What is the Importance of Machine Learning in Business?

In 2023, machine learning continues to revolutionise the marketing industry, with its impact becoming increasingly prominent. A recent study by McKinsey & Company found that “by 2025, AI-driven marketing will be responsible for up to 80% of customer interactions, leading to a significant shift in how companies approach their business” (McKinsey & Company, 2023).

Additionally, these businesses are utilising machine learning to overcome some of the most difficult problems in marketing, such big data, personalization, and rapid customer service, in order to acquire an advantage over rivals.

In this article, we will first understand the importance of machine learning in business and then elaborate on different applications in business.

What is Machine Learning

Machine Learning is a subset of artificial intelligence (AI) that focuses on the development of computer algorithms that improve automatically through experience and by the use of data. In simpler terms, machine learning enables computers to learn from data and make decisions or predictions without being explicitly programmed to do so.

Types of Machine Learning

importance of machine learning in business

Supervised Learning : Supervised learning is a category of machine learning that uses labelled datasets to train algorithms to predict outcomes and recognize patterns

Unsupervised learning : Unsupervised learning in artificial intelligence is a type of machine learning that learns from data without human supervision.

Semi-supervised learning: Semi-supervised learning is a branch of machine learning that combines supervised and unsupervised learning by using both labelled and unlabeled data to train artificial intelligence (AI) models

Reinforcement learning: Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals.

Benefits of Machine Learning in Business

Recommendation Systems using machine learning

The majority of us may identify with the particular situation where Netflix picks our next three movies for us to watch and we wind up binge-watching them for three hours. Or an instance where we wanted to buy just one item, but ended up spending significantly too much money on our internet purchases. Thus, the question is raised: How did these robots get to be so intelligent that they seem to know what we want on a mystical level?

The answer is recommendation systems. Recommendation systems are AI tools that make recommendations online based on a series of algorithms, data analysis and artificial intelligence. With this understanding of customers, the impact is a direct boost in sales. Based on recommendation systems, customer profiling can also be done by grouping customers based on similar behaviour. 

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Fraud Detection using machine learning 

Another importance of machine learning in business is fraud detection. This application uses machine learning techniques to identify and prevent fraudulent transactions, activities, or behaviours that can cause financial losses or reputational damage to the businesses or their customers. Fraud detection can leverage supervised or unsupervised machine learning methods to learn from historical data and detect anomalies, patterns, or outliers that indicate fraud. For example, credit card companies use machine learning to flag suspicious transactions and alert the cardholders or the authorities.

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Customer Segmentation using machine learning

Customer segmentation is the process of grouping your clientele based on their preferences. It helps businesses better understand their clientele. Making strategic decisions about product growth and marketing is made easier when one is aware of the variations between different client groups. Finding patterns and insights in consumer data analysis is made easier with the help of machine learning. AI models are effective instruments for commercial decision-makers. It is far more difficult to correctly identify client categories manually or with traditional analytical tools than it is with them. 

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Sentiment Analysis using Machine learning

Sentiment analysis is the process of identifying customer feedback based on a piece of text. The goal that Sentiment mining tries to gain is to analyse people’s opinions in a way that can help businesses expand. By tracking social media mentions, written feedback and other text information, sentiment analysis helps businesses make better decisions. Sentiment analysis is important for businesses as it helps them understand customer feedback, manage brand reputation, inform product development, analyse competitors, and evaluate marketing campaign effectiveness.

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Demand forecasting using machine learning

Another importance of machine learning in business is demand forecasting. Demand forecasting enables businesses to foresee future demands for their products or services based on factors, such as historical data on sales, market trends, and customer behaviour. It relies on data-driven analysis to make well-informed projections. Demand forecasting can help businesses to optimise their inventory, production, distribution, or pricing decisions and avoid overstocking or understocking issues. For example, supermarkets use machine learning to forecast the demand for their products based on the sales history, weather, holidays, or promotions.

Following are the applications of using demand forecasting in retail.

Inventory Control

The secret to effective inventory management is demand forecasting. Retailers can guarantee they have adequate inventory to meet client needs without overstocking and allocating needless resources by forecasting future demand.

Strategies For Pricing and Promotion

Retailers have the ability to modify pricing in response to market conditions, seasonal trends, and predicted demand. 

Markets for New Products

Demand forecasting aids in estimating the possible demand for new items in these sectors. Retailers may make educated decisions about product creation, marketing strategies, and sales estimates by evaluating market trends, customer preferences, and historical data.

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Image Recognition using machine learning

Image recognition is a machine learning application that helps businesses to identify, classify, or analyse images or videos based on their content, features, or objects. Image recognition can use computer vision, another subfield of AI, to process and extract information from visual data. Image recognition can have many uses in business, such as security, quality control, medical diagnosis, face recognition, or product recognition. For example, car manufacturers use machine learning to detect defects or damages in their products using image recognition.

