Predictive Analytics: Definition, Model Types, and Uses

What Is Predictive Analytics?

Predictive analytics is the use of statistics and modeling techniques to forecast future outcomes. Current and historical data patterns are examined and plotted to determine the likelihood that those patterns will repeat.

Businesses use predictive analytics to fine-tune their operations and decide whether new products are worth the investment. Investors use predictive analytics to decide where to put their money. Internet retailers use predictive analytics to fine-tune purchase recommendations to their users and increase sales.

Understanding Predictive Analytics

Predictive analytics looks for past patterns to measure the likelihood that those patterns will reoccur. It draws on a series of techniques to make these determinations, including artificial intelligence (AI), data mining, machine learning, modeling, and statistics. For instance, data mining involves the analysis of large sets of data to detect patterns from it. Text analysis does the same using large blocks of text.

Predictive models are used for many applications, including weather forecasts, creating video games, translating voice to text, customer service, and investment portfolio strategies. All of these applications use descriptive statistical models of existing data to make predictions about future data.

Predictive analytics helps businesses manage inventory, develop marketing strategies, and forecast sales. It also helps businesses survive, especially in highly competitive industries such as health care and retail. Investors and financial professionals draw on this technology to help craft investment portfolios and reduce their overall risk potential.

These models determine relationships, patterns, and structures in data that are used to draw conclusions as to how changes in the underlying processes that generate the data will change the results. Predictive models build on these descriptive models and look at past data to determine the likelihood of certain future outcomes, given current conditions or a set of expected future conditions.

Uses of Predictive Analytics

Predictive analytics is a decision-making tool in many industries. Following are some examples.

Manufacturing

Forecasting is essential in manufacturing to optimize the use of resources in a supply chain. Critical spokes of the supply chain wheel, whether it is inventory management or the shop floor, require accurate forecasts for functioning.

Predictive modeling is often used to clean and optimize the quality of data used for such forecasts. Modeling ensures that more data can be ingested by the system, including from customer-facing operations, to ensure a more accurate forecast.

Credit

Credit scoring makes extensive use of predictive analytics. When a consumer or business applies for credit, data on the applicant's credit history and the credit record of borrowers with similar characteristics are used to predict the risk that the applicant might fail to repay any new credit that is approved.

Underwriting

Data and predictive analytics play an important role in underwriting. Insurance companies examine applications for new policies to determine the likelihood of having to pay out for a future claim. The analysis is based on the current risk pool of similar policyholders as well as past events that have resulted in payouts.

Predictive models that consider characteristics in comparison to data about past policyholders and claims are routinely used by actuaries.

Marketing

Marketing professionals planning a new campagn look at how consumers have reacted to the overall economy. They can use these shifts in demographics to determine if the current mix of products will entice consumers to make a purchase.

Stock Traders

Active traders look at a variety of historical metrics when deciding whether to buy a particular stock or other asset.

Moving averages, bands, and breakpoints all are based on historical data and are used to forecast future price movements.

Fraud Detection

Financial services use predictive analytics to examine transactions for irregular trends and patterns. The irregularities pinpointed can then be examined as potential signs of fraudulent activity.

This may be done by analyzing activity between bank accounts or analyzing when certain transactions occur.

Supply Chain

Supply chain analytics is used to manage inventory levels and set pricing strategies. Supply chain predictive analytics use historical data and statistical models to forecast future supply chain performance, demand, and potential disruptions.

This helps businesses proactively identify and address risks, optimize resources and processes, and improve decision-making. Companies can forecast what materials should be on hand at any given moment and whether there will be any shortages.

Human Resources

Human resources uses predictive analytics to improve various processes such as identifying future workforce skill requirements or identifying factors that contribute to high staff turnover.

Predictive analytics can also analyze an employee's performance, skills, and preferences to predict their career progression and help with career development.

Predictive Analytics vs. Machine Learning

A common misconception is that predictive analytics and machine learning are the same. Predictive analytics help us understand possible future occurrences by analyzing the past. At its core, predictive analytics includes a series of statistical techniques (including machine learning, predictive modeling, and data mining) and uses statistics (both historical and current) to estimate, or predict, future outcomes.

