The new replacement in the Facebook App:
Since the time the most used app called Facebook substituted its old feature of Interest Targeting with Audience Optimization in the year 2016, there had been much gossip and talks about whether the newly introduced feature would make things better regarding the restrictions of its not-so-famous predecessor.
Preferred Audience Optimization on Facebook & Instagram:
After the break out of new feature called the Preferred Audience Optimization, the app made an announcement to the publishers that the new feature of interest tags does not restrict the reach. Furthermore, the social networking app Facebook, which produces more than 10% of all the organic traffic to the online websites of the publishers, guaranteed that the click-through rates will increase with the use of the newly developed interest tags.
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The wrong assumption:
Though there was much excitement initially, no independent and definitive study could make sure that the addition of the interest tags to the organic posts would positively impact the engagement or the traffic. Through the detailed data and information gathered from the pages of the publishers having numerous followers, it has now been found out that the addition of the interest tags does not pave way for the increase in click-through rates. This suggests that the traffic gained by the carefully tagged posts has less probability to click on a link share compared to the traffic gained by the untagged posts.
This research and analysis have been made based on data and information linked with numerous major news publishers in the United States. These news publishers serve varied audiences. These news publishers were Echobox clients who agreed to take part in the study. Names of the publishers can’t be provided in order to preserve their privacy and data. But the participants have been carefully chosen as in the former Echobox studies for ensuring the characteristic results.
Analysis and investigation:
In order to carry out the investigation, a machine learning algorithm was first trained with the help of natural language processing mechanisms. These mechanisms could allocate related interest tags to the posts on the social media. It was done by the algorithm through analysis and understanding of the material of the share message, and the piece being shared. It was built and designed so that the tags can be included in several organic posts, and powering the procedure was the only quick and consistent way to do so.
Results of the investigation:
The results of the investigation were both consistent and clear. Not a single page of the publishers observed a major rise statistically in the click-through rates when the interest tags were incorporated to their posts. This signified that tagging did not fulfill its main goal of directing the posts to an audience more probable to take interest in the content of the publisher. One page also observed a major decline in the click-through rates with the increase in impressions while the traffic was consistent. The results were astonishing. Thus, it was assumed that the algorithm might not have produced interest tags of the high quality.