Analytics & TestingArtificial IntelligenceSocial Media Marketing

4 Ways Machine Learning Is Enhancing Social Media Marketing

With more people being involved in online social networking every day, social media have become an indispensable part of marketing strategies for businesses of all kinds.

There were 4.388 billion internet users worldwide in 2019, and 79% of them were active social users.

Global State of Digital Report

When used strategically, social media marketing can contribute to a company’s revenue, engagement, and awareness, but simply being on social media does not mean making use of all that social media have in store for businesses. What really matters is the way you use social channels, and that’s where opportunities may be revealed through machine learning.

We’re going through the explosion of data, but this data is useless unless it’s analyzed. Machine learning makes it possible to analyze limitless data sets and find patterns hidden behind them. Typically deployed with the help of machine learning consultants, this technology improves the way data is transformed into knowledge and enables businesses to make accurate predictions and fact-based decisions. 

These are not all of the benefits, so let’s have a closer look at the other business facets that can be improved with machine learning.

1. Brand Monitoring / Social Listening

Business success today is determined by a number of factors, and perhaps one of the most impactful of them is online reputation. According to the Local Consumer Review Survey, 82% of consumers check out online reviews for businesses, with each reading 10 reviews on average before trusting a business. This proves that good publicity is crucial for brands, that’s why executives need to find a way to manage business reputation effectively.

Brand monitoring is a perfect solution, which is the search of any mentions of a brand in all available sources, including social media, forums, blogs, online reviews, and articles. Allowing businesses to spot problems before they grow into crises and react in time, brand monitoring also gives executives a thorough understanding of their target audience, and thus contributes to better decision making.

How Machine Learning Helps Brand Monitoring / Social Listening

As the foundation for predictive analytics, machine learning contributes to decision makers’ thorough understanding of all the processes going on in their companies, so that their decisions become more data-driven and customer-oriented, and thus more effective.

Now think about all the mentions of your business available online—how many of them will there be? Hundreds? Thousands? Collecting and analyzing them manually is hardly a manageable challenge, while machine learning speeds up the process and provides a brand’s most detailed review.

Unless unhappy customers contact you directly by phone or email, the fastest way to find and assist them is sentiment analysis—the set of machine learning algorithms that evaluate public opinion about your business. In particular, brand mentions are filtered by negative or positive context so that your business can quickly react to cases that can affect your brand. Deploying machine learning lets businesses track customers’ opinions regardless of the language in which they are written, which expands the area of monitoring.

2. Target Audience Research

An online profile may tell a number of things, such as its owner’s age, gender, location, occupation, hobbies, income, shopping habits, and more, which makes social media an endless source for businesses to collect data about their current customers and people whom they would like to engage. Thus, marketing managers gain an opportunity to learn about their audience, including the way the company’s product or services are used. This facilitates the process of finding product faults and reveals ways in which a product may be evolved.

This can also be applied to B2B relationships: based on such criteria as company size, annual revenues, and number of employees, B2B customers are segmented into groups, so that the vendor doesn’t need to find a one-size-fits-all solution but target different segments using an approach most suitable for a particular group. 

How Machine Learning Helps Target Audience Research

Marketing specialists have enormous amounts of data to deal with—collected from a number of sources, it may appear to be endless when it comes to customer profiling and audience analysis. By deploying machine learning, companies ease the process of analyzing various channels and extracting valuable information from them. This way, your employees can use ready-made data to rely on when segmenting the customers.

Also, machine learning algorithms can reveal behavioral patterns of this or that group of customers, giving companies an opportunity to make more precise predictions and use those to their strategic advantage. 

3. Image and Video Recognition 

In 2020, image and video recognition comes as an emerging technology necessary for all companies who want to have a competitive edge. Social media, and especially networks like Facebook and Instagram, provide an unlimited number of photos and videos being posted by your potential customers every day, if not every minute. 

First of all, image recognition allows companies to identify users’ favorite products. With this information considered, you’ll be able to effectively target your marketing campaigns to upsell and cross-sell if a person is already using your product, and encourage them to try it out at a more attractive price if they are using a competitor’s product. Also, the technology contributes to the understanding of your target audience, as pictures sometimes may tell much more about one’s income, location and interests than a poorly filled profile. 

Another way in which businesses can benefit from image and video recognition is finding new ways their product may be used. The internet today is full of photos and videos of people who conduct experiments and do unusual things using the most common products in a completely new way—so why not make use of it? 

How Machine Learning Helps Image and Video Recognition

Machine learning is an indispensable part of image and video recognition, which is based on constant training that may only be possible by employing the right algorithms and making the system remember the patterns. 

Still, images and videos that appear to be useful first need to be found among enormous volumes of information available on social media, and that’s when machine learning facilitates the mission that’s almost impossible if done manually. Boosted with advanced machine learning technologies, image recognition may foster businesses toward a completely new level of targeting, providing unique insights about customers and the way they use products.

4. Customer Targeting and Support Via Chatbots

More and more people today recognize messaging as the most convenient way to socialize, which gives companies new opportunities to engage customers. With the rise of chats in general and chatting apps like WhatsApp and Facebook Messenger, chatbots are becoming an effective marketing tool—they process information of all kinds and can serve to respond to various requests: from standard questions to tasks involving a number of variables.

Unlike usual navigation links and web pages, chatbots provide users with an ability to search and explore using a social network or a messaging app they prefer. And while traditional digital marketing typically engages through images, text, and video, bots make it easy for brands to connect to each customer directly and build a personal human-like dialogue.

Chatbots Boosted with Machine Learning

Most chatbots run on machine learning algorithms. If a chatbot is a task-oriented one, though, it can use neuro-linguistic programming and rules to deliver structured responses to the most general requests without requiring machine learning to support its basic capabilities. 

At the same time, there are predictive data-driven chatbots—acting as intelligent assistants, they learn on the go to provide relevant answers and recommendations, and some can even imitate emotions. Data-driven chatbots are powered by machine learning, as they are constantly trained, evolving and analyzing users’ preferences. Together, these facts make users’ interaction with a business more personalized: asking questions, providing relevant information, empathizing, and joking, chatbots appeal to what’s out of reach for traditional ads. 

With intelligent chatbots, businesses can assist an unlimited number of customers wherever and whenever they are. Saving money and time and improving customer experience, chatbots are becoming one of the most beneficial AI areas to invest in for mid-size businesses and enterprises.

Andrey Koptelov

Andrey Koptelov is an Innovation Analyst at Itransition, a custom software development company headquartered in Denver. With a profound experience in IT, he writes about new disruptive technologies and innovations in IoT, artificial intelligence, and machine learning.

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