Machine Learning Business Benifits & Its Use Cases

With the explosive growth of data science, more and more businesses are focusing on improve their business processes by harnessing the power of technologies like  

    Machine learning (ML)
    Big data
    Artificial intelligence

Among them, machine learning is a technology that helps businesses effectively gain insights from raw data. Machine learning?specifically machine learning algorithms?can be used to iteratively learn from a given data set, understand patterns, behaviors, etc., all with little to no programming.

Machine learning overview :

Before we look at ML benefits, we need to have a basic understanding of how ML works. Machine learning refers to the process of extracting meaningful data from raw data sets.

For example, let?s consider an online retail store that captures the user behavior and purchases within the website. This is merely data. But machine learning plays a significant role, enabling the online store to analyze and extract the patterns, stats, information, and stories hidden within this data.
Machine learning is quickly becoming ubiquitous across all industries from agriculture to medical research, stock market, traffic monitoring, etc. For instance, machine learning can be utilized in agriculture for various tasks such as predicting weather patterns and crop rotation.
Machine learning can be combined with artificial intelligence to enhance the analytical process gaining further benefits to businesses. Services like Azure Machine Learning and Amazon SageMaker enable users to utilize the power of cloud computing to integrate ML to suit any business need.

User behavior analysis :

Analyzing user behavior is one of the most common use cases of machine learning especially in the retail sector.

Think of a retail experience. Whether online or in person, businesses collect a vast amount of customer purchase information. Running this data through a machine learning algorithm allows businesses to predict consumer purchasing habits, market trends, popular products, and so on, allowing retailers to make informed business decisions based on this predicted information. For instance, ML enables companies to:

   - Make accurate decisions on managing stock
   - Streamline ordering according to market and consumer demand
   - Increase the overall efficiency of the logistical and operational processes
   - Integrate with marketing platforms to directly market products targeted at
     specific consumers

In an online environment, ML can :

   - Analyze user browsing habits
   - Predict user preferences accurately
   - Offer targeted suggestions

Here is a couple more examples :

In a pharmaceutical business that runs drug trials, user behavior analysis can help determine the effectiveness of drugs and predict any anomalies and outliers.
In the maritime industry, a logistics company can predict shipping demand by feeding the logistical data such as routes, transported goods, durations, etc. to a machine learning algorithm.

Analyzing user behavior is not limited to consumers. Any entity in this context can be considered users who interact with the business. ML can be used to extract hidden patterns and behaviors that may not be readily visible on the surface, offering businesses a far greater understanding of the overall business processes.

Security improvements :

With the flood of web-based technologies, the world has become more and more reliant on web services. This has led to a more connected and convenient lifestyle. However, there are also some risks associated with it:

   - Phishing attacks
   - Identity theft
   - Ransomware
   - Data breaches
   - Privacy concerns

Businesses follow various prevention and control mechanisms to ensure the security of the users and business. Some of them include firewalls, intrusion prevention systems, threat management applications, strict data storage policies. In large companies, dedicated security teams consistently monitor, update, and fix vulnerabilities in online applications.


Financial management-
Machine learning algorithms can be used in financial analytics for :

   - Simple tasks, like predicting business expenses and performing cost analysis
   - Complex tasks such as algorithmic trading and fraud detection

All these use cases rely on analyzing historical data to predict future outcomes accurately. The accuracy of these predictions can fluctuate depending on the ML algorithm and the provided data.