What Is a Funnel Chart & When Should You Use One? (Guide)

Author's Note

Hi, I'm Paloma, founder of Dashrendr. Early in my career, building my first SaaS, I became obsessed with user onboarding. We had signups, but a fraction of them were reaching the "aha!" moment. It felt like shouting into a void. Implementing our first crude funnel chart was a revelation; we could finally see exactly where users were dropping off, and it was the first step to fixing a leaky bucket that was costing us customers.

Disclosure: This article is published by Dashrendr. Where Dashrendr is relevant, I say so directly — including its limitations.

What Is a Funnel Chart? A Developer's Definition

A funnel chart is a data visualization that shows the progressive reduction of data as it passes from one phase of a process to another. The chart takes its name from its shape, which typically resembles a funnel — wide at the top and narrow at the bottom. This visual metaphor is incredibly effective for highlighting drop-offs in a sequential process. For a developer, the key takeaway is that funnel charts are for visualizing flow and conversion through a predefined path.

To understand a funnel chart, you need to grasp two core concepts:

• Process Stage: This is a discrete step in a sequence. For a SaaS user onboarding, stages might be 'Signed Up', 'Created Project', 'Invited Teammate', and 'Subscribed'.
• Conversion Rate: This is the percentage of users or items that successfully move from one stage to the next. Calculating and visualizing this is the primary goal of a funnel chart.

At Dashrendr, our dashboard builder uses ECharts, which provides a powerful and customizable funnel chart component. This allows you to quickly represent these flows without writing complex D3.js code from scratch. The focus is on showing quantities at each operational stage, making it a go-to for business and product intelligence.

When to Use a Funnel Chart in Your SaaS Application

Funnel charts aren't just for sales teams. For SaaS businesses, they are critical diagnostic tools for understanding user behavior and optimizing key workflows. According to a study by Invesp, the average eCommerce conversion rate is just 2.86%, highlighting how crucial it is to identify and fix leaks in any conversion process. While not every SaaS has a traditional cart, every SaaS has conversion funnels.

Common SaaS use cases include:

• User Onboarding Flow: Track users from signup to the point of activation. A funnel chart immediately shows the biggest drop-off points in your onboarding, helping you identify confusing UI or steps that need improvement.
• Sales Pipeline Tracking: Visualize the journey from 'Lead' to 'Contacted', 'Demo Scheduled', and 'Closed-Won'. This is the classic sales funnel, and it's essential for any SaaS with a sales-assisted motion.
• Marketing Campaign Conversion: Follow users from an ad click or landing page visit through to a signup or download. This helps marketing teams understand campaign effectiveness and optimize their spend.
• Feature Adoption Sequence: If using a new feature requires a few steps, a funnel chart can show you how many users start the process versus how many complete it. This provides valuable feedback on feature discovery and usability.

Funnel Chart vs. Bar Chart: What's the Difference?

A common point of confusion is whether to use a funnel chart or a simple bar chart. They can seem similar, as both represent quantities with bars. However, their applications are fundamentally different, and choosing the right one is crucial for clear data storytelling. A bar chart is used for comparing discrete, independent categories. A funnel chart is for showing flow through connected, sequential stages.

Here's the key distinction:

• Bar Chart: Use it to compare metrics that don't have a required order or relationship. For example, 'Monthly Active Users by Country' or 'Number of Subscriptions per Pricing Tier'. Each bar is a standalone data point.
• Funnel Chart: Use it exclusively for a process where 100% of the items in a lower stage must have come from the stage directly above it. The relationship between the bars is the most important part of the visualization.

The key architectural takeaway is this: a bar chart visualizes static, comparative values, while a funnel chart visualizes the health and efficiency of a dynamic process. Using a bar chart for a sequential flow hides the critical context of drop-off and conversion between stages.

If you're unsure, ask yourself: "Does the order of these data points matter?" If yes, and it's a reduction process, a funnel chart is almost always the right choice.

