Machine learning offers a series of benefits in various areas ranging from customer relations to cybersecurity, making it a tool that helps to solve complex problems or improve operational efficiency.
Let’s look at some of the advantages of this branch of Artificial Intelligence which, through algorithms, gives computers the ability to identify patterns and develop predictive analytics thanks to the study of big data.
Improving customer relations
The relationship between companies and their customers is fundamental. Therefore, being able to improve it is of great relevance for the former.
In this case, machine learning can help provide more personalised and closer attention, with chatbots – automated tools for interacting with customers – being an example of this. These chatbots also offer the advantage of not needing a time slot in which to attend.
The possibilities offered by this branch of AI, also known as machine learning, include the analysis of preferences when offering personalised products and experiences to users, as well as improving the segmentation of the target audience and, therefore, the effectiveness of advertisements.
It also helps to boost loyalty thanks to the perception of customers of receiving a more personalised and friendly service.
Cybersecurity
Machine learning also helps to reduce security breaches by being able to identify the anomalies that usually occur in the face of malware, for example.
In the face of this issue of paramount (and growing) importance, machine learning also makes it possible to strengthen cybersecurity by keeping up to date with possible new threats.
In the same way that it keeps up to date, it also allows a rapid response to these threats, as well as having the capacity to detect fraud by identifying suspicious patterns when, for example, carrying out transactions.
This capacity in the face of cyber-attacks is possible because most malware uses similar code, so machine learning can become capable of detecting and thus preventing the attacks from being repeated.
Innovation
Investing in innovation is an incentive for machine learning in the search for more effective technological solutions.
Thanks to the capacity of machine learning to obtain valuable information and data, new options can be opened up in the search for solutions that increase both sales of products or services and customer loyalty thanks to better treatment, as we have already mentioned.
Similarly, innovation can also be used by machine learning to improve processes within organisations.
Increased efficiency
The ability to automate time- and effort-consuming tasks and processes is easier with the contribution of machine learning, whose algorithms process huge volumes of data, as we saw when we analysed the different types of machine learning.
Due to this particularity, complex tasks can be performed in a much shorter space of time than would be needed by humans, thus improving efficiency.
Indirectly, this frees up time and resources that can be used for other types of activities that, by their nature, may require greater human dedication.
Time savings are also due to the ability of machine learning to identify inefficiencies and opportunities for improvement.
Increased accuracy and fewer errors
The automation of tasks helps to reduce human errors such as inattention, tiredness or bias.
In this way, minimising these possible failures increases the accuracy of the tasks and, therefore, the quality of the results.
Likewise, because machine learning is based on continuous learning, accuracy is refined and improved over time.
Linked to this idea, we also find anticipation or prediction of possible failures or errors thanks to the accumulated knowledge of previous experiences.
