Funnel Analysis: 9 Reports That Show Where Buyers Drop Off

What funnel analysis is and when to use it

A funnel is a path you want people to follow. An online store wants visitors to move from a product page to cart, checkout and payment. A services company wants visitors to move from a landing page to a form start and a form submit. A software company wants sign-ups to reach activation and then payment.

Funnel analysis counts people at each step and compares the steps. If 10,000 people view a product, 1,200 add it to the cart and 300 buy, the overall conversion is 3%. The step from product view to cart converts 12%, and the step from cart to purchase converts 25%. Now you know where to look.

Use funnel analysis when you have enough traffic to see patterns, a clear goal and events tracked at each step. If tracking is incomplete, fix that first. Our guide to Google Analytics dashboard examples shows how to set up the events and views you will need.

The funnel analysis formulas

The last row turns a percentage into money. If 5,000 people drop at checkout, 40% of those who reach payment pay and the average order is a set amount, the product of those three gives a realistic upper bound on what a fix is worth. Use it to pick the first fix.

9 funnel analysis reports to automate

These reports answer different questions, and each one can run on a schedule. Run them weekly, send them to the owner of the step and flag any step that moves more than your threshold.

1. The core step-by-step funnel

Question: Where do we lose the most people?

Build: List 4 to 7 steps from first touch to goal. For an online store, the standard GA4 ecommerce events are view_item, add_to_cart, begin_checkout, add_payment_info and purchase. Show users, step conversion and drop-off for each.

Alert when: Any step conversion falls more than a set amount against its four-week average.

Choose it if you need a first overview. Watch for too many steps. Start with the few that matter.

2. Funnel by traffic channel

Question: Which sources bring buyers, not just visitors?

Build: Split the same funnel by channel group: organic search, paid search, social, email, direct and referral. Compare conversion at each step.

A channel with high traffic and a low cart-to-purchase rate may be sending the wrong audience. Pair this with your ad cost to find cost per completed funnel.

Choose it if you spend on several channels. Watch for last-click bias. Decide one attribution rule and state it.

3. Funnel by device

Question: Is the mobile experience losing sales?

Build: Split by desktop, mobile and tablet. Look for a large gap at one step, such as mobile checkout.

A gap at one step usually points to a specific fix, such as form fields, payment options or page speed. Scripted journey tests on a phone-sized browser catch many of these problems early.

Choose it if mobile traffic is a large share. Watch for blaming the device before checking page speed and form errors.

4. New vs returning visitors

Question: Do first-time visitors need more reassurance?

Build: Split by new and returning users. Returning visitors often convert at higher rates, and a gap in early steps can show weak first-visit messaging.

Choose it if you have repeat customers. Watch for mixing in staff and test traffic. Filter it out.

5. Funnel by landing page or campaign

Question: Which entry points work?

Build: Split by landing page group or campaign. Compare the first step conversion, such as landing page view to key action.

This tells you which pages and ads deserve more budget, and which to rewrite.

Choose it if you run campaigns or many landing pages. Watch for small samples. Wait for enough users per page.

6. Time-between-steps report

Question: Where do people hesitate?

Build: Show the average elapsed time between steps. GA4 funnel exploration can show it with a toggle, per Google.

A long gap between cart and checkout suggests people are comparing prices. A long gap on a form may mean the form is too long.

Choose it if your sales cycle has a few days or more. Watch for averages hiding outliers. Look at the spread too.

7. Trended funnel over time

Question: Did a change help or hurt?

Build: Chart step conversion by week for the last 12 weeks, with markers for releases, price changes and campaigns.

This is your before-and-after view. It also shows seasonality so you do not mistake a holiday dip for a bug.

Choose it if you ship site changes often. Watch for judging a change in a few days. Wait for enough volume.

8. Abandonment follow-up report

Question: Who can we win back?

Build: List users who completed a late step, such as add to cart or form start, but not the last one, by day. Feed the list to an email, SMS or retargeting flow. Our guide to customer retention strategies covers follow-up flows for online stores.

Choose it if you capture email addresses before the last step. Watch for messaging too quickly or too often. Test timing and limits.

9. Funnel to revenue and pipeline

Question: Do the leads we get become customers?

Build: For lead businesses, extend the funnel beyond the website: visit, form start, form submit, qualified lead, opportunity and won deal. This needs web analytics and CRM data in one place. Our guides to executive dashboard examples and the CRM-ERP integration guide show how to connect them.

Choose it if form fills are your main goal. Watch for stopping at form submits. A lead that never becomes a customer is not a win.

