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Information is key, especially in business. Without appropriate knowledge, marketing teams would have difficulties to address the actual needs and requirements of any given target group. This is especially true nowadays, when clients’ attention has shifted to a much more personalized shopping experience. Because of that, the marketing approach had to change – the company's traditional, internal information about customers is no longer sufficient to draw the right conclusions about their behavior. Instead of treating them as a group, you need to start treating them as individuals with different preferences. Here is where social media come in handy. But how to avoid drowning in the sea of information provided by social media? With help from a very powerful and advanced tool - machine learning.
It is no secret that companies use big data from social media to obtain additional information about specific customer preferences. This way, they have access to information that they would not be able to obtain otherwise. That data, after proper analysis, give the possibility of much deeper understanding of the client's relationship with the brand, changes in marketing communication and loyalty programs strategies based on correctly defined segments. It is not only about the positive impact though. Companies want a clear insight into how their actions affect customers' decisions and preferences, but also to become aware of the negative effects – the sooner they can identify them, the more effective their reaction may be.
Ignoring social media as a valuable data source may lead to making wrong, costly decisions. However, the huge amount of unstructured data published by social media users daily (text, photos and videos) is extremely difficult to view, managed and analyze manually. In order to be able to use this information efficiently and draw appropriate conclusions, it is necessary to engage advanced technologies that are able to analyze the context and indicate the right sentiment expression. What would take us ages, computers can do in minutes. Thus, there is an opportunity to use machine learning in a previously unexplored area – analysis of social media data.
Machine learning, based on algorithms, enables identification of certain behavior patterns and further, accurate classification of information into appropriate groups. The software applies rules and data sets to perform complex calculations, organize and classify data, identify trends, discover hidden patterns, identify behavior and match data. These algorithms combine a statistical approach with the scale and speed of automated operation. They can be refined and taught in such a way that the statements’ categorization is performed in a manner similar to an actual human drawing conclusions (but again, much faster).
Systems using machine learning are able to provide constant monitoring and appropriate filtering from billions of social media entries and record specific trends in customer behavior in a specific business context. Thanks to sentiment analysis carried out on the basis of such mechanisms, enterprises are able to determine the degree of customer satisfaction with the products and services they offer. The function supporting sentiment analysis is based on identification of the most commonly used words in a specific topic, which allows real-time control and immediate responsiveness.
A practical example of machine learning in social media marketing is the analysis of user reactions to brand promotions, offers and updates. You can learn whether these have been received positively or negatively, as well as the content of published comments. Additionally, the system allows you to compare and examine the competition and sentiment expression on various social media channels. Thus, you are able to meet customers’ expectations better by making the advertising more personalized and precise.
Sentiment analysis is performed based on a chosen language, taking into account its specificity. Therefore, for each language version it is necessary to "teach" the system how to interpret any given statement. However, machines cannot compete with people as far as slang, sarcasm and double meanings are concerned, so you need to be very clear on phrases you want to choose.
With the increasing availability of relatively cheap and flexible computing power, machine learning technology is becoming far more accessible even to small and medium-sized enterprises. What is more, it reduces marketing expenses because it automates a lot of processes (data collecting, analysis, reporting, targeting ads, scheduling social media posts and ads, email automation, etc.).
In the future, machine learning systems will require less data to come up with satisfactory results, which means they will be able to learn even faster. Considering the ever-expanding volume of social media content, this is a really optimistic prognosis.