What Is Predictive Analytics?
Predictive analytics is the practice of using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. Businesses often need to make strategic decisions about the future with limited insight, relying on past performance or intuition alone. This makes it difficult to anticipate market shifts, customer behavior, or potential operational risks.
How it helps#
This technology provides data-driven forecasts that help leaders make proactive, informed decisions. It allows a business to anticipate customer needs, optimize inventory, identify fraud risks, and target marketing efforts more effectively.
How it works#
The process begins by gathering large amounts of relevant historical data, such as past sales figures, customer demographics, and website activity. This data contains the outcomes you want to predict (e.g., a customer making a purchase) and the many factors that may have influenced those outcomes.
A statistical or machine learning model is then "trained" on this historical data to find significant patterns and relationships. For example, it might learn that customers who view specific product pages and use a discount code are highly likely to buy again. Once the model understands these patterns, it can be fed new, current data to generate a score or a forecast about a future event.
How it is different#
Predictive analytics focuses on forecasting what will likely happen in the future. This is different from traditional Business Intelligence (BI) or descriptive analytics, which primarily summarizes past data to report on what has already happened. While BI answers "What were our sales last quarter?", predictive analytics answers "What are our sales likely to be next quarter?".