Social media content analyse

Optimize the impact on social media

 

Why social media content analysis is so important

Social media content analysis is the process by which data from social networks are collected and analyzed. Think of social networks such as Instagram, LinkedIn, Facebook and Twitter. Many marketers make use of this and that is certainly not without reason. This is because online conversations about specific products and services are recorded. The same applies to online conversations about sectors, but also specifically about companies, government institutions and organizations. You can read below why this form of analysis is indispensable today and how it is applied exactly.

Social media content analysis goes much deeper than Google Analytics

Almost everyone is curious about the interaction that takes place between a visitor and the content pages of a website. Google Analytics provides insight into this through the measurement methods 'average time on a website' and / or 'average time on a content page'. These measurement methods therefore give you a global insight into the total time a visitor has spent on a website or a specific content page. Yet this is not very reliable. For example, you would be unwise to draw conclusions from this. You can do this with the analysis of social media content. And how.

Use content analysis

You gain more insight into - and information about - the interactions on websites and website pages by using content analysis. In fact, the main purpose of content analysis is the interaction with the content. In addition, you can extract a variety of insights from the specific data that you generate with social media content analysis. The script variables of content analysis are much more specific in comparison to the known measurement methods of Google Analytics. Often this is exactly what companies, government institutions and organizations are looking for. A content analytics script is used for this.

Content analysis and creating a script

The basis of content analytics lies in the creation of a script. These scripts are often based on the research into active contact engagement of visitors to a website or website page. The main goal is to formulate a measurement method that provides better insight into the interaction that takes place actively with the website or website page in question. The specific interaction of a user or visitor on a website or website page can be measured based on a number of specific points. These points vary from pressing a mouse button to scrolling through a page.

Mousedown, mousemove, keydown and scrolling

Examples of points that are measured in social media content analysis are mousedown, mousemove, keydown and scrolling. With mousedown, measurements are made when a mouse button is pressed by the user or visitor. With mousemove, it is measured whether the mouse moves over a specific website or page. In addition, the keydown can also be measured. The keydown measures when a specific key is pressed. Finally, there is the well-known example of scrolling. As the name suggests, the user or visitor scrolls on the website or the page and this is also measured.

The number of seconds

As soon as each of the points mentioned above is detected in social media content analysis, the number of seconds at which this takes place is added together. With some programs, this total time of interaction is sent to Google Analytics every 15 seconds. Suppose that there is a 5-second delay between the various interactions of the user or visitor, then this is classified as an 'inactive user or visitor'. Once the interactions have been forwarded, the timer for these programs starts counting again. This process is then repeated until a user or visitor actually closes the website or the website page.

Opening another tab

The moment a user or visitor opens another tab is an example of a signal that he or she is no longer active. But suppose that this same user or visitor seeks interaction with the relevant website or website page - or returns - then the content script starts counting again. In this case too, the social media content analysis continues until the moment that this user or visitor closes the relevant website or website page. For correct forwarding to Google Analytics, certain things must be created for this.

The Google Tag Manager and Google Analytics

First of all, a modified HTML tag must be created. This is created in the Google Tag Manager. The script must then be placed in this. The HTML tag must also be directed to the page view. This is also called 'DOM Ready'. Only when actual interactions with a website or website page take place should the script begin to take effect. It is also necessary to send a specific event to Google Analytics. This requires a trigger in the form of a modified event.

The custom event

The modified event, or the 'nonldle', is placed in the dataLayer from the content script. This takes place as an event. This way it can be taken care of by the Google Tag Manager. In addition to the event, the time is also pushed to the dataLayer. As you might have expected, this can also be picked up by the Google Tag Manager. A variable for a layer of data is used for this. This is referred to as 'variable configuration' within the social media content analysis. This variable configuration is then used in a modified JavaScript variable. The number of seconds measured by the content script is thus obtained.

Total Engaged Time

The step that follows to set up the event in the Google Tag Manager is to set a number of things in Google Analytics. First, an adjusted statistic is created. The name of this adjusted statistic is the Total Engaged Time. The range of these must be set to 'Hit' and the relevant format type is 'Time'. It is important at this step to note the index number that appears with the adjusted statistic. This must then be filled in again at the event that is created in the Google Tag Manager.

The calculated statistic

Based on this adjusted statistics, a calculated statistic can be made for social media content analysis. This means that the total interactive time of a user or visitor is shared by the page views. With this, the average interactive time of a user or visitor per website or website page is calculated and known. As soon as the script and the trigger have been placed and the adjusted statistic and variable have been created, the desired is put together briefly. Once all tags are put live, data comes in Google Analytics.

Report adjustments in Google Analytics

The question that then arises with social media content analysis is: how are all these data made transparent? For this purpose - as soon as the data arrives - a customized report can be created in Google Analytics. Various statistics and metric types of measurements are displayed. These vary from statistical groups to dimensions. An additional filter can also be created in the form of specific content website pages. Examples of statistics groups are website and website page views, the Total Engaged Time and the average engagement per page. The statistic used for this is the Total Engaged Time divided by the number of website views or website page views. In addition, the bounce rate can also be reported. The dimensions here are the pages themselves.

Specific pages

If desired, this form of social media content analysis can also be used to only show specific web pages in the report. You should think of recipes, service pages but also blog pages and forums. Normally the results are sorted by the number of web page views. In addition, however, it is possible to sort this table based on Average Engagement. The how-to lends itself to switching to both content engagement and Average Engagement. This shows the average time per website page.

De Average Engagement

By sorting web pages by Average Engagement in social media content analysis it can be traced on which specific website pages the user's visit was active on. With the additional information about the bounce percentage and the script with the Average Engagement and the Total Engaged Time, the right conclusions are drawn. If this information were not available, then the wrong conclusions could be drawn. For an above-average bounce percentage, an additional analysis is highly desirable and the Average Engagement and Total Engaged Time provide additional insights into this.

When analyzing, don't just look at some statistics or dimensions

What is particularly important when analyzing the content of social media is the scope. Do not limit yourself to a few statistics or dimensions. If you take the Average Engagement as a starting point, then all kinds of statistics and dimensions can be added. These give the data much more value. You should think here of the number of pages per chosen session, the medium or source, the bounce percentage, but also adjusted dimensions. For example, it is an option for blogs to include the author's name or publication date.


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