HomeMACHINE LEARNINGThe Emergence Context Of Machine Learning

The Emergence Context Of Machine Learning

Context of Machine Learning: A little over 40 years ago, the world’s first great technologies, such as cell phones and computers, appeared, applied for everyday use.

The 1980s saw a significant milestone with the birth of the internet and operating systems such as Windows. In the 1990s, technology advanced with the creation of the mighty Google. 

But it was not until the 2000s that the world saw an accelerated technological advancement.

In its first ten years, the cell phone started to take photos; Apple launched the iPhone, and people were able to make the first video calls. It was when we entered social networks, such as Orkut and Facebook, in addition to watching and posting videos on YouTube. 

That was the great decade of digital transformation. After that, starting in 2010, the concern was another. In customer service, for example, the first challenge of the 2000s was implementing digital channels that facilitate customer access to information with fewer and fewer touchpoints. 

In this second wave of digital transformation, the question is to make the process faster, more accessible, efficient, and less costly for the company. Therefore, it was necessary to look into  Artificial Intelligence.

Artificial Intelligence And Machine Learning 

Being complementary to AI, Machine Learning brings the ability to improve customer service.

This is because companies moved from the scenario where there was a virtual chatbot, for example, that answered questions with answers listed in the bank, to a type of technology that allowed this robot to learn each time it answered,

This means that when he can’t answer a question, he suggests other options until the correct one is presented. In the subsequent visits, he can deliver the right answer more assertively.

This process can occur in several ways, such as evaluating how often and for why customers are referred to human service. In this way, a new learning opportunity for the robot is identified.

Analyzing customer and employee feedback, in addition to successful service, the system can learn and offer: 

  • 24/7 availability;  
  • personalization when talking to the consumer; 
  • humanized care; 
  • speed; 
  • fewer touchpoints; 
  • reduced costs for the company. 

The result? A more satisfying customer experience . After all, we are talking about consumers of different generations, but much more attentive and critical; they are also subject to a greater volume of propaganda and, therefore, immediate. That is, they demand the best service and problem-solving instantly. 

Machine Learning And Other Technologies 

To optimize the customer experience, more and more companies are turning to self-service and robots. That’s where Machine Learning can make all the difference.  

In general, this machine learning can be done in a few ways: 

  • supervised by a programmer, who provides examples to create a recognition pattern; 
  • independent, in which the system makes its associations based on data analysis; 
  • with a combination of supervised and unsupervised learning; 
  • through reinforcement, the system receives positive and negative feedback through the combinations it makes. 

Given these possibilities, the systems become more accurate, helping the operation of other technologies with which it is associated.

Chatbots And Virtual Agents 

In the case of chatbots and virtual agents, it is Machine Learning that enables language processing.  

For bots, this process allows the robots to understand a text or voice message from the consumer and the present. As a result, options to follow the service. 

In the case of agents and virtual assistants, it is this technology that allows Siri and Cortana — Apple and Windows, respectively: 

  • respond as if you were a natural person; 
  • understand requests; 
  • present correct results; 
  • learn from user habits. 

Robotic Process Automation

We know that with RPA, we can guarantee agility in the daily life of an organization. With robotic process automation, many repetitive tasks are done automatically — automatic responses can be sent, and it is possible to communicate with other systems. 

In customer service, this technology allows you to deploy bots to run simple processes. For example, presenting a menu of options and directing the customer to the next step. With this, it is possible to reduce the costs for employees of the company. 

Combined with Machine Learning, the system could be capable of better results. In the example, based on the person’s initial message, it would be possible to identify that the problem does not match the menu options, quickly directing them to a human operator.

Human Service  

In human care, the use of Machine Learning has allowed a significant advance in everyday life. This technology helps agents recognize who the customer is. Thus, you can quickly access data such as your history and the solution to your problem. This is another factor that contributes to the reduction of the waiting list.

In this way, the service is faster and with better rapport, as the consumer’s profile and the possibility of having contact are identified at the very beginning. For example, seeing that he made a purchase whose product has not yet been shipped, he can prepare for a claim and take steps such as following up on the shipment, canceling the purchase, or informing a new deadline.

Management Processes 

In managerial processes, Machine Learning was a significant advance. In addition to allowing integration between platforms, this technology is fundamental for decision-making.

In the Big Data scenario, it is possible to classify data and supply systems, such as CRM. From this, the company will know who its customer is and how it behaves, having predictive insights.

Also Read: What Is Machine Learning: Discover Everything

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