I Fired the SMM Department for a Week and Left Only Traffic Flow — What Happened?

Introduction: The Gist of the Experiment

We decided to conduct a bold experiment: for a whole week, we completely gave up the services of our SMM department and handed over social media management to an automated system. The goal was to test whether automation could fully replace humans without any loss of efficiency.

The Traffic Flow service was chosen as the main tool, configured to collect content from popular subreddits and various RSS feeds. This content was automatically published on the company's Telegram, VK, and Zen channels. The main metrics for comparison were reach, engagement, number of errors, penalties from platforms, and, of course, resource savings.

The experiment was supposed to answer the main question: is modern automation ready for independent social media management, and is this cost-saving worth the potential risks.

Setting Up the Autopipelines: How Did It Work?

The technical implementation of the experiment required careful preliminary setup. The process of creating automated content pipelines consisted of several key stages aimed at making the quality of automated posts as close as possible to manual ones.

First, thematic sources corresponding to the audience's interests were selected. Then, a filtering system was configured to screen out inappropriate or low-quality materials. Finally, a unique publication template was created for each social network, taking its specific features into account.

The key setup steps were as follows:

1. Source selection. The most relevant and active subreddits, as well as news RSS feeds, were identified to serve as a constant source of fresh content.

1. Content filtering. Complex filters were set up based on keywords, stop words, and post ratings (upvotes on Reddit) to screen out promotional, controversial, or low-quality publications.

1. Platform adaptation. Unique templates for text formatting and media addition were created for Telegram, VK, and Zen to make the posts look native.

1. Publication scheduling. A high posting frequency was set—every two hours—to test its effect on reach.

After the setup was complete, the system was launched in fully autonomous mode.

Results: Reach, Engagement, and Initial Problems

The first few days of the experiment showed an impressive growth in quantitative metrics. By increasing the posting frequency 2-3 times, the total audience reach grew by almost 50%. However, this growth proved to be deceptive, as qualitative metrics began to plummet.

The audience quickly noticed the change. Automatically generated posts often lacked context, contained machine translation errors, or simply did not match the communication style subscribers were used to. This led to a sharp decline in engagement: the number of likes, comments, and reposts dropped by more than half.

It became clear that chasing the quantity of publications and reach without maintaining quality leads to a loss of audience loyalty. The system could not replace human intuition and contextual understanding.

Errors and Penalties: The Inevitable Failures of Automation

By mid-week, more serious problems than just a drop in engagement began to appear. Despite the filters, the system started making critical errors that directly harmed the company's reputation.

The algorithms couldn't always correctly determine the tone or appropriateness of the content. As a result, posts on controversial topics, incorrectly translated memes, or news that looked like clickbait appeared in the feed. This triggered a wave of negative comments and unfollows.

• Incorrect translation: Automatic translation from English often distorted the meaning beyond recognition, turning useful information into an incoherent set of words.
• Broken links and media: The system published posts with broken links or images that failed to load.
• Content duplication: Due to a glitch in one of the pipelines, the same post was published three times within an hour.
• Inappropriate content: The filters missed several posts containing hidden advertising that did not match the community's theme.

The platforms' reaction was swift. The algorithms of VK and Zen classified the high frequency of posts with external links as spam activity. The Zen channel received a warning, and the reach of posts on VK was artificially reduced.

Conclusion: Savings vs. Common Sense

Based on the results of the week-long experiment, it became clear that completely replacing an SMM department with an automated system is impractical and even harmful at the current level of technology. Although formal savings on employee salaries were achieved, the potential losses from audience decline and reputational damage were disproportionately higher.

Automation is a powerful tool, but only in the hands of a specialist. It can handle routine tasks: finding ideas, posting at specific times, and collecting analytics. However, the creative component, content adaptation, community interaction, and quality control must still be performed by a human.

The final conclusion is simple: the optimal strategy is a hybrid approach. Using services like Traffic Flow to assist an SMM manager with routine operations can significantly boost their efficiency, but attempting to completely remove the human from the process leads to the degradation of social media presence and loss of audience.