What is Predictive Analytics?

Analysts use four types of data analytics: descriptive, diagnostic, predictive, and prescriptive analytics.

• Descriptive analytics identifies what has happened in the past through historical data analysis.
• Diagnostic analytics uses historical data to explain why something happened in the past.
• Predictive analytics predicts future trends based on historical and current data patterns.
• Prescriptive analytics prescribes future actions and decisions, allowing businesses to optimize decision-making.

Predictive analytics vs. descriptive analytics

Descriptive analytics is the data science that allows data engineers to learn what happened in past events. It explores past data and presents it in easily understandable statistical models, such as tables and charts. For example, companies have used descriptive analytics to study seasonal sales trends for several years. 

Descriptive analytics is based on factual events and patterns uncovered through data mining techniques. However, it does not predict future events, as predictive analytics does.

Predictive analytics vs. prescriptive analytics

Predictive analytics tells you what might happen based on past events, whereas prescriptive analytics further recommends decisions that influence the outcome. For example, the predictive model suggests that the delivery team cannot cope with the coming festive seasons. Factory managers then use prescriptive analytics to find the best delivery schedules, courier services, and personnel shift arrangements. 

Many organizations actively use predictive analytics to guide real-time and future outcomes. Here are some predictive analytics examples.

Finance

Banking and fund managers make high-stakes decisions that might affect the financial institution's profitability. Predictive analytics allows them to make determinations confidently by providing business intelligence based on past transactional data. For example,

• Loan managers use advanced analytics software to predict credit risk before approving loans to applicants. 
• Banking security teams use predictive analytics software to identify abnormal transactional data suggesting fraudulent activities.
• Insurance companies can use predictive modeling to identify false insurance claims. 

Retail

Retail companies use predictive analytics to forecast regional and local customer demand and pre-deliver stock to regional and local distribution stations to reduce delivery times. Other companies use lead scoring models to improve lead conversion rates and predictive recommendations to increase up-and-cross-selling opportunities based on customer profiles. Here, predictive analytics determines more effective marketing strategies. Companies also use predictive analytics to forecast future demand and sales.

Manufacturing

Manufacturers use predictive analytics to improve productivity, cost efficiency, and quality across the supply chain. For example, procurement managers use predictive analytics to forecast material prices and secure them at the lowest possible rate. Meanwhile, the logistics department runs predictive analyses to chart optimum delivery routes and reduce shipping expenses.

Manufacturing also uses predictive machine learning to identify potential equipment failure. Technicians can carry out scheduled repairs with minimum impact on the production schedule. Manufacturers use predictive data analytics to monitor production line equipment to optimize throughput, detect irregularities, and highlight equipment defects. Manufacturing companies use predictive analytics to monitor machinery, identify conditions, and predict maintenance requirements.

Healthcare

The healthcare industry benefits from predictive analytics on both macro and micro levels. For example, medical experts use predictive modeling to chart the path of global diseases based on changing variables such as vaccine development and availability. Doctors also use healthcare predictive analytics to monitor patients' symptoms and anticipate complications that might arise in the future. Healthcare companies use predictive analytics on patient monitoring equipment to detect real-time changes in patients' conditions while eliminating spurious alarms that render patient monitoring equipment ineffective.

As with many machine learning applications, predictive analytics is a dynamic activity that constantly uses new data to update predictions. This means the technique uses the pipeline of data cleansing, model training, deployment, feedback, retraining, redeployment, and the ability to ingest data in close to real-time. Data scientists use the following predictive analytics techniques.

Decision trees

A decision tree is a machine-learning model that allows the software to make predictions by answering a series of yes-or-no questions. Like its name, this technique emulates a tree shape with nodes and branches. Each node contains a feature specific to the problem that must be answered before proceeding to the next node. Each node branches out to two leaves, which lead to the subsequent nodes.

A decision tree can predict both qualitative and quantitative data. For example, you can use a decision-tree predictive model to predict property prices or a patient's health conditions based on noticeable symptoms. Decision trees are easy to understand but less flexible when analyzing diverse new data.  

Regression analysis

Regression is a statistical approach data scientists use to make predictions by classifying or correlating new data with known data sets. Linear regression models the relationship between an independent variable and a dependent value on a two-dimensional chart. For example, HR managers use linear regression to predict a candidate's salary based on years of experience. 

Meanwhile, logistic regression classifies the variables into two or more categories based on probabilities. For example, IT teams use logistic regression to detect and predict whether an email is spam. The model classifies the email as suspicious if it finds too many undesired characteristics beyond a set threshold. 

Time series analysis

Time series analysis is a predictive analytics technique used to analyze data points collected or recorded over time, recognizing the importance of temporal order. This makes it particularly useful in forecasting applications such as stock price movements, energy consumption, or demand planning in supply chain management.

A key method within time series analysis is the autoregressive integrated moving average (ARIMA), which models time-dependent relationships by factoring in past values and errors to predict future trends. More advanced approaches, such as Long Short-Term Memory (LSTM), maintain memory over extended timeframes, leveraging deep learning to capture long-term dependencies in time series data.

Deep learning neural networks

Deep learning has revolutionized predictive analytics by allowing models to process complex, high-dimensional data and uncover intricate relationships that traditional techniques might miss. Neural networks are particularly effective when making predictions with complex data such as images, videos, and voice recordings. Deep learning models enhance predictive capabilities across complex industries such as healthcare and cybersecurity.