Bounce Rate

Bounce rate is the percentage of visitors who arrive at a page on your site and leave without taking any further action: they do not click another link, fill out a form or navigate to a second page. In practical terms, it is the share of single-page sessions out of the total sessions that URL received.

The formula is straightforward: bounces / total sessions × 100. If a landing page gets 1,000 visits and 650 people leave without touching anything, the bounce rate is 65%. It is one of the most closely watched behavioral metrics in web analytics because it works as a gauge of relevance: when most people leave right away, there is usually a mismatch between what the user expected and what they found.

As a digital product metric, it lives within the analytics dashboard that Metrix consolidates and explains, where it is cross-referenced with other signals (traffic source, device, time on page) so that an isolated number does not lead to the wrong conclusions.

How it is calculated and what sets it apart from exit rate

Bounce rate only measures people who land on a page and leave the site from that same page, without triggering a second event. It is easy to confuse with exit rate, but they are not the same. Exit rate is the percentage of people who end their visit on a URL, regardless of how many pages they viewed before; a bounce requires that URL to be both the entry page and the only page in the session.

One key detail: in Google Analytics 4 the concept changed. GA4 replaced the classic metric with engagement rate and defines bounce rate as its inverse, that is, sessions that were not "engaged" (under 10 seconds, no conversion event and no second page viewed). That is why the same site can show very different numbers depending on whether it looks at the old Universal Analytics or GA4.

Why it matters for a business

A high bounce rate is not automatically bad, but it is a warning sign worth investigating. It can point to content that does not match search intent, a slow page, a confusing design, poorly segmented traffic or a mismatch between the ad and the landing page. When you are investing in advertising, it matters twice as much: you pay for every click and, if those visitors bounce, that budget evaporates without moving anyone down the funnel.

Common mistakes when interpreting it

• Reading it without the context of the page type: an informational blog or a single-article post naturally "bounces" a lot, and that can be perfectly healthy.
• Forgetting that a poor measurement setup inflates or tanks the number (events that fire false interactions, or duplicate tags).
• Comparing it across devices without segmenting: mobile bounce rate is almost always higher than desktop.
• Treating it as a goal in itself instead of as a diagnostic pointing toward a real business metric.

A concrete example (Latin America / Argentina)

An e-commerce company in Buenos Aires launches a Google Ads campaign for "running shoes" and sends all the traffic to its home page instead of to the specific category. The bounce rate for that paid traffic climbs to 78%: people were looking for a specific product and landed on a generic page. After redirecting the ads to the right category and improving mobile load speed (where 70% of the visits came from), bounce rate drops to 49% and the conversion rate rises accordingly. The bounce number did not solve anything on its own: it was the clue that showed where the leak was.

What counts as a "good" bounce rate

There is no universal value; it depends on the type of site. A single-page landing page tends to show a higher bounce rate than an e-commerce or services site, where visitors usually keep browsing. What matters is not the absolute number but its trend and the comparison between comparable segments (same channel, same device, same page type).

Ultimately, bounce rate is a diagnostic metric, not a final outcome. It becomes valuable when it is read within a dashboard that connects it to the traffic source, subsequent behavior and conversion, instead of being treated as a standalone number that triggers hasty conclusions.