How to Model Data for a Funnel Chart

This is where things get practical for developers. A beautiful chart is useless without correctly structured data. To create a funnel chart, you don't need a firehose of raw event data. You need a simple, aggregated dataset.

Your data should be an array of objects (or a similar structure), where each object represents a stage in the funnel and contains two key-value pairs:

• A `name` for the stage (e.g., 'Website Visits', 'Signups', 'Active Users').
• A `value` representing the count of users or items at that stage.

The stages must be ordered sequentially, from the largest value to the smallest. For example:

`[{'name': 'Leads', 'value': 1000}, {'name': 'Contacted', 'value': 600}, {'name': 'Demo', 'value': 200}, {'name': 'Won', 'value': 50}]`

Getting this data often involves writing an aggregation query against your production database (e.g., PostgreSQL, MySQL) or data warehouse (e.g., BigQuery). You'll typically be counting distinct user IDs that have performed specific actions. Dashrendr supports both paths: you can connect directly to databases like PostgreSQL for multi-tenant dashboards and use push-down aggregation for performance, or you can pre-aggregate the data in your backend and push it to us via our REST API for maximum flexibility.

Building a Funnel Chart: Key Considerations

When it comes to implementation, you face the classic developer dilemma: build or buy? Exploring the build vs. buy decision for embedded analytics is critical.

The 'Build' Path:
If you decide to build it yourself, you'll need to choose from one of the many JavaScript charting libraries available. You'll be responsible for writing the data aggregation queries, creating the API endpoint to serve the data, and configuring the charting library on the frontend. This gives you total control but also comes with significant development and maintenance overhead.

The 'Buy' Path:
This is where an embedded analytics platform like Dashrendr comes in. Instead of building from scratch, you use a visual, drag-and-drop builder to create the funnel chart. You connect to your data source (either directly or via API push), and the platform handles the rendering, theming, and interactivity. This is much faster and lets your team focus on your core product.

Here, I'll mention an honest limitation of Dashrendr. We do not yet support cross-database joins within our platform. If your funnel data lives in two completely separate data sources (e.g., user signups are in PostgreSQL but subscription data is in Xero), you would need to combine that data in your own backend before pushing it to a Dashrendr dashboard via our REST API. Direct joins are on our roadmap!

What is OEM Analysis?

As you start building powerful analytics like funnel charts, you might realize that this functionality is valuable not just for your internal team, but for your own customers. This leads us to the concept of OEM analysis. OEM (Original Equipment Manufacturer) analysis, often used interchangeably with white-label embedded analytics, refers to integrating a third-party analytics tool into your own software and presenting it to your users as a native part of your application. As defined by analytics provider Reveal BI, it's about seamlessly integrating BI products into SaaS offerings.

Instead of just using a funnel chart to track your own onboarding, you could provide a funnel chart *to your customers* so they can track the effectiveness of the marketing campaigns they run through your platform. This transforms analytics from an internal cost center into a premium, revenue-generating feature.

What is an example of OEM software?

The perfect example of OEM software in this context is an embedded analytics platform. Dashrendr is an example of OEM software. Our customers (SaaS teams) purchase Dashrendr not to run their own internal business analytics, but to build analytics features *for their customers*. They embed our dashboard builder and charts into their own SaaS applications.

By using an OEM solution like Dashrendr, a SaaS company can offer sophisticated customer-facing analytics, including funnel charts, without the years of development time it would take to build a comparable platform from the ground up. This allows them to get to market faster with a more competitive product.

Why Choose Dashrendr for Funnel Charts?

If you've decided that you need to visualize a process and a funnel chart is the right tool, Dashrendr offers the fastest path for a SaaS developer to get from data to a user-facing dashboard. Our visual-first builder means you can create and configure a funnel chart with no code, connecting directly to your database or pushing data via our simple REST API.

We offer full white-labeling, so the charts look like a native part of your app. And with pricing plans that start at just $6/month, we're built for solo developers and growing SaaS teams, not just enterprises. You can test the entire platform yourself with our 14-day free trial, with no credit card required. Sign up and build your first funnel chart in minutes at dashrendr.com.