Traffic is increasing, but sales are not. There is always a reason for this. Somewhere between attracting users and converting them, a bottleneck is causing users to drop off.
Funnel analysis is a method for visualizing where and why users drop off. It helps you quantify which areas need improvement, so you can make changes based on data rather than intuition.
As of 2026, improvements to GA4's funnel exploration reports, the growing adoption of AI-powered analytics tools, and the increasing importance of first-party data have made funnel analysis easier to put into practice and more directly tied to results than ever.
This article covers the fundamentals of funnel analysis, specific setup procedures in GA4, a comparison of 9 leading tools, and examples of improvements across industries.
What is funnel analysis? Fundamentals and benefits
Funnel analysis represents the process from a user's first interaction to the final goal (conversion) as a funnel, revealing the drop-off rate at each stage. This lets you quantify the bottlenecks that need improvement.
The basic structure of a funnel
A funnel represents the process from a user's first interaction to the final goal (conversion) in the shape of a funnel. It is wide at the top, where many users enter, and narrows toward the bottom as users drop off at each stage.
Funnel stage | User's situation | Examples of relevant tactics |
|---|---|---|
Awareness | Recognizes a problem or need and starts gathering information | SEO, social media, display ads |
Interest | Becomes aware of your product or service and takes an interest | Content marketing, email newsletters |
Consideration | Compares competitors and considers a purchase | Case studies, whitepapers, webinars |
Action | Decides to purchase, sign up, or sign a contract | Landing page optimization, CTA improvements, retargeting |
Retention and advocacy | Makes repeat purchases and recommends the product to others | Onboarding, loyalty programs |
What funnel analysis reveals
Funnel analysis clarifies the following 3 things.
Identifying bottlenecks
Quantifying the drop-off rate at each step shows you at a glance where the biggest problem lies. For example, if the drop-off rate from "Product pages -> Add to cart" is 70%, the product page is likely not persuasive enough.Estimating the impact of improvements
You can simulate how a hypothetical 10% improvement in the drop-off rate at the bottleneck stage would affect final conversions. This lets you prioritize improvements objectively.Understanding behavior patterns by segment
Comparing funnels by segment, such as new users vs. returning users, smartphones vs. desktops, or traffic channel, helps you find problems specific to particular user groups.
Businesses that benefit from funnel analysis
Business type | Example funnel | Metrics to watch |
|---|---|---|
Ecommerce site | Product listing -> Product details -> Add to cart -> Initiate checkout -> Purchase complete | Cart abandonment rate, checkout completion rate |
B2B SaaS | Landing page visit -> Request catalog -> Demo request -> Sales meeting -> Contract | MQL-to-SQL conversion rate, sales meeting conversion rate |
Media site | Article view -> Sign up -> Paid plan sign-up | Sign-up rate, free-to-paid conversion rate |
App | Install -> Launch -> Completed the tutorial -> First action -> Payment | Day 1/Day 7 retention rate, paying user rate |
Funnel analysis in GA4: From setup to analysis
"Funnel exploration" in GA4's "Explore" feature is a report that visualizes user behavior step by step and lets you analyze drop-off and completion rates between steps. Event-based measurement lets you track detailed user actions as well as page navigation.
Overview of GA4 funnel exploration
Compared with "Goal funnels" in UA, GA4's funnel exploration offers major improvements in the following areas.
Feature | UA (Goal funnels) | GA4 (Funnel exploration) |
|---|---|---|
Step flexibility | Must be defined in advance | Can be configured freely when creating a report |
Applying segments | Restricted | Any segment can be applied freely |
Step conditions | Page URLs only | Flexible conditions using events, pages, dimensions, and more |
Open/closed funnels | Closed only | Supports both |
Elapsed time display | None | Displays average elapsed time between steps |
How to set up a funnel exploration
Step 1: Create an exploration report
In the GA4 interface, go to "Explore" -> "Create a new exploration" -> Select the "Funnel exploration" template.
Step 2: Define the steps
In the "Steps" section, configure the user actions you want to analyze as a sequence of stages.
Example setup for an ecommerce site:
Step | Event/condition | Description |
|---|---|---|
Step 1 | page_view (page_location = /products/) | View the product listing page |
Step 2 | page_view (page_location = /products/*) | View an individual product page |
Step 3 | add_to_cart | Add a product to the cart |
Step 4 | begin_checkout | Initiate checkout |
Step 5 | purchase | Complete a purchase |
Example setup for B2B SaaS:
Step | Event/condition | Description |
|---|---|---|
Step 1 | page_view (page_location = /lp/) | View the landing page |
Step 2 | form_start (form_name = contact) | Start filling out the contact form |
Step 3 | form_submit (form_name = contact) | Complete the contact form submission |
Step 4 | page_view (page_location = /demo-request/) | View the demo request page |
Step 5 | demo_request_complete | Complete a demo request |
Step 3: Choose the funnel type
Closed funnel: Counts only users who proceed in order from Step 1. Suitable for strictly defined behavior flow analysis.
