charts

These types of column charts follow the same pattern as the bar charts, and they include:

• Clustered Column Chart. Which is ideal for comparing different categories of data points across multiple groups. It makes it easier to interpret how different segments perform against each other. And it works better with medium-sized datasets, so it doesn’t clutter the chart with too many sub-categories.
• Stacked Column Chart. It stacks data vertically within a column to display the total sum between categories while also comparing their values across other categories. It’s useful when you have multiple categories that contribute to a total sum (like total sales broken down by product) and need to visualize these proportions and highlight individual contributions.

When to use column charts

Column charts are used for different purposes, including:

• Comparing the values of segmented groups, as well as dates, months, quarters, etc.
• Showcasing trends over time, especially when focusing on time intervals (months, quarters, years, etc.) with no specific continuity.
• Ranking or ordering the categories based on the magnitude of their values.
• Comparing the performance of different segments across time or other categories.
• Denoting the frequency of events or occurrences.

Column chart example

If you need to compare your product’s main feature usage over time, for example, you can use a stacked column chart to display the number of daily active users who engage with these features each day.

This way, you can spot patterns or trends you can leverage to prepare marketing initiatives such as sending feature update announcements.

#3 Line charts

Line charts, as the name suggests, use lines to connect individual data points. It’s useful to monitor the variability of a value over a period of time. They can clearly illustrate trends in data at equal intervals, making them a go-to model for showcasing progress and forecasting future trends.

Types of line graph

The different types of line graphs are pretty simple:

• Basic Line Chart: This chart type displays data points connected by one straight line, representing a single variable’s evolution over a certain period. It excels in showcasing trends and patterns of only one metric.
• Multiline Chart: It showcases correlations and trends as well but between multiple variables. Each line represents a different parameter, making it possible to compare multiple data sets concurrently.

When to use line charts

Line charts are especially useful for:

• Tracing trends and patterns in data over a continuous dimension (most commonly, time).
• Comparing trends and patterns between different categories or variables.
• Prove a relationship and correlation between two variables.
• Displaying cumulative effects over time.

Line chart example

Let’s say you need to track product usage by device in order to optimize your app’s compatibility and prioritize the most used devices. A multiline chart can easily show you the trends and changes in product usage for every device so you can compare and see what optimizations are worth implementing.

#4 Funnel charts

Funnel charts illustrate the progressive reduction of data as it passes through multiple phases. Essentially, these charts lay out a process that begins at 100% and follows a smaller percentage after each stage so you can visualize, for example, how many leads passed through a sales process. They’re helpful for visualizing the customer journey or sales process, analyzing each step, and identifying potential areas of friction that can be improved.

Funnel chart types

There are two different types of funnel charts:

• Vertical Funnel Chart: This chart type displays data in a vertical direction, starting from the top and narrowing down to the bottom. Ideal for illustrating a process that has consequential steps, it’s especially handy for visualizing sales or conversion processes where you start with a large initial group that gradually dwindles down to a final result.
• Horizontal Funnel Chart: Quite similar to its vertical counterpart but displayed horizontally, this chart is perfect for scenarios where horizontal space is more than vertical, like presentations and infographics. It offers the same functionality of illustrating processes with sequential steps, often used for depicting pathways such as website visitor paths, till the user reaches the final action point.

When to use funnel charts

Funnel charts are primarily used to perform funnel analysis, which in itself can help you do many important tasks, such as:

• Laying out your marketing funnel to check how each stage is performing.
• Representing the stages of your sales pipeline and see how your leads have progressed through it.
• Displaying the conversion funnel across multiple channels.
• Visualizing how users progress through the user journey, spot points of friction, and optimize the customer experience.

Funnel chart example

In SaaS, you can perform funnel analysis to visualize the user journey. This allows you to identify stages where users are facing the most friction and dropping off the most often—providing improvement opportunities for the product experience.

#5 Pie charts

Pie charts reflect the proportional representation of different categories within a whole. They are called ‘pie’ charts because of their round shape, which is divided into “slices” or sectors representing different segments.

Each slice of the pie corresponds to a category from your data set, and the size of each slice is proportional to the percentage of the whole that category represents, allowing you to instantly understand the relationship between the part and the whole.

