Machine Learning in Business

Machine learning has been evolving at a rapid rate in the recent past thanks to the fuel provided by new technology. Businesses are embracing it in a never-before-experienced rush. All this can only be attributed to the numerous benefits that come with it that any business that wants to stay ahead of the competition will want to tap into. Let’s look deeper into Machine learning (ML) to understand how you can apply it in your business and the benefits you stand to enjoy when you use it.

What is Machine learning (ML)?

This is a branch of artificial intelligence (AI) that involves building systems that learn using data and algorithms and gradually improve performance and accuracy. To perform better large amounts of data are required.

Algorithms are trained to make predictions when uncovering insights after mining large amounts of data. The insights will then help in decision-making within businesses and applications. 

Benefits of machine learning in business

Machine learning helps in extracting insights from huge amounts of raw data. This can be leveraged to predict and try to understand customer behavior which can be an advantage to a business. Let’s look at the benefits that your business stands to gain when you adopt ML.

Product recommendation

You have probably experienced this before. You are browsing through Amazon and decide to read a product description like let’s say of an iPad. Then the next day you are on Facebook, and you see an advert for the same iPad. That is ML at its best. The technology allows marketers to tinker with the way they market, trying to get the best out of every marketing opportunity. This has had a big impact on eCommerce, as marketers can tailor their strategies making them more effective and in the process leading to huge savings in marketing costs.

Optimizing market campaigns and detecting spam

Segmentation of customers and personalizing content have optimized market campaigns. The message is more personalized and relatable, and will most likely touch on the preference of the customer due to insights offered by ML. This will lead to more conversions as compared to outdated cold pitching market campaigns.

Spam detection has also greatly benefited from machine learning. Previously, email service providers used to depend on specific laid-down rules to classify a message as spam. ML in faster ways than ever before is filtering messages by automatically generating new rules using neural networks.

Customer lifetime value prediction

By having large amounts of customer data, today’s business can generate valuable business insights. Analyzing this huge amount of data can provide insights into the customers’ purchasing habits, requirements, and demands. Getting to know your customer lifetime value is valuable in that it will help you create personalized offers to them based on their purchasing and browsing histories.

Eliminates manual data entry

Erroneous and duplicated data are among the major things businesses are facing today. These errors can, however, be avoided by using ML or predictive modeling methods. As a result, employees can concentrate on assisting the business in other roles.

Recruiting

ML and AI are also dominating the recruitment of employees. They have greatly advanced since their introduction, and are now helping reduce repetitive tasks, speeding up lots of processes and in the process end up saving lots of time. AI-enabled HRMs are also available, enabling businesses to develop job search engines, browse resumes effectively, identify the most qualified individuals, and conduct interviews without candidates having to come to the office.

Financial analysis

Using ML algorithms, financial analysts can complete tasks like calculating costs and estimating spending. Stock traders also rely on ML algorithms to predict the market before entry. For this process to be done accurately, historical data is examined to come up with the expected scenario. Using these timely and accurate projections, organizations can also maximize their profits and control their overall costs.

Diagnosis of medical conditions

By using successful treatment strategies and unique diagnostic tools, ML has assisted in medical diagnosis leading to reduced healthcare costs and improving patients’ health. Medical organizations which include hospitals and clinics apply it to prescribe medications, predict readmissions, provide near-perfect diagnoses and identify high-risk patients. The data that provides these insights is from patients’ records, symptoms, and other ethically sourced data sources.

Strengthening cyber security

In recent years, cybercrime has been on an upward trajectory in sophistication and the devastation caused. Money is no longer the only thing at risk but crucial and sensitive data is always on the line too. Breaching of sensitive data can be detrimental to an organization’s reputation. That is why modern analytic systems for data security have embraced ML. Now administrators can monitor activities watching for odd user behavior like breaches, fraud, and unauthorized access, among other issues. For financial organizations, these ML systems are so valuable.

Increasing customer satisfaction

Using ML predictive algorithms can give spot-on product recommendations to customers using previously collected data on them. Insights on customer behavior can also be retrieved from previous calls allowing an ML system to accurately assign the customer to the most suitable customer service representative. These all will contribute to an overall satisfactory customer experience. In ML incorporating voice cloning and realistic text-to-speech capabilities further redefine the landscape of customer interactions. Through innovative AI technology, customer service interactions are elevated to new heights of personalization.

Image recognition

ML is helping businesses make sense out of images. This aspect of ML has a wide range of use including in automated cars and facial recognition security systems. Supermarkets are using image recognition too to ensure all items are removed from shopping carts to reduce unintentional loss of sales, retailers are also using robots with computer vision and ML to scan shelves to determine items running out of stock or those misplaced.

Conclusion

Machine learning is a tool that all businesses are going to have to apply in one way or another in the not-distant future. The benefits are immense and greatly help reduce losses due to human error and maybe bias. But to be able to fully benefit from its many advantages, don’t make a rush decision and put the needs and goals of your company first  to make the right decision and not just one that is popular in your industry.

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