Open funnel: Also counts users who enter at an intermediate step. Suitable for understanding overall drop-off trends.
Step 4: Apply segments and filters
Segments let you make comparisons such as the following.
New users vs. returning users
Mobile vs. desktop
By traffic channel (organic search vs. paid ads vs. social media)
By region
How to read GA4 funnel analysis and use it to make improvements
Identify steps with high drop-off rates
The funnel exploration report displays completion and drop-off rates for each step in a bar chart. The step with the highest drop-off rate is the bottleneck you should prioritize.
How to estimate the impact of improvements
For example, suppose your current funnel looks like this.
Step | Users | Completion rate | Drop-off rate |
|---|---|---|---|
Product listing | 10,000 | - | - |
Product details | 5,000 | 50% | 50% |
Add to cart | 1,500 | 30% | 70% |
Initiate checkout | 900 | 60% | 40% |
Purchase complete | 630 | 70% | 30% |
In this case, the biggest bottleneck is the 70% drop-off rate from "Product details -> Add to cart." If you could reduce this drop-off rate to 60% (raising the completion rate from 30% to 40%), add-to-cart users would increase to 2,000. If users continued at the same rates, final purchases would reach 840. The calculation shows that just a 10% improvement in the drop-off rate would increase purchases by 33%.
Related reading
What is CV? Metrics and improvement methods for measuring digital ad performance
A detailed explanation of conversion (CV) fundamentals, measurement methods, and ways to improve conversions.
9 funnel analysis tools: Comparing free and paid options
Many tools support funnel analysis, from free options to advanced paid platforms. Choose the tool that best fits your company's size, budget, and analysis needs.
4 free tools
1. GA4 (Google Analytics 4)
GA4 is free to use and supports advanced funnel analysis through funnel exploration reports. Its event-based measurement model lets you define flexible steps that include button clicks and form interactions as well as page navigation.
Strengths | Considerations |
|---|---|
Advanced funnel analysis at no cost | Setup requires knowledge of GA4 |
Highly flexible segments and filters | Some parts of the interface are not intuitive |
BigQuery export supports more advanced analysis | Some limitations in real-time capabilities |
2. Google Sheets
A spreadsheet is sufficient for simple funnel analysis. Use data exported from GA4 or your CRM to manually aggregate and visualize user counts, completion rates, and drop-off rates at each step.
Strengths | Considerations |
|---|---|
Easy for anyone to use | Cannot be automated (manual updates) |
Highly customizable | Not suitable for large datasets |
Easy to share with your team | No real-time analysis |
3. Hotjar
Although Hotjar focuses on heatmaps and session recordings, it can also support basic funnel analysis. Its ability to show visually why users drop off complements funnel analysis based solely on numbers.
Strengths | Considerations |
|---|---|
Provides a visual understanding of user behavior | Limited funnel analysis features |
Useful for understanding qualitative reasons for drop-offs | The free plan limits data volume |
Session recordings let you track individual actions | Intended for use alongside quantitative analytics tools |
4. Microsoft Clarity
Microsoft Clarity is a free user behavior analytics tool from Microsoft. Along with heatmaps and session recordings, it automatically detects "rage clicks," "dead clicks," and other behaviors to help you identify causes of drop-offs.
Strengths | Considerations |
|---|---|
Completely free with no data volume limits | No built-in funnel feature |
Automatically detects rage clicks and similar behaviors | Intended for use alongside GA4 or similar tools |
Offers GA4 integration | Primarily for qualitative analysis |
5 paid tools
1. Mixpanel
Mixpanel specializes in product analytics and offers extensive funnel analysis features. Event-based data collection lets you break user behavior into detailed steps and easily compare segments and time periods.
Strengths | Considerations |
|---|---|
The most extensive funnel analysis features | Initial setup requires engineering resources |
Supports real-time analysis | Limited Japanese-language support |
Connects with cohort and retention analysis | Higher-tier plans are expensive |
Pricing guide: A free plan is available. Check the official website for details on paid plans.
2. Amplitude
Amplitude is a product analytics tool comparable to Mixpanel. Its particular strengths are processing large datasets and predictive analytics using AI and machine learning.
Strengths | Considerations |
|---|---|
Fast processing of large datasets | Enterprise-level pricing |
AI prediction features, such as churn risk prediction | Steep learning curve |
Strong user journey visualization | May offer more functionality than small teams need |
Pricing guide: A free plan is available. Contact the provider for paid pricing.