Types of pie chart

Although there are many types of pie charts, these are the most used varieties you’ll end up using:

• Basic pie charts: The simple, circular charts display data percentages in the form of slices, where each slice represents a unique segment from a whole sample.
• Exploded pie charts: These are variations of the basic pie chart where one or more slices are separated from the main body of the chart. They work great for highlighting certain categories or emphasizing distinctions between parts of the data being analyzed.
• Doughnut charts: These are similar to basic pie charts but with a blank center, giving them a doughnut-like appearance. The empty center offers a space for additional information, such as overall data figures or the sample size.

When to use pie charts

Pie charts are useful for comparing data from a handful of segments, as well as percentages. Just beware that too many slices can clutter the chart and make it hard to discern.

Pie chart example

A great use case for pie charts is to watch feature frequency broken down by plans. This way, you can find out which type of users are more likely to engage with specific features.

#6 Heatmaps

Heatmaps use color-coded systems to represent the magnitude of data points across a web page. They’re designed to convert complex statistical data into an intuitive color spectrum.

Said spectrum helps you identify scrolling trends, click patterns, or popular movements on a website so you can determine which elements draw the most attention and action from users.

Types of heatmap

Although there are many ways to make heatmaps, most heatmaps tools will offer these types of graphics:

• Eye Tracking Heatmaps: It tracks users’ eye movements across the interface, showcasing where they gaze, pause, and blink. It provides insights into visual patterns and user attention so you can improve the interface designs to build more engagement.
• Click Heatmaps: These maps visualize where users click or tap the most (and therefore show what elements draw the most attention). They’re often color-coded, with hot spots showing popular areas and cold spots showing areas with fewer interactions.
• Movement Heatmaps: They capture cursor or finger movement across the screen, where yellow or red areas indicate higher levels of activity. This way, you can see the mouse movements users take when navigating your product or website.
• Scroll Heatmaps: These maps show how far down users scroll, typically showing warmer colors at the top while gradually fading out as you move down the page. This way, you can assess the effectiveness of your page length, the placement of key content, and overall user engagement.
• Engagement Heatmaps: They measure which sections of your interface are visually engaging to users, offering insights on design aesthetics and content relevance to improve user experience.

When to use heatmaps

Heatmaps are especially helpful for tracking navigational data within an app or website, allowing you to:

• Display data density to identify concentrations or patterns within a specific data set.
• Represent user engagement on a website or application.
• Optimize your website or app to incentivize specific user behavior.

Heatmap example

One way to leverage heatmaps is to analyze feature engagement. For example, you could use a tool like Userpilot to track how many interactions users have with your app features.

This way, you can see which buttons are most clicked, and optimize your feature’s interface to drive more product stickiness.

#7 Cohort tables

Cohort tables are a versatile tool to visualize and track the behavior of different user groups—or cohorts—over time. Here, the columns usually represent user groups (who, for example, signed up on different periods), while the rows represent time periods.

This chart lets you compare data from multiple groups across a timeline you can use to keep track of user engagement, retention, and churn rates.

Types of cohort table

There are two common types of cohort tables:

• Time-based Cohort Tables: It groups customers by specific time frames (like the month they made their first purchase) and lists their performance across multiple periods of time (days, weeks, months.) It can be useful for comparing, for example, onboarding completion between users who signed up before and after a specific update.
• Behavioral Cohort Tables: It groups customers based on an action performed during a specific frame (users who completed primary onboarding the last month) and correlates it with other behavioral data such as engagement, retention, or conversions. This way, you can come up with personalized product strategies for cohorts that seem to be disengaged.

When to use cohort tables

Cohort tables are quite useful for:

• Tracking the performance of multiple cohorts (retention, engagement, milestones, etc) over time.
• Identifying cohorts that are disengaging or require special attention.
• Analyze the receptivity of a new feature you just launched and see how long it takes for different cohorts to adopt it over time.

Cohort table example

A great instance to use cohort tables is to track retention rates over time, as it allows you to watch the percentage of users from each cohort who come back to use a specific feature over time.

This way, you can see which cohorts are disengaging too fast and understand why.

#8 Dot charts

A dot chart, or dot plot, places individual data points on a simple scale without any adjustments. It helps you visualize individual data points to pin down trends, detect patterns, or find outliers within various datasets.

Its precision makes it easier to interpret statistical data that can’t be accurately visualized with a line chart.

