Can you tell exactly which emotions your brand evokes in customers? If not, could you figure out what content is the key to triggering a good or bad reaction? Can you see all feedback you generated on social media? Can you establish competitors’ flagships, their ratings and ratings? Can you see all the data users put on the web mentioning, comparing or rating your products? Don’t worry, AI can. With the right tools, it will provide not only an invaluable analysis of customer associations and behavior. It will also help you prepare an effective social media marketing strategy, and improve your service. AI and social media – what do you need to know? Keep reading to find out!
AI and social media – what do they say about us? – table of contents:
- Introduction to AI and social media
- What does AI see in social media?
- Association networks and customer emotions
- How to use data collected by AI in social media?
- AI and social media – summary
Introduction to AI and social media
According to a report by Verified Market Research, the social media AI market is already worth more than $987.5 million today. By 2028, forecasters believe it can reach an even sixfold increase. Why do analysts draw such rosy growth prospects for artificial intelligence? Is there more to AI than analyzing, moderating activity supporting, and developing sales in social media? Read on as we’ll.
In today’s post we will look at, the following:
- What data does AI analyze in social media?
- Why is such data useful for business purposes?
- What AI-based tools can companies already use?
What does AI see in social media?
What is the reason for such a demand to apply AI to the behavior of people using social media? In short, it has to do with what and how much amount and types of data are being acquired.
A non-AI-supported analyst who monitors social media posts may count the reactions and number of comments to assess whether posts tagging a company have good or bad reactions. As the task is tedious, intensive, and risky.
An analyst using AI will gain the ability to gather data from all the places where people mention a company, as well as to have a forecast on where they can appear. This is possible only because AI can operate on a much larger scale. In other words, it can analyze Big Data, i.e. huge amounts of data of varying structures. Also, it can analyze and fish out the average types of reactions customers have. The feed for AI mainly includes, among other things:
- numerical data – such as the number of comments, observers, reposts,
- photos – thanks to image recognition technology,
- user activity data – for example, the length and frequency of interactions with content published by the company, or the number of orders placed over a certain time scale,
- the textual content of social media.
Taken individually, each of these areas provides a solid bulk of statistical research to conclude. In contrast, what sets AI-based social media analysis apart is its ability to combine them. What AI sees in social media are patterns of customer behavior and networks of connections showing relationships that are not apparent when analyzing data of a single type or from a single source.
Association networks and customer emotions
The numbers alone won’t help to perceive the relationship between a brand and customers that takes place on social media. This is because the content published in them is primarily emotionally meaningful to users as it triggers an impact to act. To express their feelings with an expanding list of emoticons, to add a comment, and eventually – to buy a product.
So far, the greatest room for improvement, and often with surprising results, have come from tools from the field of NLP, or Natural Language Processing, to analyze social media. The field of NLP includes analyzing text data contained in posts and comments, or text mining. AI can analyze statements in a way not available to humans, i.e. pattern recognition and keyword detection by studying the frequency of words and phrases. A well-known and impressive result of text mining is the visualization of results in the form of:
- a word cloud (wordcloud reflecting the frequency of their occurrence utilizing font size,
- dendrogram, or tree, so you can additionally see the relationships between words and the frequency of co-occurrence of words.
How to use data collected by AI in social media?
AI-based tools allow us to reflect associations, for example, by showing relationships linking a product name to adjectives describing quality, emotions, or associated values. This can prove to be a key tool for social media analysis by showing how customers perceive our business.
Linking the frequency of certain words, their combination with photos and users’ emotional reactions – opens up entirely new business opportunities. However, this is only the start of the way opened up by AI-assisted social media analysis. Still, AI will help you read the data captured and help optimize the business potential of those results. For example, complex data combining the location of social media users with photos makes it possible to determine:
- what time
- with whom
customers use our product or benefit from our services.
They also enable “gap analysis,” that is, they indicate where you can find new customers who haven’t yet heard about your product, as well as groups, or even entire sites, where mentions of your services appear, but where you are not yet present.
AI-assisted analysis of social media activity is used primarily for customer relationship management (CRM, Customer Relationship Management) and customer experience management (CEM, Customer Experience Management). And the tasks that can be assigned to AI using today’s tools are as diverse as:
- Communication automation – post publishing and mailing.
- Managing the brand and maintaining the consistency of its image.
- Creative AI generating genuine posting content.
- Personalization of the displayed content.
An important issue arising alongside AI-based personalization of social media relates to user privacy and data protection. One of the main problems is the so-called personalization paradox.
The paradox of personalization appears when a customer expects a personalized experience without full consent for data or is uncomfortable viewing content that pops up “tailored” to their online activity. According to a report by Accenture, as many as 35% of social media users do not want to see ads for products from past views. whose pages they have visited.
AI and social media – summary
The performance of AI in social media is an area of development for all of us. AI can see behavioral patterns in scattered posts, as well as find invisible connections. With such tools as multifaceted analysis of textual and audiovisual content or gathering, and comparing key emotional reactions, great prospects for development open up.
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AI in business:
- Artificial intelligence in business - Introduction
- Threats and opportunities of AI in business (part 1)
- Threats and opportunities of AI in business (part 2)
- AI applications in business - overview
- What is NLP, or natural language processing in business
- Automatic document processing
- AI and social media – what do they say about us?
- Automatic translator. Intelligent localization of digital products
- AI-assisted text chatbots
- The operation and business applications of voicebots
- Virtual assistant technology, or how to talk to AI?
- Business NLP today and tomorrow
- How can artificial intelligence help with BPM?
- Will artificial intelligence replace business analysts?
- The role of AI in business decision-making
- What is Business Intelligence?
- Scheduling social media posts. How can AI help?
- Automated social media posts
- Artificial intelligence in content management
- Creative AI of today and tomorrow
- Multimodal AI and its applications in business
- New interactions. How is AI changing the way we operate devices?
- RPA and APIs in a digital company
- New services and products operating with AI
- The future job market and upcoming professions
- Green AI and AI for the Earth
- EdTech. Artificial intelligence in education