Funnel Charts: Types, Examples, and How to Create

Understanding how your users or customers move through a process is fundamental to improving it. Whether you’re tracking website visitors as they become paying customers or monitoring candidates through a hiring pipeline, you need to see where they succeed and where they drop off. Funnel charts are a powerful tool for visualizing this journey, revealing process efficiency, flow, and critical points of friction.

The funnel chart is a diagnostic tool for analytics professionals, marketers, and business leaders. It can quickly highlight bottlenecks in your sales pipeline, drop-offs in your marketing conversions, and inefficiencies in your operational workflows. When you understand how they work, you can transform raw data into actionable insights that drive growth and optimization.

This comprehensive guide will walk you through everything you need to know. You’ll learn what a funnel chart is, when to use one, and how it differs from other chart types. We will cover its mechanics, design best practices, common pitfalls, and a step-by-step process for building your own effective funnel visualization. 

What is a funnel chart?

A funnel chart is a data visualization that shows the progressive reduction of data as it passes from one phase to another. Its shape, wide at the top and narrow at the bottom, visually represents a process that starts with a large number of participants and filters them down through a series of steps or stages. Each stage represents a smaller subset of the previous one, making this chart an ideal tool for tracking conversion rates and identifying drop-offs.

The metaphor is simple but effective: just as a physical funnel guides a large volume of liquid into a small opening, a funnel chart illustrates how an initial large group is guided through a sequential process, with some portion “leaking” out at each step. The head of the funnel (the widest part) represents the starting population, and the neck (the narrowest part) shows the final number of participants who completed the process.

How funnel charts differ from other charts

While they might appear similar to other visualizations at first glance, funnel charts serve a unique purpose.

• Funnel chart vs bar chart: A standard bar chart is used to compare distinct, independent categories. A funnel chart, however, shows the flow between interconnected, sequential stages. The width of each segment in a funnel is proportional to its value, creating the characteristic tapering shape that a series of bars would not naturally form.
• Funnel chart vs pyramid chart: A pyramid chart is used to show a hierarchical structure or proportional distribution of a whole, like population demographics or organizational levels. Its segments are independent parts that make up a total. A funnel chart represents a dynamic process of reduction, where each stage is a subset of the one before it. The key difference is process flow versus static hierarchy.

When and why to use funnel charts

Funnel charts are most effective when you need to visualize a linear, sequential process with clear, distinct stages. Their primary strength is in quickly showing the health of a conversion-based workflow.

Common contexts for funnel charts

• Sales pipeline funnel: This is a classic use case, tracking leads as they move from initial contact to a closed deal. Stages might include Lead > Qualified Lead > Meeting Scheduled > Proposal Sent > Negotiation > Won. A sales funnel chart immediately shows where deals are stalling.
• Marketing conversion funnel: Marketers use funnels to visualize the customer journey. Common stages include Website Visitor > Product View > Add to Cart > Checkout > Purchase. This helps identify where potential customers abandon their journey.
• Recruitment process funnel: HR teams can track job applicants from application to hire. The stage might be Application Received > Resume Screened > Phone Interview > On-site Interview > Offer Extended > Offer Accepted.
• User onboarding funnel: Product teams can monitor how new users adopt a product. The flow through the funnel might be Sign Up > Complete Profile > Use Key Feature > Invite Teammate. Tracking these stages reveals points of friction.

Insights funnel charts help uncover

A funnel visualization is valuable because it makes weaknesses in a process obvious. For instance, by looking at the shape of the funnel, you can spot:

• Bottlenecks: A sudden, sharp narrowing between two stages indicates a significant drop-off. This is a bottleneck where a large percentage of your participants are exiting the process. It’s a clear signal to investigate why that stage is underperforming.
• Stage-to-stage conversion efficiency: By comparing the relative size of adjacent segments, you can assess how efficient each step is. A smooth, gradual taper suggests a healthy process, while an uneven one may point to specific problem areas.
• Overall process health: The final stage's value as a percentage of the initial stage gives you the overall conversion rate. This high-level metric is an important key performance indicator (KPI) for many business functions.

