Predictive analytics is a category of data analytics aimed at making predictions about
future outcomes based on historical data and analytics techniques such as statistical modeling
and machine learning. With the help of sophisticated predictive analytics tools and models, any
organization can now use past and current data to reliably forecast trends and behaviors
milliseconds, days, or years into the future. Retailers often use predictive models to forecast
inventory requirements, manage shipping schedules and configure store layouts to maximize sales.
Airlines frequently use predictive analytics to set ticket prices reflecting past travel
trends. Hotels, restaurants and other hospitality
industry players can use the technology to forecast the number of guests on any given
night in order to maximize occupancy and revenue. Predictive analytics can also be used to detect
and halt various types of criminal behavior before any serious damage is inflected. By
using predictive analytics to study user behaviors and actions, an organization can detect activities
that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.
Models are the foundation of predictive analytics. They are the templates that allow users to
turn past and current data into actionable insights. Some typical types of predictive
models include: • A Customer Lifetime Value Model pinpoints
customers who are most likely to invest more in products and services.
• A Customer Segmentation Model groups customers based on similar characteristics and purchasing
behaviors • A Predictive Maintenance Model forecasts
the chances of essential equipment breaking down.
• A Quality Assurance Model spots and prevents defects in products and services.
Model users have access to an almost endless range of predictive modeling techniques. Many
methods are unique to specific products and services, but a core of generic techniques
are now widely supported across predictive analytics platforms.
Decision trees, one of the most popular techniques, rely on a schematic, tree-shaped diagram that's
used to determine a course of action or to show a statistical probability. The branching
method can also show every possible outcome of a particular decision and how one choice
may lead to the next. Regression techniques are often used in banking,
investing and other finance-oriented models to forecast asset values and help users understand
the relationships between variables, such as commodities and stock prices.
On the cutting edge of predictive analytics techniques are neural networks. Neural networks
are algorithms designed to identify underlying relationships within a data set by mimicking
the way a human mind works. While getting started in predictive analytics
isn't exactly a snap, it's a task that virtually any business can handle as long as it is committed
to the approach and is willing to invest the necessary time and funds. Beginning with a
limited-scale pilot project in a critical business area is an excellent way to cap start-up
costs while minimizing the time before financial rewards begin rolling in.
Once a predictive analytics model is put into action, it generally requires little upkeep
as it continues to grind out actionable insights for many years.