4 Ways Machine Learning Will Affect Social Media Monitoring

In this digital age, the smartest brands are the ones that make use of social media monitoring. The

In this digital age, the smartest brands are the ones that make use of social media monitoring. The process of social media monitoring allows brands to keep track of their online reputation and image. However, with the size and complexity of data increasing day by day, brands need to understand not only how to make use of this data but also how to cope with this growing data. We have previously seen the influence of artificial intelligence on social media monitoring. In this article, we look at how a sub-branch of artificial intelligence, i.e. machine learning is affecting social media monitoring. From improved sentiment analysis to the real-time evolution of algorithms, a lot of changes can be expected due to machine learning.

#1 Maintaining a good reputation

Currently, social media managers are most concerned about conversation management. It is a critical challenge for managers to inspect content for trolls and opponent businesses. This type of content tries to spoil the community’s experience through offensive messages and indecent content.

In a recent Business of Reviews study, it was found that 79% of interviewed business owners feel that online forum posts, comments, and reviews are valuable for the reputational and financial footing of their brands. Additionally, 31 percent of the owners believed that they are looking for effective ways to monitor and regulate negative content. The survey concluded that brands believe monitoring negative content was necessary for better marketing strategy and customer service.

If undesired events occur with products or actions of trolls on social media platforms, public-relations nightmares may surround your business. Recently, Samsung faced a drop of 30 points in its BrandIndex rating because of the Samsung Galaxy Note 7 scandal. In this scandal, a high number of Samsung’s Galaxy Note 7 smartphones were reported to catch fire and explode. These reports questioned Samsung’s product integrity due to which the company had to recall all handsets. A hype was created over the social media channels which greatly affected Samsung’s brand reputation.

According to predictions, AI systems will soon manage the flow of content and conversations completely. These systems use filtering features to administer the comments of users across several social media platforms. They observe and report emerging crisis situations before they spread out too far.

The idea is not to delete all negative comments to pull the wool over users’ eyes. Instead, when there is nothing inappropriate or offensive about a comment, you should release official statements to look transparent. This gives customers the idea that their opinion matters. When customers raise concerns, you can send them personalized messages using bots and machine learning systems.

In the modern digital age, we judge a brand’s strength by its effective social media management. Likewise, we judge a brand’s success by its social media conversations and followings.

#2 Speak the universal language

Ten years ago, the most common tools of choice for marketing research were focus groups and surveys. Nowadays, machine learning has increased the reliability, speed, and accuracy of answers. Moreover, it is also used to combine collected information to answer some new questions. This assists in changing your course of action in the beginning and narrowing down options to reach an optimal decision.

The social media insights of a brand educate the marketers about how customers feel when they buy a product, what new ways are they using it, and may also give ideas for some new business opportunities.

The client segmentation techniques used earlier could not develop user personas. However, now companies use clustering to find out useful information about their typical customer – their age, occupation, interests, etc. This knowledge is then used to generate personalized posts, also known as targeted marketing.

One other aspect of machine learning is its ability to analyze and work with various languages without requiring reconfiguration. Machine learning algorithms make use of clusters which means that these can interpret different languages without modifying underlying code. Moreover, social media monitoring tools that make use of machine learning are great for analysis. Particularly if you have non-native speakers in your audience. People who do not have English as their mother tongue generally tend to be vague and inconsistent in their communication. This can be hard for even human marketers to interpret and understand. However, machine learning makes this process easier by automatically converting text and emoticons into messages. Global audiences and non-native users can then easily understand these messages.

#3 Improved social media data tracking

A brilliant aspect of machine learning is that you can code it to determine patterns. Through machine learning, we can find things without telling the algorithm where or when to look for those things. Social media is a massive landscape for communication that has a barrage of information about your brand. The key is to find information that is relevant and adds to your business. This is where machine learning plays a role. The entire social network can be analyzed through machine learning for interpreting messages that represent customer satisfaction, anger, happiness, and distress. You can learn more about the sentiments of customers by implementing machine learning in your social media monitoring process.

You can configure machine learning for identifying the parts of your sales funnel that generates the most leads. Or, you can configure it to analyze customers at an individual level to understand how they interact with your brand. Let us look at a few areas where machine learning really helps the social media monitoring process:

  • Media Analysis: Whether it is images or videos, customers will not often tag your brand when they post media on social media. This makes it incredibly difficult to find customer interactions and feedback about your brand manually. However, machine learning can streamline this process. You can train machine learning algorithms to determine patterns and logos in images and videos. This way you can identify how, where, and when people mention your brand on social media.
  • Efficient Data Extraction: There is an enormous amount of data on social media. The key is to collect, extract, and filter the data to find exactly what is useful for your brand. There is a lot of information that is extremely similar but different at the same time. For example, Shell is an online company or a shell, e. seashell. Machine learning can extract information without all of this ‘noise’ and filter out only what you need from the data.
  • Better Data Processing: Statistics show that the number of devices connected to the Internet will increase from 15.4 billion in 2015 to 75 billion by 2025. These devices are expected to soon converge with social media to form a social Internet of Things (IoT) that will allow users to automatically update their profiles and post on their behalf. With this massive growth in data sizes expected, there is a need for an intelligent and efficient method for automated data processing. This is yet another application of machine learning – it can automate the data processing process for improved and more accurate results.

