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The same applies to ad copy in performance channels. These are the types of pages that people naturally jump to. Let me elaborate on the third point. Imagine you are searching for contact information for a company. Click on "{company} contacts" on Google and write an email or call. This page had everything I needed, but I probably would have bounced back. There are also categories of pages that naturally generate bounces but Australia Phone Number Data still satisfy users. Let's consider the recipe. You usually search for them when you need them. Even if they were linked, you probably wouldn't jump from a carbonara recipe to a pizza dough recipe.
All you have to do is boil the pasta. You should always think about the actual content on your page and why users are visiting it. But at the end of the day, you're still doing quantitative analysis. Gain more insight by analyzing real user behavior. The topic of qualitative analysis is discussed in more detail at the end of this article. Overall, these tips apply to any metric, not just bounce rate. You need to understand how they are measured, what they actually mean, and use them in the appropriate context. What is a good bounce rate? The general consensus is that a bounce rate of 40-60% is considered average, so anything less than 40% is considered good. However, there is no evidence or basis behind these numbers. In reality, there is no such thing as a universally good bounce rate. With many marketing channels and multiple stages of the customer journey, bounce rates vary widely between landing pages and their traffic sources.
For example, here's the performance of Google's Merchandise Store homepage broken down by marketing channel: The bounce rates for “google/cpc” and “partner/affiliate” differ by 36 percent, or 133%. And there are even bigger disparities. If we look at things the other way around, we can see how bounce rates for landing pages vary for each specific traffic source. Here, the Google/Organic bounce rate for the 10 most visited landing pages varies between 35% and 85%. What about take-home? Forget about X% being good and Y% being bad. As shown earlier, it's important to look at your data from the right angle.
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