When in 1984 the classic of today’s science fiction cinema, Terminator directed by James Cameron, was released, hardly anyone believed that at least half the visions presented in the script could ever come true. Android to an illusion resembling a man, sent from the future, from the world, where the rebels (people) are fighting a war for survival with cyborgs. Funny? Scary?
It’s certainly unrealistic. An idea completely separated from the reality over 30 years ago. In the opening scene of the film, we see a postapocalyptic Los Angeles film from 2029. The city was destroyed during fights with deadly, intelligent machines.
Nowadays, by calling the helpline of one of Europe’s leading telecommunications networks, we can talk to a virtual assistant who will advise us on what to do if we purchase various services, report a breakdown, etc. Just a few words and the artificial intelligence is ready to help us with almost every problem related to the company’s offer.
Rapid AI development and machine learning
The level of development of “AI” is currently very high and will grow day by day, thanks to the technological novelties that are emerging. Some time ago, Bill Gates said that artificial intelligence could be our friend. We have to put aside thinking that the nearest future will look like a landscape after the battle of Cameron’s film, and start imagining that “AI” will really make our lives easier.
We would like to introduce a term that a lot of people use interchangeably, as a synonym for artificial intelligence, i. e. the so-called “artificial intelligence”. machine learning. We will present it in the context of the media monitoring industry and show what changes the development of this analytical process brings to it. Every “mind” must learn in order to be able to use its full potential. The information collected from the surrounding environment is essential for this purpose, and, as in the case of people, an important role is played here by teachers, i. e. someone who will help to process this knowledge and appropriately direct how to apply it skilfully.
The use of large data sets to optimize systems for specific tasks; a specific subset of artificial intelligence that educates the device on how to learn – that’s how we can define what machine learning is in short and simple terms. A great example is the world-famous Netflix platform for watching films and series online. Analyzing large amounts of data helps to predict which productions may appeal to the customers of the American giant. The system sends users proposals of noteworthy materials which resemble the most popular ones with their topics. The California-based company is increasingly leaning towards creating its own content, using the information collected from people already using Netflix services. The result of the analysis of this data will help to guide producers on which genres to invest in in the future, in order to count on their high ratings and popularity. Machine learning is based on the idea that computer systems can learn from information gained, identify patterns and make decisions with minimal human intervention.
The same solutions are found in the media monitoring industry. The huge influx of new technologies has forced the need to create a tool that goes beyond human cognition. Programs and panels collecting necessary data for media reports, such as Posting range, AVE indicators and publication overtones are constantly being improved so that analysts have less work to do on their collection and analysis. In this way, experts can fully focus on the final results of the process and their thorough analysis, resulting in better quality results in the form of flawless customer summaries.
Machine learning is a perfectly matched mechanism capable of processing millions of articles in seconds and extracting the most difficult data. This is now an invaluable phenomenon for marketers, marketers’ experts, and experts in the field of market research. The Commission will also be assisted by the Communications Committee and the Directors-General of the Media. The discovery of a specific trend and its forecast for the future will allow to take steps that can significantly affect the development of the brand and the quality of its services. This may result in an unrivalled position on the market.
Until 20 years ago, the media monitoring industry looked completely different. Tracking of advertising campaigns, preparation of reports, data analysis -> these were the processes that usually took place manually. The staff of skilled workers focused on each element separately. There were far fewer business publications then than today. The information cycle was 24 hours or more. The social media we know today did not exist. Now it is only one mouse click to generate ready-made data to start analysing the results we are interested in. The evolution of algorithms in real time allows for virtually unlimited research possibilities.
As expected, AI systems will soon start to fully manage the flow of content and conversation. Artificial Intelligence uses filtering functions to administer user comments on several social networking platforms simultaneously. They observe and report emerging crisis situations before they spread too far and cause a drop in confidence in the brand. For the staff responsible for managing official Facebook accounts, the fight against trolls and hejters is a major threat. The systematic development of machine learning helps managers to emerge victorious from this competition. Using properly configured and constantly updated media monitoring tools, such as Newspoint, we can safely administer the publicly available content and have control over our brand’s online reputation.The point is not to remove negative comments, but to publish official, transparent statements in response to criticism. This gives customers the confidence that their opinion matters.
You can also talk to users using bots that create personalized messages for them. The process of data processing for such messages by computer systems is machine learning. In the modern digital age, we evaluate the strength of a brand through effective social media management.
A few years ago, focus groups and surveys were the most frequently chosen tools for marketing research. Today, machine learning has increased reliability, speed and accuracy of responses. Brand reconnaissance in social media makes marketers aware of the emotions that accompany consumers when buying products and the new paths that consumers follow. Today, companies use clustering (hierarchical methods) to find useful information about their target group – age, occupation, interests, etc. This knowledge is then used to generate specific, personalized posts – targeted marketing.
Rich in materials, press releases and magazines still need people to scan them. Optical Character Recognition (OCR) programs then process the digital copies, essentially giving them the same format as online content. Algorithms search both the internet and paper media for specific cases, such as for example brand mentions. Currently, employees have to review the results obtained by the computer systems, as it may happen that information that is not relevant to the principal may fall into the compilation. Artificial intelligence is not reliable, of course, but technological development makes such cases less and less of them overnight.
Another aspect of machine learning is its ability to analyze and work with different languages without reconfiguration. Machine learning algorithms use system links, which means that they can interpret different languages without modifying the source code. This option will certainly change the future of media monitoring applications and tools. Preparing reports in several languages will become much easier and more convenient.
The main objective of social media analysis is to improve the decision-making process of companies. Machine learning streamlines social media monitoring to provide faster access to data and more detailed information for businesses. Ultimately, this leads to better conversion rates and thus to increased brand revenues.
Artificial intelligence and machine learning will have a huge impact on public relations. And it will give you more than just analysis, it will give you answers to what is happening in your media coverage and why – all on demand. It will anticipate what topics may be a problem in the future, where these crises will occur and how long they may last. These predictive verifications will make you more proactive than just reactive.
Proper understanding and implementation of artificial intelligence guarantees success in social marketing. This solution will provide a strong understanding of the audience, the competition and the types of content that inspire consumers to take action that affects the brand.
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