Machine Learning is a concept that was developed in the 50s. However, the last two decades have led to a sustained improvement of the capacity of Machine Learning (ML) especially in managing businesses.

ML is the quality of machines being capable of performing human activities with minimal to zero human intervention.

This is like the Google Self-Drive Car which uses Artificial Intelligence to make decisions on where to turn, brake or drop passengers.

The car uses its sensors to pick signals from its surrounding with the aim of making appropriate decisions.

The signals could be picked from Google Maps, thermal signals and road markings. Machine Learning makes the car predict appropriate actions to make whenever it interprets these signals.

What does ML mean for Your Business?

Research indicates that while most businesses have elaborate plans to incorporate ML in their daily operations, quite a number of industries have embraced the idea already.

For the mundane tasks like clerical duties, business could simply employ the use of technology to be efficient. The tasks may be registry, data collation, interpretation and presentation.

This could be done through the use of Biometric registers, data mining tools and online data analytics platforms.

Apart from saving your staff the pain of going through loads of data, you also get pin-drop accuracy with machine learning.

In this guide, we highlight how different types of Machine learning can help businesses to maximize their productivity.

Leveraging Machine Learning to Enhance Productivity

Machine Learning could help your business identify its challenges and work towards mitigating them.

The first step to leverage ML in your business is through identifying an existing problem.This way, you get to set specific targets to achieve within a specific time-frame.

For example, if you want to investigate why your news stories mostly get negative comments, you would ask yourself how you could get your customers more satisfied.

In machine learning, however, the question would be more of something like: What are the specific things our readers hate about the content.

Identify a Metric

If you want to monitor customer satisfaction, then you will leverage metrics like stay duration on the website, drop-off points, comments sections, likes, shares and a raft of other metrics like star rankings.

Identify a Statistical Approach

Instead of using reactionary methods of improvement, businesses could start predictive analysis of future trends. This way, also known as the BlackBox ML, ensures businesses attain more accuracy, and are prepared for the after-shocks of their decisions.

The black-box ML is able to identify the specific types of people who, for example do not find your content relatable and why. This way, you are better-placed to improve these problematic areas.

Entertainment & Media

In the Entertainment Industry, Netflix already uses Machine Learning to predict what users love to watch based on the feedback they give online. Other useful data are derived from big data analytics.

This example shows just how ML could be used to provide clients with exactly what they want, when and how they would like to have it.

Finance Businesses: With hundreds of millions of people having credit cards, attempting to manually detect fraud is clearly impossible. In 2016, $16 billion was lost through identity theft and fraud. To curb this, Fraud Detection Machine Learning uses a set of algorithm to identify and flag down suspicious transactions.

This way, companies get a better shield from losing clients’ money to fraudsters.

Healthcare: Through the use of the pattern recognition, health practitioners are able to identify cancerous cells in humans early enough before they advance to critical stages.

Infervision helps to diagnose the disease more effectively. Radiologists are able to identify Cancerous cells earlier enough in the lungs, and recommend patients for early treatment.

Manufacturing: In the vehicle industry, manufacturers use big data and Machine Learning to detect and repair of damaged parts. Volvo for example, uses these technologies to identify engine failures, when vehicles need service, and safety monitoring.

Likewise, BMW, the leading manufacturer of Level 5 driverless cars uses ML and AI in all the processes of production, marketing and maintenance.

Importance of Machine Learning to businesses

Customer Lifetime Value Prediction: Through ML, businesses can use big data to derive important insights like customer preferences, purchasing patterns, and behavior.

This helps the business to better target customers with products, or manufacture products tailored to meet customer needs.

Financial Analysis: With Machine Learning, companies are able to make use of their data to help analyze the firm’s finances at the touch of a button.

Cyber Security: Machine Learning allows companies to identify patterns in their daily operations. This means that whenever any unusual patterns are detected in the system, they can easily be flagged down and be investigated.

Conclusion

Machine Learning helps businesses in more ways than just automating processes. From reducing the time taken to do iterative activities, to identifying unusual patterns in a business management system, Machine Learning is fast becoming much more than just a compliance requirement. It is the future of business.