Examples of Prescriptive Analytics

Kelli Folse

Examples of Prescriptive Analytics

In today’s business world, we have access to more data and analytics than at any other time in human history.

We can see and dissect information in real-time. We can view it from a macro or micro level. We can customize it, analyze it, and all too often…get paralyzed by it. This is why more and more companies spend money on data scientists. Because with all this information at our fingertips, it’s never been easier to fall prey to analysis paralysis.

The good news is, you don’t need an entire team of data analysts or a crystal ball to take all this newfound analytics data and use it to make good decisions. Instead, you can simply rely on prescriptive analytics.

What Are Prescriptive Analytics?

You might find yourself thinking “what on Earth are prescriptive analytics?” Especially if you don’t spend your days buried in Google Analytics and other types of data analysis software.

The answer is surprisingly simple. Prescriptive analytics is one of the key branches of data analytics (more on the others in a bit…). It takes large amounts of data and hypothetical actions/situations and presents a series of possible outcomes. It then shows you what paths that could lead to these outcomes. Including the “best” possible path to a desired destination.

It’s not fortune telling, nor is it an exact science, but using artificial intelligence, algorithms, machine learning, pattern recognition, and a lot of other technical tools, prescriptive analytics can help you chart a course for moving forward. Either in the immediate future or for months and years down the road.

What are the Other Types of Data Analytics?

As mentioned above, prescriptive analytics is just one branch of the analytics tree. It’s joined by descriptive analytics, diagnostic analytics, and predictive analytics.

What is Descriptive Analytics?

In the simplest terms, descriptive analytics is the big picture data. This is the data that tells us what has already happened.

In the hierarchy of data processing, this is often regarded as the preliminary stage of the process. Here, you’re looking at historical data to figure out what has already happened in your business. It’s sort of like a fossil or evolutionary record in that it tends to look back from the present and provide clues as to how you’ve arrived at where you are currently.

This data can be invaluable for tracking trends, figuring out what works and what doesn’t, and for providing a general overview of your growth.

What is Diagnostic Analytics?

Diagnostic analytics builds on the foundation of descriptive analytics by examining why things happened. With the descriptive data gathered, parsed, and categorized, we can start to look at it and draw correlations between cause and effect.

Descriptive and diagnostic analytics are both valuable tools in your data analysis strategy, but both are categorized as reactive analytics because your business is reacting to data that already exists.

But what about proactive analytics?

What is Predictive Analytics?

As the name indicates, predictive analytics are basically responsible for predicting potential outcomes based on data.

Predictive analytics takes the information you gathered from your descriptive analytics and predicts results based on that information.

Prescriptive and predictive analytics are commonly referred to as proactive analytics – meaning that the information they provide can be used to move forward, finding opportunities and averting potential problems before it’s too late to do anything about them.

What’s the Difference Between Prescriptive and Predictive Analytics?

So, after reading that, you might be wonder “what’s the difference between predictive and prescriptive analytics?”

Prescriptive basically takes predictive to the next level. Rather than just give you an idea of where things are heading based on various sets of data, prescriptive analytics will show you different routes to the outcomes you desire.

You’ll still have to make decisions and implement things on the human level. But good prescriptive analytics can not only prevent you from being overwhelmed by options, it can show multiple paths to your destination and help remove some of the guesswork and “gut feeling” that factors into many decisions.

Real Word Examples of Prescriptive Analytics in Action

Now that we know what all these different kinds of analytics are, let’s look at how prescriptive analytics work in a real-world business environment.

Prescriptive Analytics in Healthcare

When you think of analyzing huge chunks of data, you’re likely to imagine giant corporations and a wide variety of companies in the retail and financial sectors.

However, prescriptive analytics can be hugely beneficial to companies in any field – including healthcare. Take, for instance, health insurance companies.

In this example from Sajan Kuttappa, a product marketing manager at IBM, a health insurance company analyzes its data and determines that many of its diabetic patients also suffer from retinopathy.

With this information, the provider can now use predictive analytics to get an idea of how many more ophthalmology claims it might receive during the next year.

Then, using prescriptive analytics, the company can look at scenarios where the reimbursement costs for ophthalmology increases, decreases, or holds steady. These scenarios then allow them to make an informed decision about how to proceed in a way that’s both cost-effective and beneficial to their customers.

In the actual hospital, prescriptive analytics can play a vital role as well.

Analyzing data on patients, treatments, appointments, surgeries, and even radiologic techniques can ensure hospitals are properly staffed, the doctors are devising tests and treatments based on probability rather than gut instinct, and the facility can save costs on everything from medical supplies to transport fees to food budgets.

