Illustration of a brain with iconography of technology, symbolizing the relationship between humans and technology in social media

Four ways machine learning can enhance social media marketing

Instagram is a global platform where businesses can showcase their products to over one billion total users, of which over 500 million are active on the app at least once a day. Facebook and Twitter also allow businesses to provide customer support and spread the word about upcoming events and sales to a huge audience. In fact, 63% of customers prefer customer support on social media, compared to other avenues like phone or email.

At the same time, artificial intelligence (AI) and machine learning are becoming more integrated in many aspects of social media. AI is far from replacing the human touch, but it is increasing both the quantity and quality of online interactions between businesses and their customers. Businesses can use machine learning in the following four ways to create effective social media marketing strategies.

Social media monitoring

Social media monitoring is one of the more traditional tools for businesses looking to manage their social media accounts. Some platforms like Twitter and Instagram have built-in analytics tools that can measure the success of past posts, including number of likes, comments, clicks on a link or views for a video. Third-party tools can provide similar social media insight and management services, teaching businesses about their audiences, including demographic information and ideal times for posting. Social media algorithms generally prioritize more recent posts over older posts, so with this data, businesses can strategically schedule their posts at, or a few minutes before, the peak times.

In the future, businesses might be able to rely on AI for recommendations about which users to message directly, or which posts to comment on, that could likely lead to increased sales. These recommendations would partly be based on the information gathered through existing analytics tools for social media monitoring.

Sentiment analysis for social media marketing

Sentiment analysis, also called opinion mining or emotion AI, is the practice of judging the opinion of text data. The process uses both natural language processing (NLP) and machine learning to pair social media data with predefined sentiment labels such as positive, negative or neutral. Then, the machine can develop agents that learn to understand the underlying sentiments in new messages.

Businesses can apply sentiment analysis in social media and customer support to collect feedback on a new product, service or design. Similarly, businesses can apply sentiment analysis to discover how people feel about their competitors or trending industry topics.

Image recognition for social media marketing

Image recognition uses machine learning to train computers to recognize a brand logo or photos of certain products, without any accompanying text. This can be useful for businesses when their customers upload photos of a product without directly mentioning the brand or product name in a text. Potential customers might also upload a photo of your product with a caption saying “Where can I buy this?” If businesses notice when that happens, they can use it as an opportunity to send targeted promotions to that person, or simply comment on the post to provide an answer, leading to increased customer satisfaction.

In addition, social media posts with images generally receive higher user engagement compared to posts that are purely text. Facebook users are 2.3 times more likely to like or comment on posts with images, and Twitter users are 1.5 times more likely to retweet messages with images. This is critical given that social media algorithms are usually designed so that posts with high engagement — measured by how many users interacted with a post such as by liking, commenting or sharing that post with other users — show up at the top of user feeds.

Chatbots for social media marketing

Chatbots are an application of AI that mimic real conversations. They can be embedded in websites such as online stores, or through a third-party messaging platform like Facebook messenger, Twitter or Instagram’s direct messaging.

For businesses with a generally young customer base, chatbots are more likely to increase customer satisfaction. More than 60% of Millennials have used chatbots, and 70% of them reported positive experiences.

The use of chatbots is not limited to situations when a customer has a specific question or complaint. Estee Lauder deployed a chatbot embedded in Facebook messenger that uses facial recognition to pick out the right shade of foundation for its customers, and Airbnb has used Amazon Alexa to welcome guests and introduce them to local attractions and restaurants.

Artificial intelligence can be a powerful tool for businesses looking to get ahead in social media marketing. Receiving feedback on how customers feel about different products, and learning how customers spend their time on social media platforms are valuable for all companies regardless of industry. Using these applications to better meet customer needs and build stronger relationships is a winning strategy, and one we can help support — get in touch with us today to get the most out of your social media channels.

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