What Is Prescriptive Analytics? How It Works and Examples

What Is Prescriptive Analytics?

Prescriptive analytics is a type of data analytics that attempts to answer the question "What do we need to do to achieve this?" It involves the use of technology to help businesses make better decisions through the analysis of raw data. Prescriptive analytics specifically factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long-term. It is the opposite of descriptive analytics, which examines decisions and outcomes after the fact.

Key Takeaways

  • Prescriptive analytics is a form of data analytics that tries to answer "What do we need to do to achieve this?"
  • It uses machine learning to help businesses decide a course of action based on a computer program’s predictions.
  • Prescriptive analytics works with predictive analytics, which uses data to determine near-term outcomes.
  • When used effectively, it can help organizations make decisions based on facts and probability-weighted projections instead of conclusions based on instinct.
  • Prescriptive analytics isn't foolproof, as it's only as effective as its inputs.

How Prescriptive Analytics Works

Prescriptive analytics tries to answer the question "How do we get to this point?" It relies on artificial intelligence (AI) techniques, such as machine learning (the ability of a computer program without additional human input), to understand and advance from the data it acquires, adapting all the while.

Machine learning makes it possible to process a tremendous amount of data available today. As new or additional data becomes available, computer programs adjust automatically to make use of it, in a process that is much faster and more comprehensive than human capabilities could manage.

Prescriptive analytics works with another type of data analytics, predictive analytics, which involves the use of statistics and modeling to determine future performance, based on current and historical data. However, it goes further: Using the predictive analytics' estimation of what is likely to happen, it recommends what future course to take.

Numerous types of data-intensive businesses and government agencies can benefit from using prescriptive analytics, including those in the financial services and health care sectors, where the cost of human error is high.

Advantages and Disadvantages of Prescriptive Analytics


Prescriptive analytics can cut through the clutter of immediate uncertainty and changing conditions. It can help prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers. When used effectively, it can help organizations make decisions based on highly analyzed facts rather than jump to under-informed conclusions based on instinct.

Prescriptive analytics can simulate the probability of various outcomes and show the probability of each, helping organizations to better understand the level of risk and uncertainty they face than they could be relying on averages. Organizations that use it can gain a better understanding of the likelihood of worst-case scenarios and plan accordingly.


But prescriptive analytics is not foolproof. It is only effective if organizations know what questions to ask and how to react to the answers. As such, it's only effective if its inputs are valid. If the input assumptions are invalid, the output results will not be accurate.

This form of data analytics is only suitable for short-term solutions. This means businesses shouldn't use prescriptive analytics to make any long-term ones. That's because it becomes more unreliable if more time is needed.

Not all prescriptive analytics providers are made the same. So it's important for businesses to carefully consider the technology and who provides it. Some may provide real, concrete results while others make the promise of big data and fail to deliver

  • Prevents fraud, reduces risk, and increases efficiency among other things

  • Simulates outcomes and shows probably of each

  • Only as effective as the inputs

  • Not suitable for long-term predictions/solutions

  • Some big data providers provide results while others don't

Types of Data Analytics

Data analytics is an automated process that uses algorithms. It analyzes raw data and allows the user to make conclusions about that information. Prescriptive analytics isn't the only type of data analytics. There are several others that we discuss below.

Descriptive Analytics

Descriptive analytics uses historical data and interprets it in a way to better understand any changes that take place in a business. Key data sets that are commonly used in descriptive analytics are changes in price, patterns in sales growth, user data, and subscriber-related revenue.

This form of big data tries to answer the question "What happened?" Having said that. business leaders can use this information to recognize their strengths and weaknesses. This allows them to make better decisions and enhance their business strategies.

Descriptive analytics can be a useful business solution when used in conjunction with other forms, such as prescriptive analytics.

Diagnostic Analytics

This type of data analytics tries to ask the question "Why did this happen?" As such, it requires much more diverse data inputs. But there's a little guesswork involved because businesses use it to find out why certain trends pop up. For instance, it tries to figure out whether there's a relationship between a certain market force and sales or if a certain ad campaign helped or hurt sales of a particular product.

Predictive Analytics

Predictive analytics tries to surmise what could happen in the immediate future by using historical data and making predictions about the future. Businesses can use this form of data analytics to find opportunities for growth and improvement as well as the chance to recognize risks that need to be addressed.

