How AI Helps SMM Specialists


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How AI Helps SMM Specialists
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SMM is one of the few areas where one must constantly switch between different niches: master different skills and tools, communicate with different audiences, and have a broad outlook.

In this case, using neural networks in SMM can not only significantly relieve the employee but also increase the effectiveness of your campaigns, boost conversion, and even improve the effectiveness of your publications. Today, we will discuss the best ways AI solutions can be used in SMM.

Target Audience Analysis

Unfortunately, the development of neural networks has not yet reached the point where they could conduct full-fledged, large-scale research. However, even today, they can give very few valuable clues, such as identifying the target audience’s pains, values, and hobbies, which you described only superficially.

Such an analysis can help you learn the key characteristics of your followers, what products will be most relevant for them now, and what user needs and problems need to be solved today.

AI can also put forward various hypotheses or create avatars for your customers. In the future, this can significantly save time when compiling a CJM (Customer Journey Map, the path a client takes from realizing the need for a product to purchasing it) and a rubricator.

What else can hypotheses from neural networks help with?

  • detailed study of tone of voice;
  • accurately identifying the prevailing preferences of various groups, as well as creating appropriate content formats;
  • adaptation of information.

You can use IBM Watson or Google Cloud Natural Language for this purpose.

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Automatic Response to Requests

Sometimes, especially when a group on a social network is prevalent, managers may be unable to cope with the round-the-clock flow of thousands of incoming messages and comments. It can often make it the most significant expense for the biggest brands. At the same time, support specialists should try to help each user by giving him an answer to his question. This is where neural networks come to the rescue.

Today, there are two standard options in SMM:

The manager copies the comment received from the user and “feeds” it to the neural network to generate a logical answer. After this, the employee corrects the received response, makes it more “alive,” adds essential elements (for example, contact information or a link to a product), and only after that sends the response to the user.

An algorithm that runs in the digital cloud on the social network is connected to the bot via an API. The bot is given a ready-made script: when to start working, what keywords to use, what tone to use when communicating, or what specific information to use when preparing a response. As a result, instead of a support specialist or manager in the chat, users are answered by a “robot.”

Even though neural networks seem unreliable enough to automate the communication process with clients (they can sometimes get confused, respond in too “robotic” language, etc.), some social networks already use AI tools to respond to comments automatically. A great example of this is the well-known Facebook.

If you want to automate this part of your work, try Postus: AI for social media.

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Content Generation

Why were neural networks initially created? That’s right, for writing texts and drawing images. And in terms of SMM, this is an absolute goldmine. Today, it is easier to say what AI cannot create. Here is just an incomplete list of content types that a neural network can help make:

  • headings for articles, posts, and other materials;
  • directly the text of publications for social networks;
  • content plan;
  • Images;
  • video;
  • advertising elements;
  • scripts;
  • concepts and much more.

At the same time, it is worth understanding that no matter how powerful and modern, a neural network cannot 100% replace content producers; they are only valuable for automating processes. So far, AI primarily creates only standard texts “for every day,” and at the same time, still makes numerous errors. 

Social media content generator can become an excellent assistant for making automated postings.

Groups and Communities Analytics

Because ChatGPT and its “brothers” are designed to work with text, they can also be used in various types of analytics. Applicable to the SMM field, this could be a description of groups on social networks, a list of keywords, or other vital information that will allow you to get to the Top quickly.

A neural network can either fulfill such a request from scratch, depending on the specific topic of your group, or help in refining already created community elements.

In addition, an SMM specialist can ask the AI to analyze the entire text. Yes, today, a neural network can cope with such a task. At the same time, artificial intelligence can easily cope with any format, be it SEO text, a post on a social network, or, for example, product cards. At the same time, remember that algorithms are not perfect, so do not forget to double-check the material after it has been “processed” by the neural network.

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Chatbots for Different Purposes

Chatbot is a vital marketing tool in SMM. Its main goals in this area are brand promotion and increasing sales, but there are more. Additionally, chatbots can perform the following purposes that are important for social media marketing:

  • interaction with the audience online;
  • a more personalized approach to each client;
  • reduction in maintenance costs;
  • automation of routine tasks;
  • group moderation;
  • attracting additional traffic.

Before ChatGPT appeared on the scene, only those teams that included programmers could afford to have chatbots. Now, leading neural networks can generate both a piece and a full-fledged fragment of working program code based on which a chatbot will be written.

The main problem is that artificial intelligence can only write simple chatbots, which have been in the public domain for a long time. However, you can try to create a more complex program by generating pieces of code via ChatGPT or its equivalent. This way, you can save serious money on programmer services.

Conclusion

Thus, using neural networks in social media can significantly improve the quality and efficiency of your actions. In addition, remember the importance of the human factor and the usefulness of specialized tools, mastering the latest technologies, and experimenting with different techniques. Such approaches will not only increase your income but also achieve better results in the field of SMM, which will make your work unique and successful.


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henry smith