Use NFTs as a means of loyalty

Before you begin your analysis, it is important to know which date range to choose. The date range can be based on the average time it takes a user to convert or the length of a marketing campaign. It could be a business cycle.

However, for accurate content analysis, we look at two types of date ranges that produce two types of periodic data:

As far as historical data is concerned, one year is enough to determine seasonal factors and which pages are most popular over the long term. In other cases, it may last six months or a year or more.

An important aspect of both trend and historical data analysis is the use of date range comparisons.

You can select dates from any standard GA4 report. Go to the left navigation menu and select Reports . Then click the date range.

These steps mainly apply to these 3 reports

Whichever column you look at, see which numbers stand out and which numbers don’t. Find pages with similar numbers to determine the average, and pay attention to numbers that are above or below the average as these may be conversion opportunities.

For example, a page with high views but low conversion rates may indicate that you’re not meeting users’ expectations. These pages need to residential phone numbers be researched and optimized for conversions (A/B testing can be applied here). With ABC in GA4. Always be sure to ask these two questions:

Do you understand?
So what?
Note: The question “Does this make sense?” does not always have a negative connotation. A few posts ranking higher than expected could mean new content opportunities that your audience might be interested in.

Here is an overview of the content analysis steps in GA4

Phone namber list

In the Pages and Screens report, you can start applying the ABC model to groups of BO Leads content in the first step of content analysis .

While it is helpful to have a general understanding of the results, it is important to note that the current level of aggregation may not provide specific details that can make a significant difference.

The problem with large aggregations is that results (whether positive or negative) are often attributed to the group as a whole rather than to individual contributors.

For example, you might assume that Canada is your most profitable market. But in reality, most of our customers live in certain cities, like Mississauga. Therefore, trying to optimize for all of Canada may not give you the results you want.

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