Examples of Prescriptive Analytics

Numerous data-intensive businesses and government agencies can benefit from using prescriptive analytics. This includes companies in the financial services and health care sectors, where the cost of human error is high. For instance, prescriptive analytics could be used to:

  • Evaluate whether a local fire department should require residents to evacuate a particular area when a wildfire is burning nearby
  • Predict whether an article on a particular topic will be popular with readers based on data about searches and social shares for related topics
  • Adjust a worker training program in real-time based on how the worker is responding to each lesson

The following are examples where prescriptive analytics can be used in various settings.

Prescriptive Analytics for Hospitals and Clinics

Prescriptive analytics can be used by hospitals and clinics to improve the outcomes for patients. It puts health care data in context to evaluate the cost-effectiveness of various procedures and treatments and to evaluate official clinical methods.

It can also be used to analyze which hospital patients have the highest risk of re-admission so that health care providers can do more, via patient education and doctor follow-up to stave off constant returns to the hospital or emergency room.

Prescriptive Analytics for Airlines

Suppose you are the chief executive officer (CEO) of an airline and you want to maximize your company’s profits. Prescriptive analytics can help you do this by automatically adjusting ticket prices and availability based on numerous factors, including customer demand, weather, and gasoline prices.

When the algorithm identifies that this year’s pre-Christmas ticket sales from Los Angeles to New York are lagging last year’s, for example, it can automatically lower prices, while making sure not to drop them too low in light of this year’s higher oil prices.

At the same time, when the algorithm evaluates the higher-than-usual demand for tickets from St. Louis to Chicago because of icy road conditions, it can raise ticket prices automatically. The CEO doesn’t have to stare at a computer all day looking at what’s happening with ticket sales and market conditions and then instruct workers to log into the system and change the prices manually. Instead, a computer program can do all of this and more—and at a faster pace, too.

Prescriptive Analytics in Banking

Banking is one of the industries that can benefit from prescriptive analytics the most. That's because companies in this sector are always trying to find ways to better serve their customers while ensuring they remain profitable. Applying prescriptive analytical tools can help the banking sector to:

  • Create models for customer relationship management
  • Improve ways to cross-sell and upsell products and services
  • Recognize weaknesses that may result in losses, such as anti-money laundering (AML)
  • Develop key security and regulatory initiatives like compliance reporting

Prescriptive Analytics in Marketing

Just like banking, data analytics is very critical in the marketing sector. Marketers can use prescriptive analytics to stay ahead of consumer trends. Using past trends and past performance can give internal and external marketing departments a competitive edge.

By employing prescriptive analytics, marketers can come up with effective campaigns that target specific customers at specific times like, say, advertising for a certain demographic during the Superbowl. Corporations can also identify how to engage different customers and how to effectively price and discount their products and services.

What Does Prescriptive Analytics Mean?

Prescriptive analytics is a form of data analytics that helps businesses make better and more informed decisions. Its goal is to help answer questions about what should be done to make something happen in the future. It analyzes raw data about past trends and performance through machine learning (so very little human input, if any at all) to determine possible courses of action or new strategies generally for the near term.

Why Is Prescriptive Analytics So Important for Businesses?

Prescriptive analytics is very important for businesses because it allows them to look at their past performance and ask themselves "What do we need to do to get to this point?" It is critical for businesses that are in need of a turnaround, especially those that are struggling with low performance metrics. Using this type of data analytics allows them to come up with strategies and a suitable course of action and, perhaps, how long it may take for them to achieve these goals.

What Are the Other Forms of Data Analytics?

The other forms of data analytics are descriptive analytics, diagnostic analytics, and predictive analytics. Each tries to ask a different question and may be used by businesses together or separately to make better, more informed decisions.

The Bottom Line

There are many things businesses can do to ensure their success and make better decisions. Data analytics is one tool that they have at their disposal to reach these goals. Prescriptive analytics is a form of data analytics that uses past performance and trends to determine what needs to be done to achieve future goals. Even with the obvious benefits, business leaders should understand that prescriptive analytics has its own drawbacks. Knowing where to start and choosing the right company or software to help you reach your goals can certainly help you in the long run.