What Is Predictive Analytics?
The term predictive analytics refers to the use of statistics and modeling techniques to make predictions about future outcomes and performance. Predictive analytics looks at current and historical data patterns to determine if those patterns are likely to emerge again. This allows businesses and investors to adjust where they use their resources to take advantage of possible future events. Predictive analysis can also be used to improve operational efficiencies and reduce risk.
- Predictive analytics uses statistics and modeling techniques to determine future performance.
- Industries and disciplines, such as insurance and marketing, use predictive techniques to make important decisions.
- Predictive models help make weather forecasts, develop video games, translate voice-to-text messages, customer service decisions, and develop investment portfolios.
- People often confuse predictive analytics with machine learning even though the two are different disciplines.
- Types of predictive models include decision trees, regression, and neural networks.
Understanding Predictive Analytics
Predictive analytics is a form of technology that makes predictions about certain unknowns in the future. 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, except for large blocks of text.
Predictive models are used for all kinds of applications, including:
- Weather forecasts
- Creating video games
- Translating voice to text for mobile phone messaging
- Customer service
- Investment portfolio development
All of these applications use descriptive statistical models of existing data to make predictions about future data.
They're also useful for businesses to help them manage inventory, develop marketing strategies, and forecast sales. It also helps businesses survive, especially those in highly competitive industries, such as health care and retail. Investors and financial professionals can draw on this technology to help craft investment portfolios and reduce the potential for risk.
These models determine relationships, patterns, and structures in data that can be used to draw conclusions about 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 a variety of industries.
Forecasting is essential in manufacturing because it ensures the optimal utilization 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 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 perform on any credit extended.
Data and predictive analytics play an important role in underwriting. Insurance companies examine policy applicants to determine the likelihood of having to pay out for a future claim 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.
Individuals who work in this field look at how consumers have reacted to the overall economy when planning on a new campaign. They can use these shifts in demographics to determine if the current mix of products will entice consumers to make a purchase.
Active traders, meanwhile, look at a variety of metrics based on past events when deciding whether to buy or sell a security. Moving averages, bands, and breakpoints are based on historical data and are used to forecast future price movements.
Predictive Analytics vs. Machine Learning
A common misconception is that predictive analytics and machine learning are the same things. 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.
Machine learning, on the other hand, is a subfield of computer science that, as per the 1959 definition by Arthur Samuel (an American pioneer in the field of computer gaming and artificial intelligence) 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."
The most common predictive models include decision trees, regressions (linear and logistic), and neural networks, which is the emerging field of deep learning methods and technologies.
Types of Predictive Analytical Models
There are three common techniques used in predictive analytics: Decision trees, neural networks, and regression. Read more about each of these below.
If you want to understand what leads to someone's decisions, then you may find decision trees useful. 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 the simplest models because they're easy to understand and dissect. They're also very useful when you need to make a decision in a short period of time.
This is the model that is used the most in statistical analysis. Use it when you want to determine 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 security.
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 it if you have several hurdles that you need to overcome like when you have too much data on hand, when you don't have the formula you need to help you find a relationship between the inputs and outputs in your dataset, or when you need to make predictions rather than come up with explanations.
If you've already used decision trees and regression as models, you can confirm your findings with neural networks.
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 their 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 could defect and go 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
There are numerous benefits to using predictive analysis. As mentioned above, using this type of analysis can help entities when you need to make predictions about outcomes when there are no other (and obvious) answers available.
Investors, financial professionals, and business leaders are able to use models to help reduce risk. For instance, an investor and their advisor can use certain models to help craft an investment portfolio with minimal risk to the investor by taking certain factors into consideration, such as age, capital, and goals.
There is a significant impact to cost reduction when models are used. Businesses can determine the likelihood of success or failure of a product before it launches. Or they can set aside capital for production improvements by using predictive techniques 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 whether the predictions drawn from the use of such analytics are accurate, their use is generally frowned upon, and data that explicitly include information such as a person's race are now often excluded from predictive analytics.
Predictive Analytics FAQs
How Does Netflix Use Predictive Analytics?
Data collection is very important to a company like Netflix. It collects data from its customers based on their behavior and past viewing patterns. It uses information and makes predictions based to make recommendations based on their preferences. This is the basis behind the "Because you watched..." lists you'll find on your subscription.
What Are the Three Pillars of Data Analytics?
There are three pillars to data analytics. They are the needs of the entity that is using the models, the data and the technology used to study it, and the actions and insights that come as a result of the use of this kind of analysis.