How Does Machine Learning Work in Business Management?

machine learning business management

Artificial Intelligence (AI) has greatly impacted the business world recently. Machine learning, a subset of AI, has similarly found a major audience in the corporate sector. Machine learning algorithms are being used to generate analytics that was not available before, and this data is changing how business management decisions are made.

Let us deep dive into machine learning and how it can benefit your business operations.


What is machine learning in the business world?

Machine learning is primarily used to gain analytical data and important business insights from huge volumes of raw data in the business world. Machine learning algorithms can learn from the data they analyze without having to be specifically programmed. Machine learning algorithms can quickly learn patterns in customer behaviors which are essential information for any company trying to increase its sales volumes.

But many business managers often confuse deep learning with machine learning. Both are subsets of artificial intelligence, so it is not like there is much stark difference between them. But some distinctions become quite visible if you pit deep learning vs machine learning. While machine learning can learn from and gain insights from raw data, this data has to be structured. 

On the other hand, deep learning is used to analyze unstructured data in the form of maybe images and text. Deep learning algorithms are also specifically programmed to mimic the human brain’s neural pathways. On the other hand, machine learning algorithms are more capable of learning independently without complex programming.

Deep learning also has applications in the business world, but machine learning algorithms are the more popular form of AI used in the business world now. The patterns that machine learning algorithms can identify help businesses improve their customer relations, and sales funnel to be more compatible with the average customer behavior.


7 Applications of machine learning in business management

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These are some of the ways how the insights from machine learning algorithms can be used to optimize business management practices:

1.  Better grasp of customer behavior 

In the retail sector, customers make hundreds of decisions about whether to buy, what to buy, how much to buy, etc. This means that daily, any retail company generates huge volumes of data on customer behavior. Machine learning algorithms can analyze this data and point out important markers of buyer behavior. 

What decisions encourage final purchase, a peak time for sales, best-selling product categories, etc., are all insights that can be gained from these machine learning algorithms. Once the patterns are identified, retailers can devise their sales funnel to cater to these customer behaviors to get the best results.

2.  Implement increased automation

Automation is a process of eliminating repetitive tasks from the responsibility of the workforce to save time and money for tasks that require a more human touch. These repetitive tasks are given over to software solutions or other automation systems that can perform the same pattern repeatedly without any errors. Machine learning helps to identify these repetitive rhythms where automation can be implemented. 

When machine learning algorithms go through all the data from the operational and manufacturing process of a company, they can identify which tasks are repeated multiple times in the same manner. Automation can then be considered for these tasks. In the manufacturing sphere, machine learning and automation are working together to improve the quality and efficiency of the production process constantly.

Machine learning can identify the system’s pain points, and then possible solutions for it can be worked out without waiting for systems breakdowns.

3.  Safer financial operations

Finance is a major sector where machine learning algorithms can use their predictive insights. Business managers have been known to use machine learning algorithms to identify fraud invoices and customer accounts. Machine learning can also predict spending patterns and is helpful for share trading. 

Advanced machine learning algorithms can be used to predict market conditions with certainty, and businesses can shape their financial portfolio and their own share prices according to this data.

4.  Advanced cyber-security

Machine learning algorithms are widely used to improve cyber-security at different business enterprises. Machine learning algorithms can identify vulnerabilities in a system before any risky situations arise. They can also use their predictive capacity to detect unscrupulous vendors, customers and employee behaviors. 

A very common security usage of machine learning algorithms is the email inbox spam filters. As the algorithms works keeps working on all company inboxes, it gains a lot of data to analyze and is able to eliminate and filter out all risky spam emails even before the employees get a chance to interact with it and corrupt the system.

5.  Implement product recommendation systems

Machine learning algorithms are now being used to create highly accurate and effective product recommendation systems for customers. These algorithms are able to learn from years of customer behavior and predict what kind of products each customer is most likely to buy. 

The algorithms also use each customer’s prior purchase history and buyer behavior analysis to predict items from the brand inventory that best suits their interests and which they are most likely to buy. Product recommendation engines are being touted as a great new sales tool and they are pushing up conversions for many retail brands.

6.  Ability to identify images

Machine learning is being used by many business managers for image recognition. Image recognition is being used for various purposes in different industries. In the retail sphere, cash-less checkouts and customer authentication services are making use of image recognition. Machine learning algorithms are able to mine lots of useful customer data from image recognition and help to predict specific behaviors.

7.  Better customer support

Machine learning algorithms are being heavily used for customer support services. These algorithms can go through hours and hours of customer-personnel interaction data to find the exact tone and keywords, and answers that satisfy different customer categories. Most companies report higher customer satisfaction after introducing machine learning-powered customer support services.


How to effectively implement a machine learning powered strategy?

The seven applications listed above are just a few common ways machine learning is used for improved business management. Many innovative start-ups and large-scale market leaders have their own machine learning algorithms perfected for more specified applications. But it is not just enough to integrate some machine learning algorithms into your operational systems. There must be a well-thought-out plan to utilize these algorithms’ insights to create effective organisational changes.

Experts suggest that one needs to have a more horizontal approach to machine learning applications within a company. Insights from machine learning algorithms cannot be restricted to one department. Every department or team within the company can make use of the same data insights to make their own improvements. For example, an insight into customer behavior can be used by the sales team to create sales decks, the marketing team can use it to build targeted campaigns, and the customer support team can use it to improve their customer satisfaction ratings.

Another important aspect to remember while introducing machine learning is that some machine learning algorithms have huge data resource requirements. To get accurate and effective results from machine learning algorithms, it is very important to feed the algorithm the right kind and quality of data it needs. 

Not all organizations will be able to fulfill this need. So before you commit completely to a machine learning-driven decision-making process, ensure that your organization runs a proof-of-concept phase with the machine learning algorithm. In this phase, it can be tested whether it is feasible to use the algorithm, and this testing phase will not cost the company major financial setbacks if there is a failure.

But lastly and perhaps most importantly, remember that machine learning can never really replace your human workforce and their ideas. So, when introducing machine learning into the operational workflow, be sure to provide your workforce with ample time and assurance so they can feel confident and comfortable with the changes being brought in. Only when a workforce is willing to take full advantage of the machine learning insights will the organization be able to reach its full potential.

If you are part of the management team of any business and trying to edge out your competitors, machine learning can be a helpful partner for you. It will give you insights that will make your decisions more cost-effective and goal-oriented and statistically improve your chances of success. So, begin the process today and adopt machine learning as part of your business management as soon as possible. 


About Author- Fatema Aliasgar is a writer and editor based in Mumbai, India. She has 7 years of experience writing blogs and articles. She likes to read non-fiction and play board games with her kids during her free time. 

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