How Businesses Use Machine Learning to Improve Processes

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Machine learning and artificial intelligence have become the latest buzzwords in the world of technology and business. While both these terms are often used interchangeably, there are subtle differences between them. Artificial Intelligence is a broader concept that involves building machines which can imitate human intelligence and behaviour. Machine learning is an offshoot of artificial intelligence and involves machines understanding repetitive processes in order to recreate them. This application of machine learning has made it highly relevant for businesses looking to improve their internal processes.

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

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