How to do funnel analysis in Google Analytics 4

GA4 has a funnel exploration template you can use for free. According to Google, you can define up to 10 steps, use events or dimension values as conditions and apply up to four segments. Here is the short path:

1. Open Explore in GA4 and choose the funnel exploration template.
2. Define the steps using events such as page_view for a landing page, add_to_cart, begin_checkout and purchase, or generate_lead for a form.
3. Choose open or closed. In a closed funnel users must enter at the first step. In an open funnel they can join at any step.
4. Add a breakdown such as device category or channel, and add segments to compare groups.
5. Show elapsed time to see how long people take between steps.
6. Switch to a trended funnel to see change over time.
7. Save the exploration, and move the numbers to a dashboard or scheduled report for the team.

Note the limits. GA4 reports a user’s first pass through the funnel in the date range, so repeat purchasers are counted once. Check the tracking in your data before you trust the numbers, and compare totals with your store or CRM.

Three funnel analysis examples to start with

For online stores, funnel work sits beside inventory and retention. See our guides to ecommerce inventory management and Shopify analytics dashboards.

Funnel analysis tools and funnel analytics platforms

What AI can do for funnel analysis

AI helps most after the numbers exist. An agent can read the weekly funnel, find the step with the biggest unexpected change, check recent releases and campaigns, and write a short summary with a suggested next test. It can also group free-text feedback and chat logs by the step where people got stuck.

Keep a person in charge of decisions. Models can find patterns in noise, so ask the agent to show the data behind each claim. Our guides to AI workflow automation tools and building an AI agent for your business explain how to set one up, and our AI automation team can build it on your data.

7 funnel analysis mistakes to avoid

• Tracking events that do not match real steps. Test each event by walking through the funnel yourself.
• Ignoring segments. The average hides the broken device or channel.
• Judging from tiny samples. A step with 20 users can swing wildly.
• Changing many things at once. You will not know what worked.
• Comparing different date ranges and seasons. Use the same weeks last year or a trend.
• Stopping at the website. Add CRM or order data to see real outcomes.
• Reporting without an owner. Each step needs a person who can change it.

Frequently Asked Questions

What is funnel analysis in simple words?

Funnel analysis counts how many people complete each step toward a goal and shows where they drop off. It turns a vague question such as “why are sales low?” into a specific one such as “why do people leave at checkout?”

How do you do a funnel analysis?

Define the steps, make sure each one is tracked, count users at each step, calculate step and overall conversion, split by segments such as channel and device, then pick the biggest drop and test a fix. Repeat weekly.

What is an example of a funnel?

A common ecommerce funnel is product view, add to cart, begin checkout, add payment info and purchase. A lead funnel might be landing page view, form start, form submit, qualified lead and won deal.

What is the difference between a marketing funnel and a conversion funnel?

A marketing funnel describes the customer journey from awareness to purchase, often as stages. A conversion funnel is the measured path of tracked steps. Funnel analysis works on the measured version.

What is a good funnel conversion rate?

It depends on your product, traffic quality and price, so compare against your own history and your segments rather than outside averages. A rising trend and shrinking gaps between segments are better signs than any fixed target.

What is the difference between an open and a closed funnel?

In a closed funnel, users must enter at the first step to be counted. In an open funnel, users can enter at any step. Closed funnels measure a strict path, and open funnels show where people join.

How many steps should a funnel have?

Most useful funnels have four to seven steps. GA4 allows up to 10, but more steps make results harder to read. Include only steps that are real decisions or real obstacles.

What tools can I use for funnel analysis?

Google Analytics 4 is a free starting point. Product analytics tools suit apps, experience analytics tools show why people drop, and BI tools join web, ad and CRM data. Choose by where your data lives.

How often should I review my funnel?

Review the main funnel weekly and look at trends monthly. Set alerts for large step drops, so a broken form or checkout is found in days rather than at month end.

Can AI do funnel analysis?

AI can summarize funnel reports, flag unusual drops, compare them with recent releases and group customer feedback by step. It supports an analyst rather than replacing one, so ask it to show the data behind each claim.

Bottom Line

Funnel analysis is a simple habit with large payoffs: define the steps, measure step conversion, split by segment and fix the biggest leak first. Automate the weekly report and alerts so problems are found while they are small.

If you want funnel reports built into your dashboards, explore our business intelligence dashboard services or our AI automation team.

Vikram Jain

Delivery Manager

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I’m Vikram Jain, a Delivery Manager, Account Manager, and Senior Technical Project Manager with over 19 years of experience in the IT industry. (PMP, PSM I, Google Certified Project Manager). I specialize in requirement gathering, stakeholder management, pre-sales, and delivering scalable solutions across industries including eCommerce, CRM, and consumer goods. I have led cross-functional teams and managed multiple projects simultaneously, ensuring timely and high-quality delivery. I’m passionate about building strong client relationships, mentoring teams, and driving strategic growth through effective planning and execution. My focus is on aligning business needs with technology solutions to deliver impactful and measurable results.