Google Analytics: Myth about Bounce Rate

Google Analytics: Myth about Bounce Rate
Google Analytics (GA) has done more than any other platform to bring the practice of data analytics to the center of organizations.

By offering a free-to-use, intuitive solution to businesses of any size, it has provided the promise of full transparency into customer behavior.

Moreover, as part of the broader marketing analytics movement, it has helped shape the language we use daily. This handy guide explains some of the most frequently heard, but at time confusing, terms GA has brought into everyday parlance in the marketing world. Pitch decks and strategy sessions abound with references to “data-driven decisions” nowadays, which is a healthy trend for businesses overall. Beyond the buzzword status, this phrase has attained, it is true that companies that integrate analytics into the decision-making process get better results.

Google reports that business leaders are more than twice as likely to act on insights taken from analytics: As Google continues to improve its offering, with Optimize and Data Studio available to everyone, and an even more impressive list of paid products via the Analytics 360 suite, marketers need to understand the data in front of them. Unfortunately, there are some common misunderstandings of how Google collects, configures, processes, and reports data.

Here’s most commonly misunderstood metrics and features within the core Google Analytics interface. By avoiding these pitfalls, you will enable better decisions based on data you can trust.

What’s Bounce Rate?

Bounce rate is a simple, useful metric that is triggered when a user has a single-page session on a website. That is to say, they entered on one URL and left the site from the same URL, without interacting with that page or visiting any others on the site. It is calculated as a percentage, by dividing the aggregate number of single-page sessions by the total number of entries to that page.

Bounce rate can also be shown on a site-wide level to give an overview of how well content is performing. As such, it makes for a handy heuristic when we want to glean some quick insights into whether our customers like a page or not. The assumption is that a high bounce rate is reflective of a poorly performing page, as its contents have not encouraged a reader to explore the site further.

Bounce rate is at times both misunderstood and misinterpreted: A ‘bounce’ occurs when a user views one page on a site, and a single request will be sent to the Analytics server. Therefore, we can say that Google uses the number of engagement hits to classify a bounced session.

One request = bounced; more than one request to the server = not bounced.

This can be problematic, given that any interaction will preclude that session from counting as a bounce. Some pages contain auto-play videos, for example. If the start of a video is tracked as an event, this will trigger an engagement hit. Even if the user exits the page immediately, they will still not be counted as a bounced visit.

Equally, a user may visit the page, find the exact information they wanted (a phone number or address, for example), and then carry out their next engagement with the brand offline. Their session could be timed out (this happens by default after 30 minutes on GA and then restarts) before they engage further with the site.

In either example, this will be counted as a bounced visit. That has an impact on the Average Time on Page calculations, of course. A bounced visit has a duration of zero, as Google calculates this based on the time between visiting one page and the next — meaning that single-page visits, and the last page in any given session, will have zero Time on Page.

Advances in user-based tracking (as opposed to cookie-based) and integration with offline data sources provide cause for optimism; but for now, most businesses using GA will see a bounce rate metric that is not wholly accurate. All of this should start to reveal why and how bounce rate can be misinterpreted.

First of all, a high bounce rate not always a problem. Often, users find what they want by viewing one page, and this could be a sign of a high-performing page. This occurs when people wish to precise information, but can also happen when they visit a site to read a blog post.

Moreover, a meager bounce rate does not necessarily mean a page is performing well. It may suggest that users have to dig deeper to get the information they want, or that they quickly skim the page and move on to other content.

How to avoid this?

Marketers should never view the bounce rate as a measure of page quality in isolation. There is no such thing as a ‘good’ or ‘bad’ bounce rate in a universal sense, but when combined with other metrics we can get a more definite sense of whether a page is doing its job well.

Tools like Scroll Depth are great for this, as they allow us to see in more detail how a consumer has interacted with our content.

We can also make use of Google Tag Manager to adapt the parameters for bounce rate and state, for example, that any user that spends more extended than 30 seconds on the page should be discounted as a bounce. This is useful for publishers who tend to receive a lot of traffic from people who read one post and then go elsewhere.

This is commonly known as ‘adjusted bounce rate,’ and it helps marketers get a more accurate view of content interactions. Read about how to implement this: How to implement Adjusted Bounce Rate (ABR) via Google Tag Manager.

Bounce rate can be a handy metric, but it needs a bit of tweaking for each site before it is truly fit for purpose.

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