4 Ways Machine Learning Can Enhance Social Media Marketing

Article by Rei Morikawa | June 26, 2019

Instagram is a global platform where businesses can showcase their products to over 800 million total Instagrammers, 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. 63% of customers prefer customer support on social media, compared to other avenues like phone or email. Major businesses such as GameStop, UNIQLO, and The Container Store are using social media to establish and maintain relationships with influencers and engage with customers in a casual setting. In recent years, social media marketing has become crucial for most businesses to remain competitive.

At the same time, artificial intelligence and machine learning are becoming more integrated in many aspects of social media. AI is far from replacing human touch in the field of social media, 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 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 like Iconosquare (*for Instagram and Facebook) can also provide similar social media insight and management services. These tools can also tell businesses a lot about their audiences, including demographic information and the peak times when their followers are most active on the platform. 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 judging the opinion of a text. The process uses both natural language processing (NLP) and machine learning to pair social media data with predefined labels such as positive, negative, or neutral. Then, the machine can develop agents that learn to understand the sentiments underlying new messages.

Businesses can apply sentiment analysis in social media and customer support to collect feedback on a new product 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 can 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 say thank you for their purchase, which could certainly lead to increased customer loyalty.

In addition, the customer might feel encouraged to post more photos of your products in the future, which leads to further brand promotion. Businesses may benefit from paying close attention when people post photos of their products, because 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 a tweet with images. This is important for marketing because 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, and Twitter and Instagram’s direct messaging.

Chatbots allow businesses to automate customer service without requiring human interaction, unless the customer specifically asks to speak or chat with a human representative. For businesses with a generally young customer base, chatbots are more likely to increase customer satisfaction. 60% of millennials have used chatbots, and 70% of them reported positive experiences.

The use of chatbots is not limited to situations when the customer has a specific question or complaint. Estee Lauder uses 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 marketing. Receiving feedback on how customers feel about different products and learning how customers spend their time on social media platforms are valuable regardless of industry. Businesses can use the applications introduced in this article to better understand and meet customer needs, and ultimately build stronger relationships with their customers.

The Author
Rei Morikawa

Rei writes content for Lionbridge’s website, blog articles, and social media. Born and raised in Tokyo, but also studied abroad in the US. A huge people person, and passionate about long-distance running, traveling, and discovering new music on Spotify.


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