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Machine Learning


What is Machine Learning?

Machine Learning is a subset/sub-category of Artificial Intelligence. It is the study of making machines more human-like in their decisions and behaviors by giving them the ability to learn and develop their own programs. This is done without human intervention / explicit programming. The process of learning is automated and improvised based on the machine’s experiences throughout the process. Quality data is fed into the machines, and different algorithms are used to build ML models for training the machines on this particular data. 

Machine learning (ML) extracts meaningful insights from raw data to solve complex and data-rich problems. ML algorithm learns from the data continuously and allows computers to find different kinds of hidden insights.

How is Machine Learning used in Businesses?

Various ML algorithms have gained extreme popularity in the business and analytics community. Factors like increasing volumes, easy accessibility & availability of data, faster computational processing, and affordable storage of data have led to a massive boom in machine learning. So, organizations can now benefit by understanding how businesses are using machine learning and implementing the same in their own processes.


  • Customer Lifetime Value Prediction
  • Eliminates Manual Data Entry
  • Predictive Maintenance
  • Product Recommendations
  • Detecting Spam
  • Image Recognition
  • Financial Analysis
  • Improving Cyber Security
  • Increasing Customer Satisfaction
  • Medical Diagnosis

How does Machine Learning work?

A machine learning system has 3 blocks: model, parameters and learner.

Model is the system that makes predictions based on data.

Parameters are the factors considered by the model for making predictions.

Learner adjusts the parameters and model for aligning the predictions with actual results.

How machine learning is used in customer service?

  • Chatbots: Chatbots have the ability to simulate an interaction with a customer support representative and respond to simple inquiries ensuring self-service solutions. Machine learning empowers chatbots to learn the usage of specific responses, gathering necessary information from users, and handing off a conversation to a human.
  • Virtual assistants: Virtual assistants differ from chatbots as they don’t try to simulate an interaction with an agent. Instead, Virtual Assistants focus on particular areas in the customer journey where assistance to the customers can be provided. When Virtual Assistants are enabled with machine learning capabilities, they learn the information they can pass along to agents and enhance the assistance. 


  • Content creation: Approximately 40% of customers claim that searches within the resource pages don’t show the articles that they’re looking for. Machine learning is used to analyze the data coming from support tickets and convert them into actionable insights for agents for applying to help articles. Agents take these suggestions and adjust the help articles, increasing the relevancy for customers to find.


  • Predictive analytics: Customer support needs analytics that is measurable for continuously optimizing, and machine learning helps to add a predictive element for support analytics. It utilizes data from previous interactions for determining the quantitative results in the future. These insights can be very useful for customer service organizations that want to deliver better customer experiences.


How Machine Learning Optimizes CS?

Customer support is one of the prime applications of machine learning because of unstructured data. This unstructured data that is generated through our conversations with customers, carries a lot of insights that are helpful in understanding what customers think and do. There are four ways that customer support teams can use machine learning. 


  • Tagging Incoming Conversations: One method for automating customer support is to route conversations to the right agent, quickly. You can tag a conversation with the context, language, and sentiment of the customer for easing the response process. Machine learning accurately identifies the correct tag for each conversation by using natural language processing (NLP). Looking at the patterns in language and text, machines can read and understand human conversation. Even though they don’t understand it in the exact way we do, they can still uncover the meaning and themes of a wide variety of topics. The more historical data the machine gets to read, the more accurate will the tagging be. Some machine learning tools let humans provide feedback on accuracy that improves the tagging over time. 


  • Predictive Support and Advice: For frequently asked questions (FAQs) that only require a straightforward and direct response, machine learning often predicts exactly what customers want to hear. 


  • Customer Insights: When you are running a large customer support team, it becomes difficult to decide the most important customer trends to focus on. One particular agent might see only 2% of the total volume, making it impossible for them to identify recent trends. Even though the teams are smaller, humans struggle for making accurate assessments of trends. This is where machine learning helps. By analyzing the data of customer conversation, machine learning uncovers trends that your team misses out on – simply because of the speed that machine learning computers can run through data. 


  • Improve Knowledge Base by Analyzing Search Activity: More often than not, customers resolve their own queries by using a knowledge base or a resource pool or help center. This is amazing because it’s much faster for the customers to receive help and cost-effective for the company that provides support. Machine learning helps to improve knowledge base through two approaches: highlighting the gaps in available content and improving the functionality of search to display help that is even more relevant. 



What are machine learning chatbots?

Machine learning refers to the ability of a system (in our case, the chatbot) to learn from its experiences of inputs. One way they achieve this is through natural language processing (NLP), which refers to the interaction between computers and human language.


But natural language processing is just a start. For achieving true general artificial intelligence, a chatbot/dialogue system should be able to do these three things:


          •  Provide an informative answer

          •  Stick to the context of the conversation

          •  Be indifferent from the human


While creating a chatbot, our aim should be to make one that requires least or no human interference. This can be achieved by two different methods: In the first method, the customer support team receives suggestions from Artificial Intelligence to improve customer service processes. The second method involves developing a deep learning chatbot that handles all of the conversations itself and removes the need for a customer service team.


When we talk about perceiving humans, we’re not quite there yet. Even the best of current day’s machine learning chatbots can’t be mistaken for a human. However, fortunately for brands, humans are still willing to talk and interact with bots as long as they are helpful, funny, or interesting, and they solve problems quickly.


A basic conversation with Siri or Google Assistant will help us realize their limited conversational skills. Playing different songs and setting up multiple alarms are their areas of expertise but a single attempt of general and fun conversation will probably end in vain.


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