Thus, machine learning is a tool used in predictive analysis.

Machine learning is a subfield of computer science that means "the programming of a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning." That's a 1959 definition by Arthur Samuel, a pioneer in computer gaming and artificial intelligence.

Types of Predictive Analytical Models

There are three common techniques used in predictive analytics: Decision trees, neural networks, and regression.

Decision Trees

If you want to understand what leads to someone's decisions, you may find it useful to build a decision tree.

This type of model places data into different sections based on certain variables, such as price or market capitalization. Just as the name implies, it looks like a tree with individual branches and leaves. Branches indicate the choices available while individual leaves represent a particular decision.

Decision trees are easy to understand and dissect. They're useful when you need to make a decision quickly.

Regression

This is the model that is used the most in statistical analysis. Use it when you want to decipher patterns in large sets of data and when there's a linear relationship between the inputs.

This method works by figuring out a formula, which represents the relationship between all the inputs found in the dataset.

For example, you can use regression to figure out how price and other key factors can shape the performance of a stock.

Neural Networks

Neural networks were developed as a form of predictive analytics by imitating the way the human brain works. This model can deal with complex data relationships using artificial intelligence and pattern recognition.

Use this method if you have any of several hurdles that you need to overcome. For example, you may have too much data on hand, or don't have the formula you need to find a relationship between the inputs and outputs in your dataset, or need to make predictions rather than come up with explanations.

Cluster Models

Clustering is a method of aggregating data that share similar attributes. For example, Amazon.com can cluster sales based on the quantity purchased, or on the average account age of its consumers.

separating data into similar groups based on shared features, analysts may be able to identify other characteristics that define future activity.

Time Series Modeling

In some cases, data relates to time, and specific predictive analytics rely on the relationship between what happens when. These types of models assess inputs at specific frequencies such as daily, weekly, or monthly iterations.

Then, analytical models can seek seasonality, trends, or behavioral patterns based on timing.

This type of predictive model is useful to predict when peak customer service periods are needed or when specific sales can be expected to jump.

How Businesses Can Use Predictive Analytics

As noted above, predictive analysis can be used in a number of different applications. Businesses can capitalize on models to help advance their interests and improve their operations. Predictive models are frequently used by businesses to help improve customer service and outreach.

Executives and business owners can take advantage of this kind of statistical analysis to determine customer behavior. For instance, the owner of a business can use predictive techniques to identify and target regular customers who might otherwise defect to a competitor.

Predictive analytics plays a key role in advertising and marketing. Companies can use models to determine which customers are likely to respond positively to marketing and sales campaigns. Business owners can save money by targeting customers who will respond positively rather than doing blanket campaigns.

Benefits of Predictive Analytics

As mentioned above, predictive analytics can help anticipate outcomes when there are no obvious answers available.

Investors, financial professionals, and business leaders use models to help reduce risk. For instance, an investor or an advisor can use models to help craft an investment portfolio with an appropriate level of risk, considering factors such as age, family responsibilities, and goals.

Businesses use them to keep their costs down. They can determine the likelihood of success or failure of a product before it is developed. Or they can set aside capital for production improvements before the manufacturing process begins.

Criticism of Predictive Analytics

The use of predictive analytics has been criticized and, in some cases, legally restricted due to perceived inequities in its outcomes. Most commonly, this involves predictive models that result in statistical discrimination against racial or ethnic groups in areas such as credit scoring, home lending, employment, or risk of criminal behavior.

A famous example of this is the now illegal practice of redlining in home lending by banks. Regardless of the accuracy of the predictions, their use is discouraged as they perpetuate discriminatory lending practices and contribute to the decline of redlined neighborhoods.

The Bottom Line

The goal of predictive analytics is to make predictions about future events, then use those predictions to improve decision-making. Predictive analytics is used in a variety of industries including finance, healthcare, marketing, and retail. Different methods are used in predictive analytics such as regression analysis, decision trees, or neural networks.