How to calculate bounce rate

Bounce rate is the percentage of users who leave your page immediately after visiting. It is a popular marketing metric showing the relevance and engagement of content for site visitors.

This tutorial shows you how to calculate bounce rate in PostHog. To get started, you need to install the snippet or JavaScript SDK and enable "record user sessions" in project settings.

How does Google Analytics 4 calculate the bounce rate? It is the percentage of sessions that do not last longer than 10 seconds, have a conversion event, or have at least 2 pageviews or screenviews.

Calculating bounce rate with SQL insights

To calculate bounce rate, we need data from raw_session_replay_events, which we can access with SQL insights. To create a new SQL insight, go to the insight tab, click new insight, then go to the SQL tab. This is where we write our SQL statement.

We count a bounce as a session where the user is active for less than 10 seconds. To do this in SQL, we use a count of sessions (using session_id) where active_milliseconds is less than 10000 and divide by the total session count, then multiply by 100. Together, this looks like this:

select (
    count(
        multiIf(active_milliseconds < 10000, session_id, NULL)
    ) / count(session_id)
) * 100 as bounce_rate
from raw_session_replay_events

This gives us a bounce rate percentage insight we can save, update, and add to dashboards.

Why not use single page visits divided by total visits? The raw_session_replay_events data does not include the frequency of events and the events data does not include session details, making calculating sessions with a single $pageview event not possible (at the moment).

Using different bounce criteria

Although we used active time as our criteria for bounce rate, PostHog has other options. They include using click_count, keypress_count, or mouse_activity_count. We can find these in the database data management tab under the raw_session_replay_events table.

Using different bounce criteria is as simple as changing active_milliseconds < 10000 to the new criteria. For example, if we wanted to count bounce rate as the percentage of sessions with fewer than 3 clicks, we can use click_count < 3 like this

select (
    count(
        multiIf(click_count < 3, session_id, NULL)
    ) / count(session_id)
) * 100 as bounce_rate
from raw_session_replay_events

We can add more criteria to our multiIf statement as well. For example, if we wanted to count bounce rate as the percentage of sessions with fewer than 3 clicks and 2 keypresses or less than 10 seconds, we can use click_count < 3 and keypress_count < 2 or active_milliseconds < 10000 like this

select (
    count(
        multiIf(click_count < 3 and keypress_count < 2 or active_milliseconds < 10000, session_id, NULL)
    ) / count(session_id)
) * 100 as bounce_rate
from raw_session_replay_events

Calculating bounce rate for a specific page

We use a more complicated SQL query to get the bounce rate for a specific page.

1. We get a count of distinct session_id values where the click_count is 0 and the active_milliseconds is 60000.
2. We divide this the total number of distinct session_id values for the page.
3. Use an INNER JOIN to add the events table to get the created_at date and $properties.current_url value.
4. Filter for created_at dates in the last week with the $current_url of a specific URL (in this case, https://posthog.com/).

Altogether this looks like this:

SELECT 
    (COUNT(DISTINCT CASE 
        WHEN (raw_session_replay_events.click_count = 0 AND raw_session_replay_events.active_milliseconds < 60000) 
        THEN raw_session_replay_events.session_id 
        ELSE NULL 
    END) * 100.0) / COUNT(DISTINCT events.properties.$session_id) AS bounce_rate
FROM 
    events
INNER JOIN 
    raw_session_replay_events ON events.properties.$session_id = raw_session_replay_events.session_id
WHERE 
    events.created_at >= now() - INTERVAL 7 DAY 
    AND events.properties.$current_url = 'https://posthog.com/'

This gives a bounce rate percentage for our homepage, and you can edit it for any specific page you want.

Further reading

• Calculating average session duration, time on site, and other session-based metrics
• Using HogQL for advanced time and date filters
• Using HogQL for advanced breakdowns

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