Media monitoring: new technological challenges
Year after year, media monitoring is becoming an increasingly popular tool in building a positive brand image. All kinds of organizations and corporations are fighting against the scourge of disinformation, which, unfortunately, is nowadays the driving force behind a wave of negative opinions, completely not based on facts. A thorough analysis of all publications, comments and references appearing in cyberspace requires a great deal of effort and is extremely time consuming. Technical innovations cause that there is a hope for an equal fight, and what is most important, for defeating in it an army formed from the fake news. All data collected from cyberspace are the basis for creating the right marketing strategy. Deep insights into big data optimize sales and improve customer relationships.
The development of artificial intelligence
Media monitoring brands, such as Newspoint, implement intelligent technologies on an ongoing basis to ensure the highest accuracy and quality of the content analysed. Marketers in organizations have a wide range of tools to understand their clients, determine the nearest prospects and catch the ever-changing trends in social media. These Artificial Intelligence applications allow for accurate monitoring and analysis of social media by providing access to very relevant information.
Using AI for such research makes it possible to automate marketing tasks, improve accuracy and reduce human effort. Tools for online reputation management and competition monitoring are absolutely essential for building a positive image of each brand. The platforms created for social media research based on artificial intelligence, which have been developed using an innovative approach, enable marketers to better understand their customers and are certainly the closest future of web analytics and PR industry.
Social marketing has become very complex today. There are many social media channels, SMART-based devices and technologies that are changing into increasingly intelligent systems overnight. Together, they generate a huge amount of data that marketers and marketers simply can no longer manage efficiently. Social media marketing supported by Artificial Intelligence (AI) features makes it easier for companies to run digital campaigns while providing exponential results and reducing operating costs.
AI will certainly play a key role in social media marketing by introducing new task automation functions in the next few years. Digital analytical tools can interpret and analyze information about the target audience using such functions. They also improve customer experience management.
Although there are many applications of Artificial Intelligence in marketing, they all meet one goal: to better understand the consumer. Understanding the customer, brands can identify and create the right marketing content, find the right channels in social media, improve marketing content strategy and get insightful information about their users. A deep understanding of consumers guarantees an effective marketing strategy in social media.
Artificial intelligence will play a key role in the development of media monitoring tools, providing new functions for intelligent suggestions and decisions based on hundreds of thousands of analysed data.
Let’s just say you’re quite satisfied with your current social media marketing strategy and want to discover more platforms and channels. One of the first steps you will take is to find out which users are registered on them. You want to make sure that people from your target group are actually active there.
Demographic data in the social media help to create the so-called “social media”. staff marketing and understand potential customers in more detail. If you want to expand your audience, for example by trying to reach “Generation Z”, you can use current demographic data.
It can be extremely difficult to establish a reliable demographic profile, especially among the youngest users. A lot of teenagers set up accounts by entering false birthdays, which later results in an inconsistency in the data collected from social networking platforms. This may affect the results of your marketing strategy. The development of the already mentioned artificial intelligence and machine learning can help. This is an extremely important and difficult challenge for the media monitoring industry, because creating a genuine demographic profile is the next step to achieving a huge number of reach and reach, and these are the goals of each brand.
Recognition of human faces and brand logos
The human face is one of the most powerful channels for conveying feelings. The emotions that accompany us every day show how well we remember events, brands and their products. There are many benefits for organizations that want to use face recognition for marketing purposes. This function allows market experts to analyse facial expressions on a large scale. Thanks to traditional research methods such as focus groups and modern neuroscience techniques, people may be biased or reluctant to share their true opinions. Face recognition records the objective and authentic experience of the participant in a discreet way.
By analyzing the data, organizations can improve relationships with consumers by arousing certain, specific emotions that will increase trust and loyalty to the brand. Face recognition technology can strengthen it with extremely valuable data to make informed marketing, strategic and business decisions.
A challenge for the media monitoring industry may be that face recognition software violates privacy. Security is a very serious issue these days. Computers and people will record other people. In some cultures this may be a taboo subject; in others it is accepted but not very welcome.
