Artificial Intelligence

Improve Decision making with Machine Learning Technology

I’m quite sure you would have heard of machine learning or seen it in your news feed, social media posts, etc. with everyone wanting to understand the “magic” in machine learning and how to take advantage of them. Before we dive deep into this topic, let’s briefly have a look at what machine learning really means.

Machine learning is a branch of artificial intelligence that allows machines to learn and perform tasks without being programmed explicitly to do so. In other words, the machine learns from data fed to it and derives insights and patterns from it.

The term Machine learning (ML) was coined by Arthur Samuel of IBM in 1959 and has gained prominence in recent years with lots of powerful and useful algorithms for huge data analysis and modeling. ML is rapidly evolving and has prompted the invention and rise of other technologies such as fraud detectors, chatbots, recommendation systems amongst others.

In the field of machine learning, there are two main types known as supervised learning and unsupervised learning. In supervised learning, the machine is given labeled training data to learn so as to be able to accurately label any other data given to it. In unsupervised learning, the machine learns unlabeled data, seeking patterns with no human intervention.

Nowadays, businesses and organizations are leveraging the power of artificial intelligence and machine learning in order to make important data-driven decisions. According to research, 2.5 quintillion bytes of data are produced every day, hence the need for investing in machine learning technology to unravel the data. With the help of recent and upgraded ML tools, big data can be analyzed so as to extract meaningful information, and the actionable insights gotten would then assist the organization in making well-informed decisions backed by data. There are multiple ways in which organizations can benefit from using machine learning in their workplace. Below, we would examine 10 different examples:

Analyzing customer reviews and sentiments

A lot of data is generated nowadays from customers; this data ranges from queries and reviews to sentiments displayed by customers. Natural language processing (NLP), a branch of AI makes it possible for machines to comprehend and analyze human language. By using NLP combined with ML algorithms, these reviews can be properly analyzed to understand what the customers are saying about a particular brand or issue. Analyzing these reviews helps in discerning customer’s different preferences and these extracted insights can then be used to improve customer service and experience.

Sentiment analysis can also be carried out on social media posts, surveys, ratings to discover certain trends over time, figure out whether customers like or dislike a particular feature or upgrade. When this type of analysis is carried out, companies are able to respond faster to negative feedback to suit the customer’s needs. Irrespective of the type of analysis involved, these results can be used to further help the organization in making better decisions about their brand, products, and potential upgrades.

Predictive Modeling

Another aspect of machine learning is predictive modeling. Predictive modeling makes use of data and statistics to create models able to predict future outcomes. In machine learning terms, training data is being studied, understood, and modeled so that it is possible to predict the next occurrence with very high accuracy. There are two types of predictive models which are classification and regression models. Regression simply involves the prediction of a continuous variable (y) based on predictor variables (x) while classification involves the prediction of classes i.e. identifying which set of categories data belongs to.

Businesses can take advantage of predictive modeling to predict future sales and revenue in different locations. This would greatly assist them in avoiding potential losses and in carrying out effective investments. Industries can use also machine learning to predict the occurrence of defects in advance and reduce costs.

Customer Demand Forecasting

Demand forecasting is the process of making estimations of future demand for a product or service. Using machine learning techniques, it is possible to predict the number of goods and services to be sold during a specified period. By carrying out time series-based forecasting and uncovering hidden patterns, organizations are able to know which and which goods are likely to be in demand resulting in proper inventory evaluation and supply chain management.

Demand forecasting also helps organizations in marketing plans and organizing campaigns at the appropriate time.

Image Recognition

This is also known as computer vision and deals with how machines can interpret and classify information gotten from digital images or videos. In this way, the computer imitates the way humans see and identifies images and tries to be able to identify more images after it has been trained already. Image recognition is used in many industries today such as healthcare, security, retail, e-commerce, marketing, agriculture, etc.

Customer Lifetime Value (CLTV) prediction

In today’s world, there are lots of businesses competing against each other to attract and serve customers better than their competitors. Building up a customer base can be a significant achievement for a business but it is known that not every customer is valuable to a business so this is where CLTV prediction comes in. Customer Lifetime Value (CLTV) represents the total amount of money a customer is expected to spend in a particular business during his/her lifetime. This metric helps in estimating the total profit that a business would receive from a specific customer over time so it helps such businesses in making decisions about retaining existing customers or acquiring new ones.

Customer Churn

Customer retention is one of the major challenges faced by most businesses today. After getting a customer to patronize your business, there is a need to retain such a person to avoid a switch to competing parties as customer churn can really hurt the business and damage its reputation. For clarification, Customer churn is the tendency of customers to quit a brand or stop patronizing a particular business. Customer churn rate indicates the growth/decline of a business.

To curb customer churn, ML can be implemented to study the online behavior of customers, their purchasing patterns, and overall satisfaction. Being able to detect customers likely to churn helps the business in taking prompt measures to keep such customers.

Fraud Detection

Fraud is a big problem faced by financial institutions all over the world. In 2019, about 1.7 million reports were fraud-related leading to the loss of huge amounts. Now, with the help of machine learning algorithms, fraud can be tackled actively and prevented to a minimum as the machine is able to differentiate fraudsters from legitimate clients by analyzing a lot of financial information. By also analyzing fraud-related data, institutions can gather relevant details and information for tackling fraud and making better decisions.

Medical Diagnosis

ML can be properly leveraged to positively impact the healthcare industry such as helping with a medical diagnosis. After being exposed to large volumes of health data containing patient records along with the symptoms recorded, ML is able to correctly diagnose a patient and offer useful recommendations. These algorithms are also able to make health risk predictions based on existing data from similar cases.

For example, Google recently developed an AI algorithm to predict breast cancer at an early stage. The AI analyzes mammograms — the X-rays commonly used to check for breast cancer — to determine whether the disease is present, and it was recorded that this system was so good that it actually outperformed actual doctors. This is a case where AI and ML really impact positively.

Pricing Optimization

Price optimization can simply be referred to as the setting of prices driven by ML algorithms to increase sales and drive more engagements. This is another great advantage of implementing ML in your business. By learning from available customer information, seller data, past promotions, marketing campaigns, and inventory and supply data, ML is able to determine the best price for each good and service which would drive much more sales.

Chatbots and Virtual Assistants

In a bid to improve customer experience and satisfaction, Many companies are now using support-focused customer analytics tools enabled with machine learning such as chatbots and virtual assistants to initiate prompt service.

These AI-enabled tools can converse with customers like service agents and help them with quick and helpful solutions. By taking note of customer queries in form of data, the company can be able to create more actionable tips in the form of help articles.


ML will help businesses in detecting malicious attacks and stopping them before it causes harm. ML also helps human analysts in carrying out vulnerability assessments, analyzing the network, and other potential attacks.

So far so good, we have been able to look at the numerous ways machine learning (ML) can be implemented to make better decisions in a business or organization. Simply by carrying out some exploratory data analysis, useful and actionable insights can be gotten. These ways can also be applied to small and medium businesses to improve customer service and drive sales.

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