Social media marketing is now a must in the advertising strategies of almost every small, medium, and big company in the world. Whenever a new social platform emerges, it provides brands with new opportunities to increase their brand awareness. The pace, size, and variety of advertising techniques on social networks are so huge that companies are recruiting numerous employees to handle them.
Even the smartest social media marketers can’t handle all the required information when monitoring social media performance and making the right decision. That’s why more and more businesses are utilizing artificial intelligence technology to monitor social media trends, target relevant audiences, and recognize customers’ demands. In this post, you will learn how machine learning can provide marketing strategists with a smart method for advertising on social media.
Basic Principles of a Great Social Media Strategy
First, it’s good to know what a social media marketing strategy is. This is a typical social media strategy that marketers use to grow their business on social networks:
- Setting S.M.A.R.T goals
- Defining the target audience
- Choosing the best channels
- Generating content
- Engaging with audiences
- Promoting your content
- Analyzing your performance
As you can see, many stages of this process need big data analysis. For example, targeting a relevant audience is an important component of a social media strategy. This stage will affect all the other stages in your strategy, especially content marketing. You can’t generate relevant content without knowing your audience and their characteristics.
Imagine your audience are Gen Z; you can’t generate old-fashioned social media posts for them and expect a great conversion. That’s exactly why you need a smart audience recognition and targeting approach. Machine learning has recently revolutionized the audience targeting process, and you need to include it in your plans.
Many other stages in a social media marketing strategy need advanced methods for recognizing patterns and finding the best response. Machine learning is definitely the solution.
What is Machine Learning?
Machine Learning is the method of making machines learn systems behaviors and act accordingly, just like humans do. This process usually happens by feeding computer data and information about the system and an algorithm for learning. In fact, with machine learning, computers can model the performance of real systems and react based on the model. With advanced processing technologies and machine learning methods, computers can process a large amount of data within seconds and recognize the patterns.
Artificial Neural Networks are now vastly used for machine learning and have been revolutionized in recent years. Deep learning using ANNs provides researchers with the unprecedented ability to make machines smart. This is a powerful technology for modeling complex systems and has applications in almost every field like the economy, engineering, meteorology, etc. So this technology worth considering. As a result, $28.5 billion was allocated to machine learning projects worldwide only during the first quarter of 2019. Around 75% of companies worldwide believe machine learning will revolutionize future industries and job markets. Of course, marketing and advertising are not exceptions and can be benefited from this technology.
Benefits of Machine Learning to Social Media Marketing
Now, let’s review some beneficial aspects of using machine learning in social media marketing.
#1 – Sentiment Analysis
Sentiment analysis is the process of analyzing audiences’ comments to recognize negative, positive, and neutral intents. The results help brands figure out how their customers feel about their products/services. Customer services should apply sentiment analysis and react accordingly. Of course, sentiment analysis will be a time-sucking task when the number of audiences increases. Machine learning can tackle this problem properly. Sentiment analysis tools can be trained with emotions in sample texts to be able to recognize the intents of new inputs.
#2 – Mention Monitoring
As a digital marketer, you should appear wherever your niche keywords are mentioned. This is not possible unless you use machine learning. For example, if you’re active in the food industry, you can train the neural network to recognize whenever words related to food are mentioned. Then, you can interact with those social posts and redirect users to your profile.
