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The progress of machine learning is fueled by the virtually infinite amount of accessible, inexpensive data storage, and the development of less costly and more efficient computations. As a result, many sectors are now building more powerful models to analyze complex data while delivering quickly and more accurate answers on massive scales.
Machine learning is a branch of artificial intelligence in which computers use algorithms to understand data and find patterns, a skill that businesses can employ in various ways.
Here are five business applications of machine learning that are delivering significant benefits:
#1. Advanced Chatbots
The early version of the chatbots overcame the interaction gap between people and technology by allowing people to communicate with computers with scripted rules that instructed chatbots on what actions to execute depending on keywords.
However, machine learning and NLP or natural language processing, another branch of AI technology, allow chatbots to be more engaging and effective. These newest chatbots are more responsive to users’ requirements and speak more like actual people, hence being more productive.
#2. Decision Support
Another area where machine learning may help organizations transform a multitude of data into meaningful insights that generate value is decision assistance. Algorithms based on historical data or any relevant data sources may assess information and go through various possible outcomes at a volume and pace that humans can not match, allowing them to give suggestions on the right course of action.
For instance, clinical decision support technologies that use machine learning to educate physicians on diagnosis and treatment choices in the healthcare business boost caretakers’ performance and patients’ welfare.
#3. Recommendation Engines for Customers
Customer recommendation engines, which use machine learning to improve the customer experience and create tailored experiences, are powered by machine learning. Here, algorithms analyze data points about a single customer, like past purchases and other data sources, such as a company’s existing stock, demographic patterns, and the purchasing histories of other customers, to determine which solutions to recommend to each particular consumer.
For example, YouTube employs recommendation engine technologies to help viewers locate videos that match their preferences fast.
#4. Pricing Strategies That Change Over Time
Companies may use historical price data and data sets on various other variables to understand better how multiple factors affect consumer spending, such as the period of a day, climate, and seasons. Machine learning algorithms can take such data and integrate it with other market and customer data to help businesses dynamically price their items depending on the vast and varied variables. This method allows companies to optimize income.
The most obvious example of this variable pricing is Uber’s surge pricing, which occurs when many customers seek trips at the same time.
#5. Detection of Fraud
Machine learning’s ability to recognize trends and discover irregularities that deviate from those patterns makes it a valuable tool for identifying illicit practices. As a result, financial organizations have been effectively applying machine learning in this field for years.
One can learn a lot more through various online short courses available on the implementation of AI.
Therefore, it is evident that like manufacturing, retail, healthcare, life sciences, travel, and financial services, many other industries have also benefited from advances in machine learning, making it inevitable for the progress in each business activity.