As the importance of social media continues to increase, especially in terms of brand management and customer engagement, companies have had to employ new strategies and technologies to better understand consumer behavior and sentiment. In-house or agency-based social media teams have become common over the past decade, but many have struggled to thoroughly understand what motivates customers. Understanding complex patterns of behavior and engagement on social media has eluded even the savviest social media and marketing professionals.

One potential solution? Machine learning technology, which combines human insight into language and contextual meaning with the speed, accuracy, and data-driven insight of artificial intelligence.

What Is Machine Learning?

Machine learning has been defined as “the subfield of computer science that gives computers the ability to learn without being explicitly programmed.” In other words, machine learning emphasizes the development of programs that change or “learn” to adapt to new data. Machine learning algorithms can be programmed or “trained” to make decisions and deliver insights based on information input.

For instance, developers can provide specific examples of the type of content they would like to identify, and machine learning programs will recognize patterns in those examples and return similar results. The more examples provided, the more the program can adapt and deliver specific content based on what it has learned. A relatively recent phenomenon, machine learning has become a technological frontier in many industries and business contexts, including social media.

Machine Learning in Social Media

In terms of social media, machine learning can be used to identify trends across a wide range of posts or updates and determine similarities between them. Machine learning programs excel at analyzing data that is unstructured or data that may be too complex or cumbersome for humans to categorize or analyze. This data requires more sophisticated analysis, looking beyond more common indicators such as likes, shares, comments, and hashtags. Although traditional social media management generally consists of manually looking for easily identifiable (and quantifiable) patterns, machine learning algorithms can be trained to identify more subtle patterns across a much wider spectrum of posts.

For example, machine learning programs can examine audience sentiment to understand why users are posting about or discussing certain topics, as well as why people tend to buy a certain product from one company over a similar one from a competitor. Sentiment analysis provides companies with powerful insights to understand how people feel about their brand and their products. Positive and negative language is analyzed, as well as common patterns of conversation that humans may not be able to see.

Additionally, consumer behavior is often analyzed by machine learning tools. Behavior can be better understood when machine learning techniques are leveraged to understand why potential customers respond (or don’t respond) to certain offers at certain times. For instance, machine learning can examine all of the users who’ve engaged with customer support staff on social platforms like Twitter and Facebook, identifying common problems. These data points drive predictive analytics that can be used by marketing teams to create more targeted ad campaigns and offer special deals.

Brand Identity Monitoring

Online brand identity analysis has become critical for company strategy. Most business owners now acknowledge the importance of online reviews as well as comments and posts that affect the reputation and overall status of their brand. A study from Business of Reviews found that 31 percent of business owners feel it’s important to find ways to monitor and manage negative content as part of their marketing strategy.

Social media “listening” tools are designed to do exactly this. These artificially intelligent tools detect patterns in customer sentiment and trends as they happen in real time or, in some cases, even before they happen. Machine learning-based listening tools can analyze quantitative data in order to analyze customer interaction, provide insights, and optimize campaigns.

Although humans are still currently better at evaluating nuanced communication (like sarcasm and irony), machine learning technologies outperform human-based teams when it comes to gathering important social data quickly.

Toward Smarter Social Media Strategies

Social media has come a long way. What started as a fun way to communicate with friends and family has become a necessity for companies of all sizes in every industry. And as organizations look for new ways to market to potential customers, machine learning programs have become increasingly important tools for analyzing and predicting social sentiment. Brand success and identity can be more effectively monitored when the right indicators are made available by these programs, and, because of their ability to continuously learn and adapt, they also represent a huge leap forward toward smarter, more engaging social media experiences.