Many repetitive, time-consuming processes previously performed by humans can now be automated through machine learning. The benefits of machine learning in business are plenty; not only is machine learning more accurate and faster, it also frees up time for employees to spend more of their time on tasks that add value to the organization.
In recent years, a growing number of organizations have adopted machine learning in order to automate several processes. Here are some of the main machine learning examples in businesses and their significance.
1. Provide better customer service
Waiting for ages on a phone line in order to get in touch with a customer service representative has now become a thing of the past. In today’s fiercely competitive marketplace, quality customer service can be the biggest differentiator and source of customer loyalty for a brand. Earlier, brands would have to expand their customer service teams in order to successfully reduce response times for customer complaints. Today, however, machine learning can streamline this process without the requirement of a large customer support team.
Machine learning in tandem with innovations like natural language processing (NLP) can understand customer complaints, sweep through the database for a resolution and drastically reduce the response time. A human customer rep can be roped in only for special cases which don’t match historical data.
Related article: The Importance of Data Processing in Machine Learning
2. Improved customer targeting and retention
Targeting the right customers and sending them the most appropriate communication at the exact right moment is critical for successful marketing. Doing so can significantly increase the number of leads and conversions generated while lowering your ad spends. But getting your audience targeting right requires careful analysis of volumes of past consumer and audience data. When done by a human, this can greatly increase the time required and the possibility of human error.
Machine learning algorithms, on the other hand, can quickly identify behaviour patterns and zero in on the most relevant users to target. Machine learning applications can also automate much of the communication throughout the consumer decision making funnel. This can help keep your consumers engaged and build top of the mind recall so that they become loyal customers.
3. Improve cybersecurity
With hackers becoming more advanced and sophisticated, cybersecurity is a growing concern for most businesses. This is especially true for businesses that deal with large volumes of sensitive data such as banks, NBFCs and software companies. Machine learning algorithms can be the best way to prevent security breaches and to quickly notify concerned parties if a breach does take place. Some of the most common machine learning examples in the field of cybersecurity are detecting anomalies in transactions or user access patterns and raising an alarm when such an event occurs.
An increasing number of financial institutions are understanding the vast potential for machine learning applications and are actively using them to identify cases of identity theft, tax fraud and unauthorized transactions. Machine learning applications have a definitive edge over humans in the realm of cybersecurity because they can even detect novel types of fraud that have never been done before. This makes them an integral part of an organization’s cybersecurity measures.
Related article: Types of Cyber Attacks to Protect Your Business Against
4. Improve talent acquisition
Finding the right talent is absolutely essential for the success of an organization. HR teams often have to sift through huge volumes of resumes and job applications in order to find the individual that would be the perfect fit for the role. But even then, the winning candidate might not be the best choice. As humans, there are a number of biases that we view the world through. Therefore, it’s very natural for most individuals to be swayed by these biases when judging the qualifications of a candidate.
A screen process driven by machine learning offers several advantages in this scenario. It can reduce the overall time spent in reviewing applications by scanning each of them and identifying those that fit the requirements best. It does so in a completely objective way, free from human bias. Once this initial screening has been done, the HR department will have far fewer applications to vet. Since they will be able to spend more time on the shortlisted applications, they can go through them in more detail and decide which ones to narrow down.
Artificial Intelligence and machine learning have become technologies that can help businesses improve their internal processes, strengthen their security and increase the efficacy of their marketing. Many companies are hesitant to adopt machine learning in business for two major reasons: they are unsure whether these technologies would make a certain section of their workforce redundant and they are unwilling to pay for these often expensive solutions. However, both these factors are largely untrue. While machine learning will require less human intervention for certain processes, this will actually free up time for your employees to focus on more value-driven tasks. This can improve employee productivity as well as employee satisfaction. And while machine learning applications do require a significant investment, they can actually save your company money in the long run. Through more efficient ad targeting and the potential for increased sales, machine learning in business is an investment which can pay for itself.
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