What is a Pareto Chart?

The Pareto Chart is a complex bar chart containing a line graph. It represents individual values in descending order using bars. Meanwhile, a line represents the cumulative total of the individual values in percentage form. The chart was inspired by Italian economist Vilfredo Pareto and his 80/20 principle, which states that 80% of effects arise from 20% of causes.

Pareto Chart Example

In the modern world of advanced data analysis, the Pareto Principle holds great significance. It has far-reaching implications in various business applications, from research to manufacturing, marketing, and sales. Using the Pareto Chart, users can unveil actionable insights from data distribution and learn about the factors leading to a specific outcome.

The Anatomy of a Pareto Chart

A Pareto Chart comprises the following components that work together to provide an actionable representation of data identifying key factors contributing to an outcome or issue:

Categories or Factors

The x-axis of a Pareto Chart contains different categories or factors users need to analyze. They can be incredibly diverse, ranging from defective product components to billing error types, quality control failures, sources of website traffic, inventory shortages by item, and most popular menu items, to name a few. For instance, the image above outlines the key factors causing people to arrive late to work, including traffic, weather, and emergencies.

Frequency or Quantity

Similarly, the y-axis of a Pareto Chart displays the outcome or occurrence of each factor in terms of quantity, frequency, or impact in descending order of value or importance. For instance, the image above shows traffic as the leading cause of late arrivals, followed by childcare and public transportation. The bar’s height can identify the difference, as it directly corresponds to the y-axis measurement.

Cumulative Percentage Line

The cumulative percentage line is among the key differentiators separating a bar chart from a Pareto Chart. This line demonstrates the aggregated impact of all the categories/factors and their outcomes in terms of frequency or quantity. The CP line also illustrates the percentage impact of the top categories on the total outcomes.

Vertical Bars

Vertical bars on the x-axis baseline represent each category or factor contributing to an outcome or issue. The Pareto Chart uses the height of these bars to visually illustrate which category has the biggest impact on a particular outcome, such as late arrivals in the image above. These bars are arranged in descending order to avoid confusion and optimize data integrity.


The Pareto Chart comprises a baseline at its bottom as a reference point for measuring bar height and other essential data for accurate comparisons.


Every Pareto Chart has a descriptive title at the top, informing viewers and users about the data being measured and presented along both axes. The title ensures they understand the categories, measurement units, and other essential information the chart illustrates for accurate interpretation.


Many Pareto Charts display data from various sources or periods. Hence, creators use key or explanatory information (or legend) to differentiate the categories and measurements illustrated.


Finally, a Pareto Chart contains various annotations, including text labels, arrows, or notes, to provide additional information, context, or explanation.

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Key Benefits of Using a Pareto Chart

A Pareto Chart offers several lucrative benefits extending to different applications and industries, including (but not limited to) making it an ideal data analytics and BI reporting tool:


The core benefit Pareto Charts offer is helping users prioritize factors or issues based on their outcomes or impact. They present data in descending order of value or importance, allowing decision-makers to allocate their focus and resources on the most influential factors to achieve their desired outcomes. For instance, if traffic is the main factor responsible for late arrivals to work, businesses will devise different strategies and solutions to reduce the impact of traffic on productivity and delays, such as flexible timings, remote work, telecommuting, and carpooling.

Visual Representation

Pareto Charts are among the most visually engaging data charts available to businesses and data analysts. They combine bars and graphs representing different categories/factors and cumulative percentages to simplify pattern recognition and relationship management.

Data Interception

For most people, interpreting complex datasets can be challenging. However, Pareto Charts make it easier to understand and use the information presented by displaying data graphically after breaking it down into smaller categories. Hence, individuals can comprehend the various factors leading to a specific outcome and their importance with relative ease.


Pareto Charts can aid internal and external communication by digitalizing, centralizing, and streamlining complex data into a format most viewers can easily understand. Hence, they’re ideal for conveying information, such as findings, issues, insights, and recommendations, to employees, customers, vendors, stakeholders, and other personnel in the value chain.

Problem Solving & Goal Setting

The Pareto Chart is an excellent problem-solving tool. It provides a comprehensive framework encompassing structured datasets that can be used for brainstorming solutions to complex problems at different touchpoints of an organization. Users can also set realistic goals by identifying target areas and continually guiding efforts to improve their processes and systems.

How Different Industries Use the Pareto Chart

Today, many industries employ a data-driven approach to problem-solving, decision-making, and resource allocation. Using Pareto Charts, they can simplify and improve their efforts by illustrating valuable datasets and their impact on different processes and outcomes. Below is how common industries use Pareto Charts today:


The manufacturing industry uses Pareto Charts at various touchpoints in their value chains, from production to quality control and supply chain. They leverage the chart to analyze data on manufacturing defects, production issues, customer pain points, and other frequent problems that impact efficiency and revenue. Manufacturers can use the data to address these issues by allocating resources more strategically.


Hospitals and clinics can use the Pareto Chart to identify patterns in patient, administration, and other data types. For instance, they can determine the most common medical conditions they treat to ensure they have sufficient inventories of drugs and essential supplies, mandatory equipment, and a team of experienced healthcare professionals specializing in those conditions. Similarly, administration departments can use Pareto Charts to identify the key factors affecting patient dissatisfaction and turnover, including billing, responsiveness, data issues, etc.


The retail industry is incredibly diverse, with millions of businesses selling different products and services to consumers worldwide. Pareto Charts are excellent data analysis tools that help businesses understand which products, customers, marketing channels, and supply chain models drive most of their revenue. They can also compare their inventories, product development issues, consumer pain points, and other high-impact factors influencing profitability, sustainability, and continuity.


