Social media marketing is now crucial for businesses to remain competitive. Facebook and Twitter allow companies to spread the word about upcoming products or events and provide customer support on their platform. Instagram can be used to showcase products to over 500 million people active on the app at least once a day. Customer support on social media is preferred by about 63% of people, as compared to phone or email. Due to these platforms, businesses can engage with customers and maintain a relationship with influencers. They educate companies on how customers feel when they buy their product, ways in which they’re using them, and helps generate ideas on new business opportunities.
Machine learning in social media is increasing the quality of online interactions between companies and their customers. In this blog, we discuss how Machine learning is helping brands make sense of the thousands of conversations happening every day on social media.
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Basics of Machine learning
Machine learning is a sub-field of Artificial Intelligence which involves creating problem-solving systems that are characteristic of human intelligence. Machine Learning in social media can help produce more accurate insights from online engagements. It creates awareness about marketing campaigns that can perform best and helps companies divide the overall marketing spending more accurately.
Data also plays a key role, as the analysis relies on Big Data to get more information from social media platforms. Machine learning takes advantage of these large volumes of data that are spontaneous and unstructured. It scales the data and keeps up with customer opinions and trends.
The applications mentioned in the blog can help companies better understand and meet consumer needs, which ultimately allows them to build a stronger relationship with customers.
Machine Learning can be used in the following ways for effective social media marketing:
1. Social media monitoring
Social media monitoring is a more traditional tool for companies. It enables them to keep track of their online image and reputation. It can help in conversation management, i.e., inspect social media content for trolls and business opponents. This type of negative content is spread to spoil the community’s experience with offensive messages. Monitoring and regulating such content are necessary for a better customer service and marketing strategy.
There are built-in analytics tools in platforms like Twitter and Instagram that can measure success of past posts, such as number of likes, clicks, comments, or views. Third party tools can also provide similar social media insights such as demographic information about their audience, and the peak times when they are the most active on the platform.
2. Sentiment analysis
Sentiment analysis or opinion mining judges the opinion of a text. It uses Natural Language Processing (NLP) to analyze social media data with predefined labels such as positive, negative, or neutral. Sentiment analysis is used to analyze social conversations and understand the deeper meaning as it applies to a brand.
Sentiment analysis can be applied in social media for customer support and collecting feedback on new products. Companies can apply sentiment analysis to:
- Evaluate a brand’s reputation by understanding social sentiments
- Dealing with shifts in brand image due to spike in negative posts
- Understand how people feel about their competitors or trending topics in the industry, and change conversation about the brand accordingly
3. Image recognition
Computer vision has made it possible to make sense of content within images, i.e. recognize brand logos and images of products without the texts. This is useful when customers upload photos of products without directly mentioning the product name or brand. For example, if someone uploads a photo of a product saying, ‘Where can I buy this?’ companies can notice it and send the targeted promotion to that person. If there is a positive review for a product by someone, the company can thank the customer for their purchase. This leads to interaction with the customer and increased customer loyalty.
Images on social media receive much higher engagement compared to posts that are purely text. So, it can benefit companies to pay close attention to people posting photos of their products. Positive engagement by companies on these photos encourages customers to post more in future, leading to further brand promotion.
Chatbots are AI applications that mimic real conversations. They are embedded in websites, or through third party messaging platforms like Twitter, Facebook messenger, and Instagram’s direct messaging.
Chatbots enable companies to automate customer service by offering personalized assistance to customers. They solve consumer frustration related to customer care assistance by offering standard solutions to common problems. Chatbots are more likely to work for companies having a young customer base as they are more popular with the younger audience. They help save time, cost, and human efforts.
Knowing how customers spend time on social media platforms, is highly valuable to companies. And, Machine Learning in social media proves to be a powerful tool to help them get ahead with this.
There are too many conversations taking place on social media for companies to monitor them all manually. Machine Learning makes social media analysis more powerful and accurate. At Skyl.ai, we offer end-to-end Machine Learning platform, using Computer vision, Natural Language Processing, and Data labeling. Our Natural Language Processing platform lets enterprises work with texts, through systems such as content classification, entity analysis, and sentiment analysis. Skyl.ai helps businesses attain useful information from unstructured data, automate processes and enhance their decision-making skills.
So, go ahead and optimize your social media marketing strategy with the help of Skyl.ai’s Machine Learning platform.