What is machine learning used for?#
By automatically discovering patterns and relationships in historical data, machine learning allows businesses to forecast future outcomes, categorise information, and make automated, data-driven decisions. This leads to increased efficiency, reduced risk, and the ability to personalize customer experiences at scale.
How does machine learning work?#
Instead of being given rigid, step-by-step instructions, a machine learning system is "trained" by feeding it large amounts of relevant data. For instance, to build a system that predicts which customers might not renew a subscription, it would be shown historical data of thousands of past customers, including their usage patterns, support interactions, and whether they ultimately renewed.
The system analyses this data to identify the subtle factors and combinations of behaviors that are most correlated with a customer leaving. It builds a statistical model based on these learned patterns. Once trained, this model can then analyse a current customer's data and calculate the probability that they will not renew, allowing the business to intervene proactively.
How is machine learning different from rules-based AI?#
Machine learning is a specific method for achieving artificial intelligence. It differs from older, "rules-based" AI systems where human experts had to manually write every single decision-making rule (e.g., "IF a transaction is over $1,000 AND from a new location, THEN flag as suspicious"). Instead of following pre-written instructions, a machine learning system learns its own rules directly from data, making it far more powerful and adaptable for complex, real-world problems.