#4 Enhanced business decision-making

The primary objective of social media monitoring is to improve the decision-making process for businesses. Machine learning enhances social media monitoring to provide better insights and more detailed information to businesses. This ultimately leads to better conversion rates and hence, increased revenue. Given below are some ways in which machine learning can help in refining the decision-making process.

Improved categorization through machine learning

Machine learning algorithms pre-defined data sets to interpret information about data sets that are expected in the future. This ‘derived’ information enables us to make accurate predictions and estimations about how things will turn out in the future. Machine learning is possible because no matter what kind of data there is, it will contain patterns. The only data that does not contain patterns is data that is completely random. These patterns allow machines to categorize information and hence, make predictions accordingly.

Why is all this important? Because this is exactly what we social monitoring tools do! Social media monitoring tools can classify information based on tags, sentiments, and categories. These classifications make it easier for businesses to make decisions. What machine learning does is that it improve this process by automating it. The artificial intelligence algorithms that advanced monitoring tools such as Mentionlytics use can classify information in a variety of categories. This provides more meaningful insights to businesses that they can use to make more informed business decisions.

Drawing conclusions in real-time through machine learning

Like machine learning, Natural Language Processing (NLP) is a branch of artificial intelligence. Through NLP, machines can learn to understand and interpret voice or text messages directly. This means that there is no need for filtering, cleansing, or transforming the data from input channels for the machines. NLP allows machine learning algorithms to identify patterns in casual messages from customers. On social media platforms, such as Facebook, users send out spontaneous messages in a variety of tones, dialects, and emotions. The key to a successful social monitoring strategy is to categorize these efficiently and accurately.

The combination of NLP and machine learning allow social monitoring tools to precisely classify customer messages. This means that if a customer tweets about how positive their experience was, you would know. In this case, you should leave a thank you message. Similarly, if a customer tweets about how negative their experience was, you would know as well. In this case, you can take care of the customer’s concerns by learning more about their experience. Using NLP and machine learning, we can analyze, categorize, and respond to the various elements of a customer’s journey in real-time to take strategic business decisions.

Successful Examples

We looked at how machine learning is (and will) affect social media monitoring earlier. Now we move on to brands that have successfully implemented machine learning in their social media marketing strategy. Two examples of well-known brands that utilize machine learning are Delta Faucets and Adore Me.

Delta Faucets

Delta Faucets is a well-known faucets manufacturer based in Indiana, United States. The company makes use of machine learning and natural language processing in conjunction with social media monitoring. This allows them to measure how well their content performs. According to Marketing Artificial Intelligence Institute, the information that Delta Faucets utilizes for its content helps them recognize key business indicators. This includes information about which people are most likely to engage with their brand. This strategy helped them boost their page views and content performance by up to 49%.

Adore Me

Adore Me is a women’s lingerie and intimacy company that is based in New York, United States. It is a renowned brand that manufactures and sells women exclusive products all around the globe. Adore Me makes use of machine learning and associated technologies to segment its audience. This helps them develop effective one-to-one marketing strategies. According to Tech Emergence, the lingerie brand has a noted improvement in its segmentation strategy and marketing success since it incorporated machine learning into its marketing. Adore Me has improved their targeting drastically with this. Their revenue has grown by as much as 15% in the last year!

To Sum Up

There is an ongoing argument that artificial intelligence can lead to the absence of a human element from community management. However, the reality is that artificial intelligence and machine learning enable us to work with more meaningful information for decision-making. Though roles might change, the core decision-making always must be taken by humans. With machine learning, marketers can focus on providing the best possible positive experience to their customers. Social media monitoring becomes much more efficient, reliable, and accurate when we incorporate machine learning into it.

Social media is full of rich insight for brands. What you need is the right technology and technique to process this information. This is where machine learning helps you refine the data coming from billions of devices across the globe. To reap the best possible benefits from social media monitoring, it is essential that marketers utilize tools that incorporate machine learning.

Manos Perakakis

About Manos Perakakis

Manos is the co-founder of Mentionlytics. He has a PhD from Brunel University in User Experience and HCI. Also, he has also been teaching Digital Marketing and Web Design to Bachelor degree students for the past 9 years, as a lecturer in Hellenic Mediterranean University. • Follow Manos on TwitterCheck Manos on Linkedin