Limitations of funnel charts

Despite their strengths, funnel charts are not a one-size-fits-all solution. They cannot show:

• The “why” behind drop-offs: A funnel chart tells you where users are dropping off, but it doesn't explain why. It's a diagnostic tool that points you toward areas needing deeper analysis, often through user surveys, session recordings, or A/B testing.
• Complex or non-linear journeys: Funnels are designed for simple, sequential processes. If users can skip stages, move backward, or follow multiple paths, a funnel chart will be misleading.
• Detailed segment comparisons: While you can create multiple funnels to compare segments (e.g., mobile vs desktop users), a single funnel chart isn’t ideal for showing this kind of comparative detail.

How funnel charts work: The mechanics

To build and interpret a funnel chart, you need to understand its core components and the metrics used to measure flow.

Stages and values

A funnel is composed of stages, which are the distinct steps in the process you are measuring. For each stage, you need a corresponding value, which is typically an absolute count of the users, leads, or items that reached that point.

Visualizing drop-off

The drop-off, or attrition, between stages is what gives the funnel its shape. The decrease in width from one segment to the next visually represents the number of participants who didn’t go forward in the funnel. This type of chart makes it easy to spot the largest points of friction at a glance.

Key metrics and annotations

To make a funnel chart truly insightful, you must annotate it with the right metrics. Simply showing the raw numbers isn’t enough to make your chart useful. You’ll also need:

• Absolute numbers: The raw count for each stage (e.g., 10,000 visitors, 800 signups). This provides scale and context.
• Percentage of initial: This metric shows the value of each stage as a percentage of the starting value. It helps in understanding the cumulative drop-off from the very beginning. For example, if the final stage is five percent of the initial, you know your overall conversion rate is five percent.
• Stage-to-stage conversion rate: This is the percentage of users from the previous stage who made it to the current stage. It’s the most important metric for identifying specific bottlenecks. For instance, a 90 percent conversion from “Add to Cart” to “Checkout” could be considered healthy, depending on your industry, but a 30 percent conversion may be concerning.

Here, we should distinguish between cumulative conversion (percentage of initial) and step conversion (stage-to-stage). Both are valuable but show different things. Cumulative rates tell you about the overall efficiency, while step conversion rates pinpoint the exact location of problems.

Data structure and input format

The data you need to create a funnel chart is easy enough to understand. Most visualization tools need data to be in a simple tabular format with two columns:

1. Stage column: Contains the names of each stage in your process (e.g., ‘Website Visitors’, ‘Signed Up’).
2. Value column: Contains the corresponding numerical count for each stage (e.g., 50,000, 2,500).

The stages must be sorted in the logical, sequential order of the process. An incorrectly ordered funnel is meaningless. When preparing your data, make sure your stages are sorted chronologically from top to bottom. Also, practice good data hygiene: check for missing values or unexpected zeros that could make the visualization messy. If a stage legitimately has zero participants, the funnel will correctly show a complete drop-off at that point.

Here is a simple example data set:

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From this data, we can calculate conversion rates:

• Pricing View Rate (Stage-to-Stage): (15,000 / 50,000) = 30%
• Trial Start Rate (Stage-to-Stage): (3,000 / 15,000) = 20%
• Customer Conversion Rate (Stage-to-Stage): (750 / 3,000) = 25%
• Overall Conversion Rate (Start to Finish): (750 / 50,000) = 1.5%

Variants and funnel chart styles

The standard vertical funnel is the most common. But several variations exist, each with specific uses and design considerations.