3. Adobe Analytics
Adobe Analytics is a large-scale analytics platform for enterprises. Its strengths include advanced segmentation, real-time analysis, and integration with other Adobe products, such as Adobe Target and Adobe Campaign.
Strengths | Considerations |
|---|---|
Enterprise-level analytics features | Requires expertise and an operational structure |
Integration with Adobe Marketing Cloud | Long lead time for implementation and operation |
Extremely customizable | High pricing based on annual contracts |
Pricing guide: Contact the provider (enterprise-level pricing).
4. Kissmetrics
Kissmetrics tracks the entire customer lifecycle and offers funnel analysis linked to lifetime value (LTV). It is suited to ecommerce sites and SaaS companies.
Strengths | Considerations |
|---|---|
LTV-based funnel analysis | Enterprise pricing |
Analysis directly tied to revenue | No Japanese-language support |
Integrated analysis of A/B test results | Somewhat dated interface |
Pricing guide: Contact the provider (check the official website for details).
5. Heap
Heap's "Autocapture" feature automatically records all user interactions on your site. Its main feature is the ability to freely build funnels later without defining events in advance.
Strengths | Considerations |
|---|---|
No advance event design needed (automatic collection) | Data volumes can become very large |
Eliminates the problem of missing data from earlier periods | May affect processing speed |
Funnels can be rebuilt later | Enterprise pricing (contact the provider for details) |
Pricing guide: A free plan is available. Contact the provider for paid pricing.
Tool comparison table
Tool | Pricing | Funnel analysis capabilities | Features | Best suited for |
|---|---|---|---|---|
GA4 | Free | High | Event-based, BigQuery integration | All business sizes |
Google Sheets | Free | Low (manual) | Flexible but manual | Small businesses and early stages |
Hotjar | Free/paid | Low | Complementary qualitative analysis | Teams focused on UX improvements |
Microsoft Clarity | Free | None (supplementary) | Heatmaps and session recordings | Understanding qualitative causes of drop-offs |
Mixpanel | Free/paid | Highest | Specializes in product analytics | SaaS and apps |
Amplitude | Free/paid | Highest | AI predictions, large-scale processing | Growth-stage products |
Adobe Analytics | Paid | High | Enterprise integration | Large companies |
Kissmetrics | Paid | High | LTV-based analysis | Ecommerce and SaaS |
Heap | Free/paid | High | Autocapture | Companies seeking to reduce event design work |
Funnel analysis improvement examples: Results in numbers
The following hypothetical numerical examples show what improvements funnel analysis can support. They explain in detail how to approach a problem, analyze it, and put improvements into practice.
Example 1: Reducing cart abandonment on an ecommerce site
Problem: The drop-off rate from product pages to adding an item to the cart remained high at 72%.
What the analysis revealed:
GA4 funnel exploration showed that the drop-off rate for mobile users was twice that of desktop users (mobile 80% / desktop 40%).
Hotjar session recordings confirmed that the "Add to cart" button on mobile was positioned where users had to scroll to see it.
Improvements:
Changed the mobile "Add to cart" button to a floating button fixed at the bottom of the screen.
Placed the price and CTA immediately below the product image, making everything accessible above the fold.
Clearly displayed shipping information on the product page, which had previously been unavailable until checkout.
Results:
Metric | Before | After | Change |
|---|---|---|---|
Add-to-cart rate (overall) | 28% | 41% | +46% |
Add-to-cart rate (mobile) | 20% | 38% | +90% |
Completed purchases (monthly) | 630 | 920 | +46% |
Example 2: Improving a B2B SaaS company's lead generation funnel
Problem: The conversion rate from landing page visits to catalog requests was low, leaving room for improvement.
What the analysis revealed:
GA4 funnel exploration showed a high drop-off rate of 65% after the form was displayed.
Microsoft Clarity's rage click analysis revealed that the form's "Company sizes" dropdown had no options, causing users to click it repeatedly.
Improvements:
Reduced the form from 12 fields to 5 (name, email, company name, job title, and a multiple-choice question about challenges).
Changed the process to collect "Company sizes," "Number of Employees," and "Budget" during follow-up after sending the requested materials.
Added "Complete in 30 seconds" microcopy above the form.
Results:
Metric | Before | After | Change |
|---|---|---|---|
Form reach rate | 18% | 22% | +22% |
Form completion rate | 23% | 48% | +109% |
Landing page -> Catalog request conversion rate | 4.2% | 10.6% | +152% |
Monthly leads | 84 | 212 | +152% |
Example 3: Improving an app's onboarding funnel
Problem: Tutorial completion after installation was low, and Day 7 retention also remained low.
What the analysis revealed:
Mixpanel funnel analysis showed that the most drop-offs occurred at the tutorial's third step, profile setup.
There were 8 required fields, and a profile image was also required.
Improvements:
Reduced the number of required tutorial steps from 5 to 3.