The necessity for organizations to recognize these traits is easy to comprehend. It enables them to analyze their clientele in great detail. In this approach, a fashion store can know that eighty percent of its customers are women, that their average age is between thirty and forty-five, and that they don’t seem to find any particular item in the store appealing. When they glance at a product in the store, their facial expression usually record what they think about the product. They must take note of all this information in order to identify their goals and modify their communication going forward.

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Optimising marketing campaigns using machine learning

By using data-driven decision-making and machine learning technology, marketing efforts can be optimized. It’s no longer the case for marketers to start a campaign with the hope that it would be successful, unlike in the past. Data is used in modern digital marketing strategies to help you reach the appropriate clients at the right moment.

Even marketing initiatives can be automated by machine learning technologies, saving you both money and time. For example, machine learning can assist with digital advertising campaigns by automatically identifying and targeting appropriate people and customizing material to meet campaign objectives.

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Recruitment using machine learning

Machine learning can be used in human resources (HR) to optimise recruitment, job performance tracking, employee retention, and more. For example, machine learning algorithms can evaluate candidate resumes and predict the best-fitting job candidates

In general recruitment using machine learning can be used in the following scenarios.

Candidate Sourcing and Screening – Machine learning algorithms can automatically search through large databases of job postings, resumes, and other sources to identify the best-fitting job candidates.

Candidate Assessment: Machine learning can help in the candidate assessment phase by evaluating job candidates to determine the best suited for the position.

Interview Automation – Interview automation uses machine learning algorithms to evaluate candidate responses during interviews and generate insights into their suitability for the role

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Predicting the customer’s lifetime value

The average revenue you can expect from a customer is measured as the customer lifetime value. The lifetime value of a customer is calculated based on the following formula:

Customer Lifetime Value (CLV) =

[Average Transaction ($)]   X   [# of Transactions]   X   [Retention time period]

For example, if you are a SaaS company and you sell $20 monthly subscriptions for customers who stay subscribed for an average of 18 months, your CLV is:

$20 (subscription cost) x 12 (transactions per year) x 1.5 years = $360 CLV

Knowing your CLV is important for business decision-making and getting a better understanding of your company’s financial health and future. 

Customer lifetime value is essential for many areas of your company’s future like advertising, marketing and sales. When you use machine learning to analyse and help predict customer monetary value, you can develop a potentially more accurate future picture of the business

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Financial Analysis using machine learning 

Financial analysis using machine learning is now considered a key aspect of several financial services and applications, including managing assets, evaluating levels of risk, calculating credit scores, and even approving loans. 

Fraud Detection Using Machine Learning

Fraud is a significant problem for financial services firms and banks, resulting in annual losses of billions of dollars. In the past, fraud detection systems were created according to a set of standards that were simple for contemporary scammers to go around. As a result, machine learning is used by the majority of businesses today to identify and stop illegal financial activities. Large data sets are scanned by machine learning algorithms to find unusual or unusual activity. These behaviors are then flagged for additional investigation by security experts.

Portfolio Management Using Machine Learning

Online tools known as “robo-advisors,” which employ machine learning, give investors automatic financial guidance. The applications create a financial portfolio based on an investor’s risk tolerance and goals using algorithms.

Loan Underwriting

Businesses in the banking and insurance sectors have access to millions of customer records, which they may use to train machine learning algorithms that will streamline the underwriting procedure. Businesses can save time and money by using machine learning algorithms to quickly decide on underwriting and credit scoring.

How to Apply Machine Learning to Your Online Business

Create and Optimize Content

You can use smart AI tools to research content better, write content better and finally optimise them for the right keywords. While SEO tools like writerzen provide you the right tools to optimise your content, you can use tools like writerzen and frase to optimise for long tail keywords 

Increase Personalization

You can provide the most individualised customer experience by using machine learning. An algorithm that monitors user behaviour at a fine level, determines the goods that users are interested in, and generates a customised homepage and recommendation list as a consequence can be used.

For example, Amazon employs artificial intelligence (AI) algorithms to determine which product recommendations are most likely to convert based on customer browsing patterns, goods in their basket, and purchase history.

Additionally, every buyer may receive customized offers via email from the same algorithm at the optimal moment to make a purchase.

Improve Marketing Automation

Let’s imagine that when a consumer subscribes to your newsletter or leaves their basket empty, you automatically send them an email. While the majority of brands will send a generic email, businesses that use machine learning can customize offers and content according to the customer’s previous browsing activity.

An offer on chew toys that is relevant to them might increase their likelihood of interacting with your brand again, especially if they browsed your selection of dog toys before subscribing to your newsletter.

AI-powered marketing automation solutions for SaaS firms can better segment leads by analyzing larger and heterogeneous data sources. Sales representatives can now give priority to leads that have a higher conversion rate.

Importance of Machine Learning in Business

The application of machine learning in businesses is only going to keep growing in 2024. By employing machine learning in various aspects of your business like research, marketing and sales, you can automate several tasks and improve the efficiency of your business.

Therefore, organisations can now benefit by understanding how businesses can use machine learning and implement the same in their own processes.


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