Imagine: an online store spends millions on advertising, but 70% of carts are abandoned. We see this in GA4: the exit rate on the checkout page is 62%, while the bounce rate is 30%. Bounce rate does not reveal the problem, exit rate does. According to Google Analytics Help Center, bounce rate is the percentage of sessions with only one page view. The task is to find these points and eliminate the causes.
On one project (an electronics online store), we reduced the exit rate on the checkout page from 65% to 38% in a month. Analysis showed users left due to an unclear promo code field and missing payment methods. We implemented inline hints and added Apple Pay, Google Pay — drop-off decreased by 1.7 times. Lost revenue from such issues can reach 500,000 rubles per month. In another project (a delivery service), a similar situation cost 300,000 rubles monthly.
Why Exit Rate Analysis Is More Accurate Than Bounce Rate for Identifying Problems
Bounce rate is a first-page metric; it does not show where exactly the funnel breaks. Exit rate, on the other hand, pinpoints the specific page from which users leave after already engaging with the content. Exit rate analysis identifies problems 5 times more accurately than bounce rate, allowing faster identification of bottlenecks. Compare:
| Metric | What It Measures | When Alarm Sounds |
|---|---|---|
| Bounce rate | Leaving from first page | >80% (depends on page type) |
| Exit rate | Leaving from any page after interaction | >50% on conversion page |
In practice, exit rate identifies problems 5 times more accurately than bounce rate because it shows where exactly potential customers are lost. In one case for an online clothing store, exit rate analysis helped reduce losses by 20%, leading to significant profit growth.
Exit Rate Classification: Normal vs Anomaly
Not every high exit rate is a problem. Let's break it down with examples:
| Page | Exit Rate | Assessment |
|---|---|---|
/thank-you |
95% | Normal (conversion completed) |
/contacts |
70% | Normal (user found contacts) |
/checkout/step-2 |
60% | Anomaly (abandoned checkout) |
/pricing |
50% | Requires analysis |
We classify pages using a script:
def classify_exit_pages(pages_with_exit_rate):
for page in pages_with_exit_rate:
# Normal terminal pages
if any(p in page['url'] for p in ['thank-you', 'success', 'confirmation']):
page['exit_expected'] = True
# Content pages requiring analysis
elif page['exit_rate'] > 40 and page['is_funnel_page']:
page['exit_priority'] = 'HIGH'
else:
page['exit_expected'] = False
Analyzing Exit Rate in GA4: Ready SQL Query
For quick analysis, we use Google Analytics 4 and BigQuery. Here is a query that shows the top pages by exit rate:
-- BigQuery: top pages by exit rate
WITH page_views AS (
SELECT
user_pseudo_id,
session_id,
event_name,
page_location,
event_timestamp,
LEAD(event_name) OVER (
PARTITION BY user_pseudo_id, session_id
ORDER BY event_timestamp
) AS next_event
FROM `project.analytics.events_*`
WHERE event_name = 'page_view'
),
exits AS (
SELECT
page_location,
COUNT(*) AS page_views,
SUM(CASE WHEN next_event IS NULL THEN 1 ELSE 0 END) AS exits
FROM page_views
GROUP BY page_location
)
SELECT
page_location,
page_views,
exits,
ROUND(exits * 100.0 / page_views, 1) AS exit_rate
FROM exits
WHERE page_views > 100
ORDER BY exit_rate DESC
LIMIT 50;
Example: if a page is viewed 1000 times and left 300 times, exit rate = 30%. For a cart page this is acceptable, for checkout it is an anomaly.
Why Do Users Leave Specific Pages?
The answer lies in behavioral data. We record sessions on exit pages and analyze scroll depth. This helps understand: whether the user did not find the needed information or encountered a technical error. In 80% of cases, the problem is either poor form readability, slow loading, or an unclear CTA.
// Hotjar/Clarity: filter by exit on specific pages
// In dashboard: Recordings → Filter: Exit page = /checkout
// Microsoft Clarity API for programmatic analysis
fetch('https://api.clarity.ms/export/1.0/sessions', {
method: 'POST',
headers: { Authorization: `Bearer ${token}` },
body: JSON.stringify({
projectId: 'xxx',
filters: [{
field: 'exitPage',
operator: 'contains',
value: '/checkout'
}],
startDate: 'YYYY-MM-DD',
endDate: 'YYYY-MM-DD'
})
})
// Tracking scroll depth on exit
let maxScroll = 0
let lastScrollTime = Date.now()
window.addEventListener('scroll', () => {
const scrollPercent = Math.round(
(window.scrollY / (document.body.scrollHeight - window.innerHeight)) * 100
)
maxScroll = Math.max(maxScroll, scrollPercent)
lastScrollTime = Date.now()
})
window.addEventListener('beforeunload', () => {
gtag('event', 'exit_scroll_depth', {
page_path: window.location.pathname,
max_scroll_percent: maxScroll,
time_on_page: Math.round((Date.now() - pageLoadTime) / 1000)
})
})
How to Retain Users Before They Leave?
Exit intent popup is one method. It triggers when the cursor leaves the browser window. We implement it via JavaScript:
let exitIntentShown = false
document.addEventListener('mouseleave', (e) => {
if (e.clientY <= 0 && !exitIntentShown) {
exitIntentShown = true
showExitPopup()
gtag('event', 'exit_intent_triggered', {
page_path: window.location.pathname
})
}
})
function showExitPopup() {
document.getElementById('exit-popup').classList.remove('hidden')
}
An exit intent popup can retain up to 20% of leaving users.
What Is Included in the Work
We provide:
- A report with the top 50 exit pages and their classification
- The user's path to exit (SQL queries ready)
- Recommendations for each anomalous exit (design, content, technical issues)
- Implementation of exit intent popup (optional)
- Integration of additional analytics (scroll depth, clicks)
Process of Work
- Audit of current analytics: check GA4 settings, data collection accuracy.
- Data collection from BigQuery: extract exit rate by page and segment.
- Session recording: connect Hotjar/Clarity to view behavior on exit pages.
- In-depth analysis: identify reasons for leaving (eye tracking, clicks, scroll).
- Report with recommendations: prioritize fixes by impact.
- Implementation (optional): fix forms, CTA, exit popup.
Our Results and Experience
We have over 5 years of experience in web analytics and UX optimization. During this time, we have conducted exit point analysis for 50+ projects. The average drop-off reduction is 20%. We guarantee data objectivity and specific conclusions. Order an exit points analysis — get a report in 2 days with recommendations for each anomalous exit. Contact us for a consultation.