• Standard funnel: This is the classic, top-down funnel with trapezoidal segments. It’s intuitive and widely understood, making it the default choice for most use cases.
• Inverted funnel (triangle style): An inverted funnel is sometimes used to represent a process of growth or buildup, such as contributions to a goal. However, it can be confusing and is often better represented by a different chart type. Its primary use is stylistic, but the inverted funnel can sacrifice clarity.
• Smoothed vs stepped funnels: A standard funnel uses stacked, trapezoidal segments. Some tools allow for smoothed, curved edges, which creates a more fluid appearance. This is purely an aesthetic choice and doesn’t change the data representation. Stepped funnels are often clearer, as the distinct edges reinforce the separation between stages.
• Bar-style funnel: This variant uses a series of progressively shorter horizontal bars instead of a connected funnel shape. This can be useful when you want to label stages more clearly on the side or when the tapering effect of a traditional funnel feels exaggerated. It functions like a sorted, single-series bar chart.

Our suggestion is to prioritize clarity when choosing your funnel chart style. Consider accessibility by ensuring high contrast between colors and text, and choose color palettes that are distinguishable for users with color blindness.

Best practices for funnel chart design

A poorly designed funnel chart can be more misleading than helpful. Follow these data visualization best practices to ensure your chart is clear, accurate, and impactful.

• Order stages logically: This is the most critical rule. The stages must follow the actual sequence of the process, usually from top to bottom.
• Keep stage count reasonable: A funnel with too many stages (e.g. more than eight to 10) becomes cluttered and hard to read. Combine minor sub-steps into broader, more meaningful stages. A good target is four to six stages.
• Ensure width represents value: The width of each funnel segment must be directly proportional to its value. Most charting tools handle this automatically, but it’s a foundational principle of the visualization.
• Label clearly: Each stage should be clearly labeled. If space allows, include three key pieces of information for each stage: the stage name, the absolute number, and the stage-to-stage conversion rate.
• Use color purposefully: Use a single color with varying shades or a distinct color for each stage. Avoid using colors that are too similar or jarring. The goal is to differentiate stages, not distract the viewer. Ensure your color scheme works in both light and dark modes and has sufficient contrast.
• Show both numbers and percentages: Always display the overall conversion rate and the stage-to-stage conversion rates. These percentages are often more insightful than the raw numbers alone.
• Optimize for all devices: For digital dashboards, ensure your funnel chart renders well on mobile devices. Use interactive tooltips to provide more detailed data on hover without cluttering the main view.

Limitations and when not to use funnel charts

Knowing when not to use a funnel chart is as important as knowing when to use one. Their simplicity is a strength, but it's also a limitation.

Avoid funnels in these situations:

• Comparing multiple categories: If you want to compare the conversion funnels for different marketing campaigns or user segments (e.g., iOS vs. Android), creating two separate funnel charts side-by-side is better than trying to cram the data into one. For this, a grouped bar chart is often a superior alternative.
• Non-sequential or branching paths: If users can enter the process at different stages, skip steps, or move back and forth, a funnel chart will be highly misleading. For complex user journeys, a Sankey diagram is a much better choice as it is designed to show flow and branching between multiple paths.
• Many stages or small differences: When a process has over 10 stages or the drop-off between stages is very small, the visual differences in the funnel become almost imperceptible. In this case, a simple table or a waterfall chart might communicate the incremental changes more effectively.
• Explaining causality: A funnel chart is a “what” tool, not a “why” tool. It shows you that a drop-off occurred, but not the reason. Never present a funnel chart as a complete analysis; it is the starting point for a deeper investigation. To understand causality, you need to combine it with other analytical methods, such as cohort analysis, user segmentation, or qualitative feedback.

How to create a funnel chart: A step-by-step guide

Building a funnel visualization involves more than just plugging numbers into a tool. You’ll need a thoughtful approach to defining, measuring, and presenting the data.