Made "Do this later" the default for profile setup.
Made the first action experience, using the app's core feature once, the highest-priority step.
Results:
Metric | Before | After | Change |
|---|---|---|---|
Tutorial completion rate | 35% | 68% | +94% |
First action completion rate | 22% | 55% | +150% |
Day 7 retention rate | 12% | 28% | +133% |
Related reading
What is CPA? Practical guidance on calculation, improvements, and goal settings
A detailed explanation of CPA, an ad efficiency metric to understand alongside funnel improvements.
5 points for successful funnel analysis
Funnel analysis does not always produce results. These 5 points help you make the entire process, from analysis to improvement, successful.
Point 1: Define steps around user decisions
Define funnel steps around the points where users make decisions, rather than page navigation. For example, "Homepage -> Category page -> Product page" describes page navigation, while user decisions follow "View product -> Add to cart -> Pay -> Complete purchase." The latter provides insights more directly connected to improvements.
Point 2: Improve only one step in a funnel at a time
If you improve multiple steps simultaneously, you cannot tell which changes worked. Identify the single step with the biggest bottleneck, focus on improving it, and then move to the next step.
Point 3: Combine quantitative and qualitative analysis
Use GA4 funnel exploration to identify where users drop off (quantitative), and Hotjar/Clarity session recordings to understand why they drop off (qualitative). Combining the two helps you identify the right improvements.
Point 4: Always compare segments
Knowing only that "the overall drop-off rate is 50%" does not tell you how to improve it. If you know that "mobile is 65% and desktop is 30%," you can decide to focus on mobile UX. Comparisons by device, traffic channel, and new vs. returning users are essential.
Point 5: Keep the PDCA improvement cycle going
Funnel analysis is not a one-time task. Continuously repeat the cycle of implementing improvements -> Measuring results -> Identifying new bottlenecks -> Implementing the next improvements. This gradually improves the efficiency of the entire funnel.
To identify why users are dropping off on particular pages, see Comparing and choosing heatmap tools. For understanding conversion rates at each step, see CVR formulas, interpreting benchmarks, and improvement methods.
For ways to translate an ecommerce purchase funnel into metrics, also see Ecommerce site analysis: Metrics, procedures, and tools.
FAQ
Q. Is funnel analysis useful for small sites?
Yes, it is effective. Funnel analysis works even for small sites with only a few thousand monthly pageviews. In fact, identifying where improvements will have the greatest impact is particularly valuable for small sites with limited resources. However, statistically significant results require a certain amount of traffic, so a longer analysis period is recommended.
Q. How many steps should a funnel have?
Too few or too many steps make accurate analysis difficult. Too few steps hide opportunities for improvement, while too many spread the data across steps and reduce analytical accuracy. Focus only on the points where users make decisions to arrive at an appropriate number of steps.
Q. Should I use a closed or open funnel?
A closed funnel suits processes that follow a fixed sequence, such as an ecommerce purchase flow. An open funnel suits understanding overall trends when users may enter at an intermediate step, such as arriving directly on a product page. Start with a closed funnel for strictly defined flow analysis, then complement it with an open funnel to see the full picture.
Q. What should I do if funnel analysis shows high drop-off rates at every step?
Even when every step has a high drop-off rate, the rule is to select the one with the highest drop-off rate or greatest impact and focus on improving it. Trying to improve everything at once spreads resources too thin and fails to produce results. Prioritize using 3 factors: drop-off rate x ease of improvement x business impact.
Q. Can AI automate funnel analysis?
As of 2026, AI-powered automation of funnel analysis is advancing rapidly. AI agents such as Cascade can automatically run Funnel Analysis and carry out the resulting Actions for ad operations. In particular, real-time bottleneck detection and optimization recommendations enable continuous improvement that was not possible with traditional manual analysis.
Summary: Start making data-driven improvements with funnel analysis
Funnel analysis turns the vague question "Why aren't conversions increasing?" into the specific issue of "At which steps are which segments dropping off, and by how much?"
Here are the key points from this article.
The core of funnel analysis: Visualize drop-off rates at each step and identify the biggest bottleneck.
How to use GA4: In funnel exploration reports, define event-based steps -> Compare segments -> Identify bottlenecks.
How to choose tools: GA4 (free and essential) + A qualitative analytics tool (Hotjar/Clarity) + A paid tool suited to your needs.
How to approach improvements: One step at a time, a combination of quantitative and qualitative analysis, and an ongoing PDCA cycle.
Before increasing traffic, use funnel analysis to visualize and address the opportunities you are missing with your existing traffic. This is the most cost-effective way to improve results.
If you want to make funnel analysis and the implementation of improvements more efficient, an AI agent such as Cascade can automate the analysis and improvement cycle that previously required manual work, enabling more advanced, continuous optimization.