Project managers working in construction sites can use Pareto Charts for several applications, including identifying major causes of delays, safety incidents, and scope creeping. They can also analyze data that affects project performance, including budget, planning, regulation, and resource allocation, to make strategic adjustments proactively.


Government agencies use Pareto Charts in daily operations, from handling citizen complaints to prioritizing public issues and managing infrastructure. The descending order of bar heights and impact allow them to identify the most prevalent and impactful issues in their communities, state, and country to simplify and accelerate decision-making.

Freight Shipping and Logistics

Freight Shipping and logistics companies rely heavily on complex datasets to simplify and improve supply chain operations, especially when storing and moving goods across vast distances using different transportation modes (trucks, trains, ships, planes, etc.). They use Pareto Charts for several applications, such as choosing the best routes in terms of traffic and distance, managing cargo handling equipment and labor issues, and reducing bottlenecks in resource allocation by targeting the most impactful categories for optimal change.

How to Create a Pareto Chart

There’s more to creating a Pareto Chart than drawing bars and calculating cumulative percentages. It’s a much more complex process that involves collecting data from various sources and visualizing it in an organized manner to prioritize and address different impactful factors. Here’s a simple breakdown of the creation process:

Data Collection & Categorization

The first step in creating a Pareto Chart involves articulating the problem or topic you’re addressing, whether it’s high freight transportation costs, high customer turnover, or manufacturing defects. Once you’ve identified the key focus of your chart, you can gather relevant data from various sources, including incident reports, customer feedback survey forms, or fleet benchmarking reports. You must organize the data into distinct and mutually exclusive categories. For instance, if you’re analyzing high freight transportation costs for your trucking operations, categories might include – rising fuel costs, idling, frequent maintenance repairs, traffic, loading/unloading times, etc.

Data Ranking by Impact or Frequency

The next step is to count the number or frequency of occurrences in each dataset category. With this, you can calculate the impact of each category by assigning a numerical value to quantify it based on factors such as time, cost, number of people, etc. Finally, you can rank each category in descending order based on the frequency or impact.

Bar Chart Construction

Once you’ve structured the data, you can create a Pareto Chart by adding the categories in descending order on the x-axis and the frequency count or impact value on the y-axis. Once you’re done, create bars with heights corresponding to the value or count of its category.

Cumulative Percentage Calculation

Once you’ve drawn the bars, the final step is calculating and adding the cumulative percentage line for each category. You can do this using the following formula:

Cumulative Percentage = [Cumulative Sum of Y-Axis (Frequency Counts/Impact Value) / Sum of All Counts and Values] x 100%

Note: To calculate the cumulative sum, you must add the value of each dataset to the sum of all the previous data points.

Cumulative Percentage Line Addition

Once you have a dataset of all the corresponding cumulative percentages for each category, you can add a secondary line on the y-axis and label it from 0-100%. Next, simply plot a line graph using the percentage values and label them accordingly.

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How to Read & Interpret a Pareto Chart

Reading and interpreting a Pareto Chart involves understanding the cumulative impact of different categories and factors on outcomes. Below is a simple guide to analyzing the most significant contributors, reading the cumulative percentage lines, and using the information for insights or improvements:

Analyzing the Most Significant Contributors

Interpreting a Pareto Chart starts with understanding the key differences in the categories or factors leading to a particular outcome. Using the bar heights as a guide, you must recognize and focus on the most significant contributors since they’re the primary source of the issue or problem you’re looking to analyze and solve.

Reading the Cumulative Percentage Line

Interpreting the cumulative percentage line is the most technical aspect of understanding the information on a Pareto Chart. However, it’s also the most important component of the chart since it provides actionable insight into data distribution. Hence, you must understand how it works and what it represents. The CP line shows the aggregate contribution of each category to the total as it moves along the x-axis. Steep increases indicate that a category is making significant contributions. However, the line begins to flatten as the bars get shorter since their incremental contribution becomes smaller.

Generating Actionable Insights for Improvement

You should target the top categories on a Pareto Chart for maximum impact. However, targeting multiple smaller bars, especially when the most significant factors or categories are difficult to address, is a smart way to prevent resource waste.

Common Variations of Pareto Charts

In the modern age, there are multiple variations of Pareto Charts serving specific purposes or providing more insights beyond their traditional counterpart. Below are the most common variants:

Horizontal Pareto Chart

A Horizontal Pareto Chart is a horizontal counterpart of a traditional Pareto Chart. Hence, the only difference is that the values are switched between the x and y axes. However, the tallest bars still represent the most important categories of factors. The main reason many people prefer this version is to analyze lengthy datasets by representing factors vertically instead of horizontally.

Multi-Dimensional Pareto Chart

As the name suggests, a multi-dimensional Pareto Chart allows users to analyze multiple categories or factors contributing to a problem. For instance, if you’re analyzing customer dissatisfaction in your restaurant, you can consider factors like food quality, service, and ambience and break each factor down into their respective issues. With this multi-faceted approach, you can enhance business intelligence and identify interconnected patterns between different factors.

Comparative Chart

Finally, a Comparative Pareto Chart is commonly used by businesses to compare the performance or issues of different groups or periods. For instance, marketers can use it to compare engagement metrics of different market segments or sales in varying seasons. The key difference between this variant and traditional Pareto Charts is that it displays multiple sets of bars together, allowing you to compare categories, frequencies, and variations in cumulative percentages.