Dot chart types

• Basic Dot Charts: They’re useful to present grouped or clustered data points, where each dot represents an individual data point. It can provide a simple, clean visualization of sample data without any adjustment.
• Cleveland Dot Charts: It emphasizes the relative position of data points along a common horizontal (or vertical) line, making it easier to compare different data points and observe trends within your dataset. It’s very similar to a bar chart but uses dots instead.
• Quantile Dot Charts (Wilkinson chart): This chart uses dots to display density. It can effectively illustrate the variation or distribution across different categories, magnitudes, or time periods—making it useful for large data sets.

When to use dot charts

Dot charts are very versatile and can be used for many purposes, including:

• Highlighting individual data points in small datasets, hence allowing you to place special emphasis on each specific observation.
• Organize, analyze, and visualize larger sets of data.
• Visualizing patterns, trends, or clusters within multiple segments of data.
• Illustrate the variability and dispersion of data, helping you spot specific patterns or correlations within the data.

Dot chart example

One way to use dot charts is to track feature interactions among active users and see, for instance, the popularity of each feature according to the number of interactions it has from users.

This way, you can see which features are mostly used by loyal customers and try to replicate their behavior in the rest of your user base to drive customer loyalty.

#9 Bubble charts

Bubble charts help you to visualize three dimensions of data in one plot. They function like scatter plots but use circles or “bubbles” to represent the magnitude of a data point.

Essentially, the location of the bubble along both the x and y-axes represents two dimensions, while the bubble’s size measures the third dimension.

These charts work great for displaying relationships between three variables without having to rely on 3D graphics.

Bubble chart types

There are two ways to visualize bubble charts:

• 3D Bubble Charts: It illustrates the bubbles in 3D to visually represent the three dimensions of data. Bubbles can vary in size and color based on the data they represent, making them useful for complex multi-variable analyses and identifying relationships between three or more variables.
• Scatter Plot Bubble Charts: These charts work as dot plots but with bubbles that represent a third dimension. It helps highlight relationships between multiple variables and works great for forecasting trends, identifying potential outliers, or shaking out correlations within your data set.

When to use bubble charts

Overall, bubble charts are an excellent tool to visualize data where multiple variables can represent a correlation, making it good for:

• Find out the relationship between multiple business metrics to find out which numbers lead to business success.
• Visualizing the correlation between in-app behaviors such as time spent on the page and engagement.
• Plot feature requests to get a visual representation of priorities based on parameters like user demand, development time, and potential impact.
• Comparing the risk/reward ratio of multiple strategies to make more informed decisions.

Bubble chart example

A great instance of a bubble chart use case is for studies that require statistical depth. The following example (by Hubspot) shows how the gender and age of a user can have an effect on their time spent online.

And as the graphic shows, it seems like females tend to spend more time online as they grow up.

#10 Stacked area charts

A stacked area chart is a multilayered graphic that accumulates data points from multiple categories to create a cumulative effect, with each layer representing a different category.

It uses the “area” beneath the lines to represent volume, making it useful when you need to display how individual categories contribute to the whole over time while maintaining the total and proportions of each category intact.

Stacked area chart types

Stacked area charts are often represented in two ways:

• Basic Stacked Area Chart: The regular visual that layers series of data upon each other, filling in the space below the lines to clearly show the cumulative value of multiple data series over time. It can be used for understanding the proportions of categories contributing to total values.
• 100% Stacked Area Chart: This chart type also visualizes the cumulative trends of multiple data series. However, it normalizes the data to represent each category as a percentage of a whole (100%). It provides a more comparative perspective, allowing easy identification of not just the total trends but the relative contributions of each category over time.

When to use stacked area charts

Stacked area charts are an excellent visualization tool for:

• Visualizing the contribution of multiple categories of data over time to check their performance.
• Spot changes or shifts in the composition of data over time to point out trends and patterns.
• Compare the performance or contribution of each category over time and see which ones make more impact.
• Illustrate the relative proportions of different components within a total, whether by a sum or a percentage.

Stacked area chart example

An example of how to use stacked area charts is to observe the cumulative effect of users with different plans on their engagement with a specific activity and track it over time.

This way, despite seeing the same number of engagements in the last few days, you can discern if the majority of it is coming from freemium or paid users (and if it has changed recently).

Conclusion

There are data analytics charts for every context and pool of data.

Once you have enough experience using them, you’ll be able to collect more insights from the same data, perform data-driven strategies, and unlock product growth potential for your company.

And since you’ll need a product analytics tool for this, why not book a Userpilot demo? Here, you’ll be able to actively process user data and implement product strategies that lead to more SaaS growth—all with a low budget and without having to code!