What is prescriptive analytics with example

We’re willing to bet you’ve already had firsthand experience with prescriptive analytics and you didn’t even realize it.

Table of Contents Show

What Is Predictive Analytics?
Predictive Analytics Examples
What Is Prescriptive Analytics?
Prescriptive Analytics Examples
Build Your Future in Business Analytics
Recommended Readings

Have you ever shopped online? Visited Amazon? If the answer is yes, then you’ve already seen the power of prescriptive analytics in action.

Whenever you go to Amazon, the site recommends dozens and dozens of products to you.

These are based not only on your previous shopping history (reactive) but also based on what you’ve searched for online.

They analyze what other people who’ve shopped for the same things have purchased, and about a million other factors (proactive).

Amazon and other large retailers are taking oceans of data and running it through a prescriptive analytics system. The end goal is to find products that you have a higher chance of buying.

Every bit of data is broken down and examined with the end goal of helping the company suggest products you may not have even known you wanted.

We see a similar use of this technology on the video site YouTube.

YouTube’s algorithm factors in billions of data points to create a customized viewing experience. That’s why the YouTube homepage looks different every time you visit.

The more you engage with videos, the more data you give the algorithm.

How you engage with them matters, too. If you dislike a certain kind of video, YouTube will notice patterns and stop recommending those to you. When you subscribe to certain channels, the site will recommend similar ones.

Back over in retail, prescriptive analytics can also help with:

Scheduling
Shipping logistics
Inventory control

And countless other ways. There aren’t many things it can’t provide insights for.

Examples of Prescriptive Analytics in Higher Education

When you think of places using and analyzing big sets of data, you may not immediately think of colleges and university admission offices. But it turns out prescriptive analytics can benefit them just as much as a retail chain.

An oft-cited example has a college admissions department receiving a report in July that fall enrollment rates are down. Without prescriptive analytics, this could cause panic and the implementation of a plan that may or may not work.

With prescriptive analytics, colleges can discern the best ways to enroll potential students.

For example, some students could be swayed by a campus visit. Others could be won with financial aid assistance, scholarships, and so on.

Predictive analytics would only give you a good idea on which students were most likely to enroll.

Prescriptive analytics would tell you who’s likely to enroll and what approach is most likely to convince them your school is the perfect fit.

As with all the other examples, it goes beyond just that. Prescriptive analytics can impact a wide range of other areas on campus as well.

With enough data, a prescriptive analytics program can also help with scheduling.

For example:

Making sure there are enough class types for students
Staffing the teachers are to cover them
Dropping curriculum no one is interested in

It can help predict student housing needs like when to expand with more buildings and classrooms, and myriad other issues.

In the world of education, prescriptive analytics is like a dean, guidance counselor, faculty member, and alumnus. All rolled into one.

Examples of Prescriptive Analytics in Banking

Have you ever had the misfortune of having your bank contact you to let you know there have been suspicious charges on your account? Then you’ve just experienced prescriptive analytics.

While humans staff these fraud departments, machines are the ones watching your transactions.

Machines learn your spending habits, your general location, and tons of other data. They then verify each expenditure against that knowledge. If something doesn’t line up, you’re notified immediately and can act.

Beyond that, banks can analyze several factors to predict when you might switch to a different financial institution.

While predictive analytics makes the observation, prescriptive analytics can offer solutions to keep your business.

On top of that, they can help banks decide which services and products to offer as well.

The purpose of any analytics degree program in business is to prepare students for an ever-changing, complex, global business world. The curriculum nurtures students’ ability to combine the troves of internally sourced, public, and other third-party sourced data into actionable insight to improve business operations. Of the four analytics disciplines in the analytics portfolio, two — descriptive and diagnostic — provide businesses with insight into events that happened and why they occurred.

The other two analytics disciplines, predictive and prescriptive, are a step up the analytics ladder. Both give insight, and even foresight, to support business decision-making. Both predictive and prescriptive analytics incorporate statistical modeling, machine learning, and data mining to give MBA executives and MBA graduate students strategic tools and deep insight into customers and overall operations.

In addition to their similarities, it is important for analytics professionals to know the differences between predictive vs. prescriptive analytics to use both effectively and efficiently.

What Is Predictive Analytics?

It may be tempting to think of predictive analytics as a fortune-telling strategy that tells people what the future holds. It does not have that capacity, of course — no analytics method does. What it does offer is a means to use statistics and modeling techniques to make intelligently calculated predictions about future business outcomes.

The three keystones of predictive analytics are decision analysis and optimization, transactional profiling, and predictive modeling. Predictive analytics exploits patterns in transactional and historical data to identify risks and opportunities. It doesn’t guarantee positive results, but it may help make positive results more likely.

Predictive Analytics Examples

Predictive analytics’ use of decision analytics and optimization, transactional processing, and predictive modeling can provide more in-depth information about customer behavior and other similar metrics that BI cannot. An example of this can be found in the retail sector, specifically when it comes to customer behavior. While BI can inform what ZIP code a company’s most valuable customers come from, predictive analytics and its keystones can provide data that informs about how much revenue those customers can generate.

Predictive analytics can be applied in a wealth of non-retail scenarios as well. Netflix, for instance, uses predictive analytics models to curate user experiences and even develop new show concepts. In the health care sector, predictive analytics can be used to build proactive health and wellness strategies that can reduce ER visits and lower costs.

What Is Prescriptive Analytics?

Prescriptive analytics is an emerging discipline that represents a more advanced use of predictive analytics. Prescriptive analytics goes beyond simply predicting options in the predictive model. It actually suggests a range of prescribed actions and the potential outcomes of each action.

A prescriptive model can ultimately help a business create a more cohesive business strategy. It builds upon the findings gathered from a predictive analytical model by proposing strategic applications based on predicted behaviors.

Prescriptive Analytics Examples

Waymo, the autonomous car that started off as Google’s self-driving car project in 2009, is a prime example of prescriptive analytics in action. The vehicle makes millions of calculations on every trip that helps the car decide when and where to turn, whether to slow down or speed up, and when to change lanes — the same decisions a human driver makes behind the wheel.

The energy sector also provides an excellent example of the power of prescriptive analytics. Utility companies, gas producers, and pipeline companies use prescriptive analytics to identify factors affecting the price of oil and gas to secure the best terms and hedge risks. These companies are also using prescriptive analytics to improve operational safety and minimize the threat of potential environmental disasters from occurring.

Build Your Future in Business Analytics

Predictive and prescriptive analytics are co-dependent disciplines that take business intelligence to unprecedented levels. With both forms of analysis, business executives and leaders gain both insight and foresight.

Ohio University’s Online Master of Business Administration program and its business analytics concentration can help you apply these forms of analytics in ways that can make a profound impact in business. The curriculum is designed to equip you with the advanced knowledge and skills to glean actionable insight from data so effective business strategies can be created, and better business decisions can be made.

Learn how we can help prepare you to embark on a successful career.