Prescriptive Analytics in Sales

It should come as no surprise that one area where prescriptive analytics can really have an impact is sales.

Taking all of your descriptive, diagnostic, and predictive data and then analyzing it with a prescriptive methodology can impact every step of the sales process. From maximizing first-contact success rates to figuring out how to get customers at the bottom of the sales funnel to complete their transactions.

It can even help your sales team become more effective at their job.

Prescriptive analytics can show a sales team member where all of their customers are at in the purchasing process. By analyzing a wide range of factors, it can then help them prioritize their focus on who’s most likely to actually complete their purchase, who is more on the fence (with strategies to get them back on the path to the sale), and so on. It can even offer up suggestions for how to keep specific customers moving through the funnel.

Beyond that, it’s possible for a sales manager to examine prescriptive data on each individual sales team member to see where they tend to lose a customer in the buyer’s journey.

If a rep is losing leads early or in the demo phase, perhaps there’s an issue with how they’re opening with clients or showcasing the product. If they’re losing sales in the bottom of the funnel, prescriptive analytics can offer a different approach to get the employee back on track.

Armed with this information, the manager can work with the sales rep on their specific issues to help them better reach quotas and goals.

Prescriptive analysis should be a goal of every major sales department going forward. The level of insights that can be gained into customer and sales rep behavior can literally be a game changer.

Prescriptive Analytics in Retail

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

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, 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 deductive, diagnostic, and predictive data and then running it through a prescriptive analytics system 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 video site YouTube.

YouTube’s algorithm factors in billions of data points in order to create a customized viewing experience unique to you every time you visit the site’s home page. The more data you give the algorithm (by selecting videos, liking and disliking, subscribing, leaving comments, and watch time), the better it gets at surfacing videos that are likely to be of interest to you.

Back over in retail, prescriptive analytics can also help with scheduling, shipping logistics, inventory control, and countless other ways. There really aren’t many things it can’t provide insights for.

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.

However, with prescriptive analytics, it’s entirely possible to look at the list of potential students who have expressed interest in enrolling and determine what approaches might get them to fully commit.

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

While predictive analytics would give you a good idea as to which of the pool of 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 help with scheduling. For example, making sure there are enough class types for students, that teachers are available to cover them, and that you’re not wasting time offering programs that 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.

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 bank fraud departments are made up of flesh and blood human beings, machines are the ones watching yours (and billions of other) transactions made every day.

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 are also able to analyze a wide variety of factors to predict when you might be thinking about switching to a different financial institution. When predictive analytics make this observation, prescriptive analytics can kick in with a wide range of potential offers and solutions to keep you right where you are.

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

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Prescriptive Analytics in Sports

If you’ve seen the 2011 Brad Pitt film Moneyball, then you’re already aware that big data has become a major component of professional sports.

These days, everyone from the NFL to the National Hockey League has a team of data scientists on staff crunching numbers to determine everything from which free agents offer the most return on investment, to which up and coming players could be the next superstar.

It doesn’t stop there, though – teams are using prescriptive analytics to figure out the chances of success and failure running certain plays in certain situations. There’s now an entire culture of data analysts who’ve taken the term “stat geek” in sports lingo to a whole new level.

And it makes sense. With multimillion-dollar contracts and hundreds of millions of dollars in revenue at stake, trying to get a competitive edge can be the difference between winning a championship and missing the playoffs entirely.

Data analytics has changed the landscape of the front office in pro sports on a seismic level – and it’s a given that the trend will continue for the foreseeable future.

Final Thoughts

Hopefully by this point you’re seeing just how important data science in general — and prescriptive analytics in particular – can be to business.

We’re still in the relatively early stages of prescriptive analytic adoption in the business world (most experts think it will be another few years before full integration occurs), which means this is the perfect time to get a leg up on your competition.

By implementing a full suite of data analytics tools you’ll be able to not only see how your business has gotten to where it is currently, but figure out new paths for going forward that eliminate a lot of the guesswork and trial and error.

Prescriptive analytics isn’t a Magic 8-Ball. But it can give you a lot of different options for how to grow your business and solve your problems. And the best part is that it has something to offer for every kind of business out there. From mega corporations to small non-profits and everything in between.

If you’re not taking advantage of these different types of data analysis, you’re not making the best, most informed decisions possible.

Data doesn’t have to be intimidating and there’s no need for analysis paralysis.

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