Create an account and monitor the media Americans don’t like the idea that they are being watched. When marketing uses face recognition technology, opponents of this software claim that people are manipulated and exploited by corporations. Furthermore, registering someone’s appearance is a risk for people whose photos are stored and used for advertising purposes. Their data can be hacked and used without permission, even to forge ID documents.
According to Rich Relevance, 67% of shoppers find it disgusting when marketing professionals use facial recognition technology to identify previous buying habits and communicate this information to different brands. In addition, 64% say they would be scared if a salesperson greets them by their first name in the store because their mobile phone or app signals their presence.
Social media is a treasure trove of huge data resources that can be used to develop business, increase sales and fight competition. There are many indicators on different platforms that can be collected according to the audience and the type of industry. Each brand has different goals in terms of numbers and statistics, but the methods of achieving them remain the same.
Predictive analytics can be deeply linked to social data, because it is the recipients who communicate constantly changing market trends.
Business itself no longer drives product development as much as the customer needs and wants it. It is therefore important to understand what predictive methods are and how to use social data to make informed strategic and sales decisions.
In short, predictive analytics describes sets of tools and methodologies used to optimize resource allocation and increase positive marketing results. It uses archive data, algorithms and machine learning to make sales strategies as effective as possible. This is a way to go beyond guessing what will happen to product development by providing the best forecast – even before the product is launched.
Predictive methods can become a major challenge for the media monitoring industry, as the ever-changing regulations for social media platforms such as Facebook, Instagram and Twitter are causing a significant reduction in data availability. On the one hand, of course, this is good, because the safety of users is the most important thing, but on the other hand, when we put ourselves in the role of a company building awareness of our brand in social media, we will see that collecting user data for marketing purposes is becoming extremely difficult and increasingly limited.
Improvement of algorithms for measuring publication overtones
When someone is writing you a sarcastic message (without emotion icons), can you say one hundred percent that this is exactly what the addressee wanted to say, or maybe he was a little bit nonchalant about the situation and wanted to shine with his specific sense of humor? If a person has trouble catching sarcasm from a single statement, how hard it must be to have a machine that filters several hundred such contents per minute.
The sound of publications is something that has accompanied the media monitoring industry practically from the beginning. It is one of the most important indicators of building a positive image of each brand. This makes a thorough analysis of sentiments a very desirable and interesting field. Also known as opinion mining, it defines and categorises opinions in a given text as positive, negative or neutral.
Sarcasm is a form of irony, which automatic sentiment analysis unfortunately cannot detect. Hiring an army of people whose only task would be to read every comment on the company’s profile in social media channels with understanding is a missed target, as there can be tens of thousands of such mentions, and additional cataloguing and describing them by employees would take a very long time and cost a lot. In building a brand image, time counts. The longer it takes to react to negative opinions, the sooner people will stop trusting the organisation because they will find that it absolutely does not count on their opinion.
Nevertheless, with the increase of technological possibilities, the overtones of publications are becoming an increasingly used marketing tool for companies. Social media monitoring platforms use it to give users an insight into how public opinion influences the image of the brand and its products.
Unfortunately, in the past there were also darker circumstances of using this tool. For example, Facebook was under fire when it was discovered that it uses sentiment analysis to see if it can manipulate people’s emotions, changing the algorithms so that negative or positive posts are more often entered into users’ information channels without informing them.
Algorithms responsible for the collection and analysis of publications, such as the SVMs (Carrier Vector Machine), NB and Decision Tree, are constantly being improved to ensure that the data they have downloaded reflect as much as possible the mood of the public at large.
The analysis of sentiments is not perfect and probably nothing will change in the next few years. As part of social media monitoring, brands need the overtones of publications as a starting point to understand general social moods. Thanks to this they are able to initiate advertising and marketing campaigns based on users’ opinions.
Social media channels are the largest pool from which you can extract reviews and start gathering information about the success or failure of your brand in marketing campaigns.