How machine learning enhances decision-making in business

Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and models that enable computer systems to learn and make predictions or decisions without explicit programming. In general terms, it creates systems that can automatically analyze and interpret data, identify patterns and improve their performance or behavior over time through experience.

In traditional programming, developers write explicit instructions to solve a specific problem. In machine learning, instead of explicitly programming a solution, the system is trained on a large amount of data and learns from that data to recognize patterns or relationships. This training involves using mathematical and statistical techniques to develop models or algorithms that can generalize from the provided data and make accurate predictions or decisions on new, unseen data.

At its simplest, machine learning can be used to automate tasks by training a model to perform a specific task, such as image recognition or natural language processing.

In more complex uses, it can analyze customer feedback, behaviour and sentiments to enhance the understanding of customer sentiment, improve customer satisfaction and make data-driven decisions to drive growth and success.

Dr. Rushdi Alsaleh, who teaches Business Analytics, Machine Learning Tools and Techniques, and Predictive Analytics: What Works? in University Canada West’s MBA program, says there is a huge focus on artificial intelligence and machine learning in different fields to test the ability to automate various tasks.

“It’s interesting how these models can mimic the functionality of the human brain — how humans are thinking, how humans can make decisions, business decisions and rationalize them,” Dr. Alsaleh says.

How can machine learning be integrated into business

Machine learning can be used in business in various ways. It can help businesses gain insights from data, improve operations and adapt to ever-changing markets.

Here are some common applications of machine learning in business:

Predictive analytics: Machine learning algorithms can analyze historical data and patterns to make predictions about future outcomes. Businesses can use this capability to forecast sales, customer behaviour, demand for products and other important factors, enabling better resource allocation, inventory management and strategic decision-making.

Customer personalization: Machine learning algorithms can analyze customer data, such as demographics, browsing behaviour, purchase history and social media activity, to segment customers into distinct groups with similar characteristics. This segmentation allows businesses to tailor their marketing efforts, personalize customer experiences, and deliver targeted recommendations or offers. Machine learning algorithms can also analyze customer preferences, behaviors and historical data to generate personalized recommendations. This is particularly useful in e-commerce, streaming services and content platforms, where businesses can suggest relevant products, movies, music or articles to enhance customer engagement, increase sales and improve user satisfaction.

Customer feedback analysis: Machine learning can be used to extract valuable insights from customer feedback, enabling businesses to identify patterns, trends and common concerns, which can guide product development, customer service improvements and marketing strategies. Models can also be trained to automatically classify text as positive, negative or neutral, allowing businesses to quickly understand the overall sentiment of their customers’ feedback.

Fraud detection and risk assessment: Machine learning algorithms can be trained on historical data to identify patterns and anomalies that indicate fraudulent activity. Businesses can utilize these algorithms to automatically detect and prevent fraud in areas such as financial transactions, insurance claims, cybersecurity and identity verification. Machine learning can also assess and mitigate risks by analyzing complex data sets and providing insights into potential threats or vulnerabilities.

Monitor brand reputation: By analyzing social media posts, online reviews or comments, businesses can monitor and track their brand reputation in real-time, identifying potential issues or areas for improvement.

AI chatbots: AI technologies enable businesses to automate customer interactions and provide 24/7 support. Chatbots can handle customer inquiries, provide information and assist with basic tasks, improving customer service and reducing response times.

Process optimization: Machine learning can be used to optimize various business processes. For example, in supply chain management, algorithms can analyze historical data to predict demand, optimize inventory levels, and optimize logistics and distribution routes. Machine learning can also be applied to manufacturing processes to improve efficiency, quality control and predictive maintenance.

Image and video analysis: Machine learning algorithms can analyze images and videos to perform tasks like object recognition, image classification, facial recognition and video surveillance. These capabilities find applications in industries such as retail, health care and security.

Further learning

Machine Learning Tools and Techniques (BUSI 651) is an elective in University Canada West’s Master of Business Administration program. In this course, students explore the world and models of machine learning and how to use best practices with data to help the learning algorithm find patterns to map the target attributes. Students consider different patterns in outputs to discover if the machine learning model can predict new data sets of potential new targets.

In conclusion, machine learning can be a powerful tool for businesses looking to track customer behaviour. By analyzing data on customer interactions, preferences and purchasing behaviour, machine learning algorithms can help businesses identify patterns and trends that can help them optimize their operations, reduce costs and improve customer satisfaction.

Published on June 2, 2023.