Post by lizaseo11 on Nov 9, 2024 8:51:10 GMT -2
There are many opinions about the usefulness of behavioral metrics in Google Analytics. Some users note their usefulness, while others believe that they do not play a special role. Some marketers and SEO specialists note that data on user behavior on the site should be taken into account for building a further strategy, and some (for example, specialists from top SEO agencies like SEER Interactive) say that these metrics should not be trusted and paid close attention to.
As in most cases, the truth lies somewhere in the middle. And in today's article, we'll tell you when you should and shouldn't pay attention to behavioral metrics from Google Analytics.
When should data not be used?
1. When they are used to replace conversion rates
If a team demonstrates the success of promotion based on such indicators as viewing depth, bounce rate or time spent by a user on the site, then such data is of little value if the goal is, for example, to increase sales.
The above metrics cannot act as conversion indicators that need to be optimized further.
2. When comparing with irrelevant sites
You shouldn't use a site's behavioral metrics to compare them with similar metrics for sites that fall into a different category.
In our shopify website design previous articles, we looked at examples of different bounce rate indicators and pointed out that the indicators will be completely different for sites of different industries and their types. Thus, for a landing page, a bounce rate of 70% will be the norm, for a commercial site - within 45-55%, and for entertainment blogs, the average conversion is 40% and lower.
The situation is similar with the time the user spends on the page. For e-commerce sites, this indicator does not play a big role and the team does not need the user to stay on the site as long as possible. If a visitor spends not 5-10 minutes on purchasing a product, but 40 minutes, then this only indicates poorly structured interaction.
Therefore, you shouldn’t compare the behavioral metrics of a landing page, a news site, an online store, and a blog.
3. There is no analytics of traffic sources and time period
Google Analytics behavioral metrics will also be useless if analyzed without taking into account traffic sources and time periods.
Users who conduct sessions from mobile phones will “bring” low viewing depth and high bounce rates. Similar indicators will be with traffic received from social networks. This is not an anomaly, but a typical behavior of users on the network.
This is why it is necessary to take into account traffic sources and compare them with behavioral characteristics, as well as conduct analytics not for the current period of time, but for a period of a week, month, quarter, etc.
When should data be used?
1. When compared with relevant competitors (average information for the niche)
If in the second point of the previous section we indicated the inadmissibility of comparison with irrelevant competitors, then in this case we get the opposite picture: data on the behavior of the site's audience can and should be compared with the indicators of relevant competitors.
In other words, comparing data between two landing pages, news sites or blogs will allow you to identify the strengths and weaknesses of your competitor, as well as derive your own strategy based on the data obtained.
2. For conversion funnel analytics
Has the number of registrations increased? Has the number of buyers on the site increased? Check the conversion funnel and determine the stages at which visitors are better converted into leads, and also look at their behavioral characteristics for more in-depth analysis.
In turn, a conversion funnel can be created in Google Analytics.
Funnel1
3. Comparison of indicators for different time periods
In this case, using behavioral metrics can be useful. For example, by comparing the indicators for May and November of the current year, you can draw conclusions about the internal or external reasons for changes in visitor behavior.
For example, if user engagement and conversion increase in the period May-August and the same indicators fall in the period October-March, then we can conclude that there are seasonal fluctuations affecting supply/demand. Another example: a new author appeared on the site, after which the bounce rate increased. After a while, this indicator leveled out - this indicates that the new author adapted to the style and workflow, and the readability of the materials on the blog returns to normal.
Let's draw a general conclusion: Google Analytics behavioral metrics can be use
As in most cases, the truth lies somewhere in the middle. And in today's article, we'll tell you when you should and shouldn't pay attention to behavioral metrics from Google Analytics.
When should data not be used?
1. When they are used to replace conversion rates
If a team demonstrates the success of promotion based on such indicators as viewing depth, bounce rate or time spent by a user on the site, then such data is of little value if the goal is, for example, to increase sales.
The above metrics cannot act as conversion indicators that need to be optimized further.
2. When comparing with irrelevant sites
You shouldn't use a site's behavioral metrics to compare them with similar metrics for sites that fall into a different category.
In our shopify website design previous articles, we looked at examples of different bounce rate indicators and pointed out that the indicators will be completely different for sites of different industries and their types. Thus, for a landing page, a bounce rate of 70% will be the norm, for a commercial site - within 45-55%, and for entertainment blogs, the average conversion is 40% and lower.
The situation is similar with the time the user spends on the page. For e-commerce sites, this indicator does not play a big role and the team does not need the user to stay on the site as long as possible. If a visitor spends not 5-10 minutes on purchasing a product, but 40 minutes, then this only indicates poorly structured interaction.
Therefore, you shouldn’t compare the behavioral metrics of a landing page, a news site, an online store, and a blog.
3. There is no analytics of traffic sources and time period
Google Analytics behavioral metrics will also be useless if analyzed without taking into account traffic sources and time periods.
Users who conduct sessions from mobile phones will “bring” low viewing depth and high bounce rates. Similar indicators will be with traffic received from social networks. This is not an anomaly, but a typical behavior of users on the network.
This is why it is necessary to take into account traffic sources and compare them with behavioral characteristics, as well as conduct analytics not for the current period of time, but for a period of a week, month, quarter, etc.
When should data be used?
1. When compared with relevant competitors (average information for the niche)
If in the second point of the previous section we indicated the inadmissibility of comparison with irrelevant competitors, then in this case we get the opposite picture: data on the behavior of the site's audience can and should be compared with the indicators of relevant competitors.
In other words, comparing data between two landing pages, news sites or blogs will allow you to identify the strengths and weaknesses of your competitor, as well as derive your own strategy based on the data obtained.
2. For conversion funnel analytics
Has the number of registrations increased? Has the number of buyers on the site increased? Check the conversion funnel and determine the stages at which visitors are better converted into leads, and also look at their behavioral characteristics for more in-depth analysis.
In turn, a conversion funnel can be created in Google Analytics.
Funnel1
3. Comparison of indicators for different time periods
In this case, using behavioral metrics can be useful. For example, by comparing the indicators for May and November of the current year, you can draw conclusions about the internal or external reasons for changes in visitor behavior.
For example, if user engagement and conversion increase in the period May-August and the same indicators fall in the period October-March, then we can conclude that there are seasonal fluctuations affecting supply/demand. Another example: a new author appeared on the site, after which the bounce rate increased. After a while, this indicator leveled out - this indicates that the new author adapted to the style and workflow, and the readability of the materials on the blog returns to normal.
Let's draw a general conclusion: Google Analytics behavioral metrics can be use