Predictive analytics is the process of using data to answer the question, “What is likely to happen in the future?”
Without many of us realizing it, predictive analytics has become a part of our daily lives – powering our weather forecasts, navigation apps, and streaming video recommendations. But predictive analytics has also transformed how businesses make decisions, plan for the future, and gain insight into customer behavior.
What Predictive Analytics Actually Is: A Complete Definition
Predictive analytics uses historical data to forecast future outcomes. Statistics, data mining, and artificial intelligence can all be used in predictive analytics to identify patterns and trends in a set of data. These patterns and trends are then used to create a statistical model to generate predictions.
Companies use predictive analytics to identify risks and opportunities. It’s a tool that is leveraged across a wide range of industries, from HR to finance, for everything from determining customer churn in businesses to predicting equipment failures in manufacturing.
3 Quick Examples of Predictive Models
To better understand how predictive analytics works, let’s look at a few common types of predictive models used to assess data:
- Classification Models: These models sort data into categories based on certain characteristics. Classification models use past examples or behavior to decide which classification a new piece of data falls into. A simple classification model example is an email spam filter – it looks at past emails marked as spam and learns to identify new emails that belong in the spam category. Common business uses of classification models include predicting if a customer will buy a product or churn, predicting if a loan applicant will repay, and sorting medical scans into "disease" or "no disease” categories.
- Regression Models: These models predict a numerical outcome. Regression models determine the relationship between different variables to predict outcomes using a mathematical equation. For example, a real estate regression model could predict how much a house is likely to sell for by examining past sales data such as the size, location, and condition of homes previously sold. Other common regression model uses include forecasting weather temperatures, predicting future company revenue numbers, and determining future demand for products to optimize inventory levels.
- Time Series Models: These models make predictions based on time-related data patterns. Time series models analyze data collected at regular intervals (daily, monthly, yearly, etc.) to understand trends, seasonal patterns, and cycles. Stock price forecasts are made using time series models that examine past values to predict future prices based on recurring patterns. Other common uses include forecasting sales during holiday periods, predicting seasonal tourism demand, or projecting energy consumption.
What is Predictive AI?
Predictive AI is an advanced type of predictive analytics that uses artificial intelligence. Traditional predictive analytics relies on historical data and statistical methods, while predictive AI incorporates machine learning to identify complex patterns in data.
By enhancing the speed and accuracy of data analyzation, predictive AI makes it possible to quickly assess thousands of factors and decades of data. This makes it a powerful tool for industries where precision is crucial, such as healthcare and finance.
Machine Learning vs. Predictive Analytics: What’s the Relation?
Machine learning and predictive analytics are closely related, often working together while playing distinct roles. Predictive analytics can be thought of as the application, while machine learning is the technology that powers it.
Machine learning is a branch of artificial intelligence in which computers create algorithms to analyze and make decisions based on patterns in data. It has been around since the 1950s, when IBM employee and AI pioneer Arthur Samuel coined the term and defined machine learning as “the field of study that gives computers the ability to learn without explicitly being programmed.” Today machine learning powers chatbots, language translation apps, Netflix recommendations, and social media algorithms.
When used in predictive analytics, machine learning automates the data analyzation process to recognize patterns, refine models, and improve the accuracy of predictions over time.
How are Businesses Utilizing Predictive Analytics?
Predictive analytics is helping businesses make better decisions, optimize operations, and create personalized customer experiences.
It has a wide range of uses across industries such as enabling companies to anticipate customer needs, optimize supply chains, and improve marketing campaigns.
Examples of Predictive Analytics in HR
In human resources, predictive analytics can be used to improve employee performance, happiness and development:
Talent Acquisition: Predictive analytics can help HR teams identify the best candidates for roles. By analyzing applicant data such as education, work experience, and skills it can predict the likelihood of a candidate succeeding in a role. Prioritizing candidates more likely to be a good fit streamlines the hiring process.
Employee Performance and Engagement: Predictive models can analyze performance metrics and engagement data to help managers identify high-potential employees, and support others who may be struggling. This allows companies to better allocate resources and improve employee engagement resources.
Turnover Prediction: By analyzing data on job satisfaction, employee engagement, and performance metrics, an HR department can identify employees at risk of leaving the company. HR can then take proactive steps to improve retention and reduce costs.
Examples of Predictive Analytics in Finance
The financial sector has long been a leader in adopting predictive analytics. Its wide range of applications include:
Credit Scores: Predictive models are widely used in credit scoring to assess the likelihood that a borrower will default on a loan. By analyzing past data, lenders can make more informed decisions, reduce defaults, and extend credit to a wider audience.
Fraud Detection: Banks and financial institutions use predictive analytics to detect unusual patterns in transactions that could signal fraudulent activity – such as an extremely large purchase or a transaction in a foreign country. These models allow banks to send real-time fraud alerts, helping protect consumers and reduce financial losses.
Market Trend Forecasting: By analyzing historical and real-time market data, predictive analytics can help investment teams anticipate future market shifts. This allows finance companies to prepare for market changes, make more informed investment decisions, and reduce losses.
Expect to see predictive analytics become even more integrated into business strategy across industries. With AI and other technological advancements making it easier for companies of all sizes to use predictive analytics, it is poised to become a fundamental part of business operations.
