Predictive analytics uses predictive modeling and machine learning to help businesses forecast future business performance. It can be applied to a wide range of business problems, including predicting customer lifetime value, optimizing marketing campaigns and forecasting maintenance needs.
In the airline industry, for example, predictive models predict flight delays and optimize crew assignments. They can also help airlines predict passenger influx and outflux to avoid overstaffing.
Data mining
Data mining in predictive analytics uses sophisticated algorithms to discover patterns and insights hidden in large datasets. This process is a labor-intensive task, and it requires the combined efforts of data scientists, statisticians, and business analysts. The first step is to identify a problem. This can be anything from detecting fraud to forecasting seasonal inventory levels. Once the problem has been defined, the next step is to collect and organize the relevant data. This involves pre-processing the data to remove anomalies, missing data points, and extreme outliers. The final step is to develop a model. There are several methods for this, including regression models and machine learning techniques.
Companies use predictive analytics to improve key performance metrics, reduce risk and optimize operations. It is used in marketing, sales and customer service; credit risk assessment and fraud detection; manufacturing; healthcare; and a variety of other industries. Technology writer Mary Pratt reported that predictive analytics is also helping businesses achieve competitive advantages in sports, such as the NBA’s Orlando Magic, which analyzes data to predict starting lineups and revenue.
Modeling
A predictive analytics model is a statistical model that uses historical data to predict future trends. It can analyze full data sets or use sampling techniques to streamline the process. Depending on the application, a predictive analytics model can be used for customer segmentation, quality assurance or to identify fraud and cyber crime. The model uses regression methods to determine a mathematical relationship between two or more variables.
The results of predictive analytics can be used to make better business decisions and improve operations. This technology can help to target online advertisements, flag suspicious financial transactions and predict equipment failures before they occur. Other business applications include churn analysis, competitive analysis, and fleet and IT hardware maintenance.
Predictive analytics can also save lives by identifying potential allergic reactions before they occur and predicting the severity of the reaction. For example, a sensor called AbbieSense detects early physiological signs of anaphylaxis and can administer epinephrine before the symptoms progress.
Predictive analysis
Predictive analysis transforms diverse, massive data sets into models that can deliver clear, actionable outcomes that support a business goal. It’s a process that can take time, but it’s worth the investment for most companies. It also requires the expertise of data scientists, statisticians and skilled data analysts. These specialists help collect and organize data and prepare it for predictive modeling.
Retailers gather vast amounts of customer information, tracking behavior online and in the real world. They use this information to improve customer services, forecast revenue, and optimize inventory levels. They can even detect customer churn and predict upcoming customer needs based on past behaviors.
Using predictive analytics, the AbbieSense sensor detects early physiological signs of an allergic reaction and automatically injects epinephrine to prevent the most severe form of the allergic reaction. This technology saves lives and could dramatically reduce the cost of healthcare and pharmaceuticals. It’s also being used to detect fraudulent claims, predict maintenance failures, optimize warehouse inventory and improve shipping logistics and on-time deliveries.
Algorithms
A predictive analytics model is an algorithm that analyzes data to predict future outcomes. It enables businesses to identify risks faster and make more informed business decisions. It can also reduce costs by identifying patterns and eliminating waste. Some of these models can even perform calculations in real-time.
The most popular predictive analytics models include decision trees, regression techniques and neural networks. These algorithms can help you understand the relationships between variables and forecast asset values. These tools are invaluable to a variety of industries. From fraud prevention in banks to disaster management for governments and sublime marketing campaigns, predictive analytics is one of the most useful business tools.
A time series model is another popular predictive analytics tool that handles metric value prediction. This type of model identifies trends over specific periods of time, such as when a call center is expected to receive peak customer support calls or how much inventory a shoe store will need for a sales period.