1. Define your objective and process: What question are you trying to answer? What specific user journey or business process are you analyzing? Clearly map out the essential, sequential stages.
2. Assemble your data table: Gather the raw numbers for each stage. Your output should be a simple two-column table: one for stage names (text) and one for user counts (numeric). For this example, let's use the data from earlier: Visited Homepage: 50,000, Viewed Pricing: 15,000, Started Trial: 3,000, Became Customer: 750.
3. Choose the base population: Define the group of users at the top of your funnel. Are you tracking all website visitors, or only those from a specific campaign? This defines the context for your entire analysis. Here, it's all homepage visitors.
4. Compute conversion metrics: Calculate the stage-to-stage conversion rates and the percentage of the initial population for each step. These metrics are crucial for annotation.
5. Configure the visualization:
   
   • Choose a funnel chart type in your data visualization tool.
   • Map your ‘Stage Name’ column to the dimension/stage axis and your ‘User Count’ column to the measure/value axis.
   • Ensure the chart is sorted according to the logical order of your stages.
6. Customize and annotate:
   
   • Apply clear labels for each stage.
   • Add annotations for absolute numbers and key percentages (stage-to-stage conversion is often the most important).
   • Adjust colors for clarity and brand alignment.
   • Configure tooltips to show supplementary data on hover.
7. Review and validate: Check your numbers and labels for accuracy. Does the visualization accurately reflect the data? Does it clearly show a 70 percent drop-off after the first stage?
8. Share and iterate: Share the funnel chart with stakeholders. Use it as a conversation starter to dig deeper into the problem areas it reveals.

Examples and use cases in practice

You’ve likely seen a funnel diagram before, but let’s look at how to interpret funnel charts in common scenarios at work.

Marketing conversion funnel

A marketing team wants to analyze their new user acquisition flow.

• Stages: Visited Homepage > Viewed Features Page > Started Signup > Completed Signup
• Data:
  
  • Homepage Visitors: 80,000
  • Features Page Viewers: 24,000 (30 percent stage-to-stage conversion)
  • Started Signup: 12,000 (50 percent stage-to-stage conversion)
  • Completed Signup: 9,000 (75 percent stage-to-stage conversion)
• Possible interpretation: The biggest drop-off by far is between the homepage and the features page—a staggering 70 percent of visitors leave immediately. This is the primary bottleneck. The funnel chart makes this instantly obvious. The team should now investigate why the homepage isn't compelling enough to drive exploration.

Sales pipeline funnel

A sales manager reviews the Q3 pipeline.

• Stages: New Lead > Contacted > Qualified > Proposal Presented > Closed-Won
• Data:
  
  • New Leads: 500
  • Contacted: 450 (90 percent conversion)
  • Qualified: 180 (40 percent conversion)
  • Proposal Presented: 162 (90 percent conversion)
  • Closed-Won: 48 (30 percent conversion)
• Possible interpretation: Two major bottlenecks appear. The first is at the qualification stage, where 60 percent of contacted leads are lost. Are the leads low-quality, or is the qualification process too strict? The second is at the final closing stage. Why are 70 percent of deals with proposals being lost? This funnel gives the manager two clear areas to focus on with the sales team.

Key takeaways from Domo’s guide to funnel charts

Funnel charts provide a clear, high-level overview of step-by-step processes, and they make it easy to spot inefficiencies and measure conversion rates from start to finish.

Remember these key points:

• Definition: A funnel chart visualizes the progressive drop-off of data through sequential stages.
• Usefulness: Its primary value is in identifying bottlenecks and measuring stage-to-stage conversion in linear processes like sales, marketing, and recruitment.
• Caveats: A funnel chart shows the “what,” not the “why.” It's unsuitable for non-linear journeys and can be misleading if designed poorly.

Also, recall that the true power of a funnel visualization is realized when you use it as a starting point. Once you’ve identified a bottleneck, your next step is to combine it with deeper diagnostics. For instance, you can segment your data to see if the drop-off is more pronounced for certain user groups, devices, or regions. Or you could combine this quantitative data with qualitative insights from user feedback or surveys to understand the root cause of the friction.