Examples of Prescriptive Analytics

Real-world examples of prescriptive analytics from seven different industries

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.

It’s not fortune-telling, nor is it an exact science. But through the use of:

• 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.

SEE ALSO: What is Prescriptive Analytics?

What are the Other Types of Data Analytics?

As mentioned above, prescriptive analytics is 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 like a fossil that looks back from the present and provides clues to the past.

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

What is Diagnostic Analytics?

Diagnostic analytics builds on the foundation of descriptive analytics by examining root causes. 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. However, both are what’s called “reactive analytics.” In other words, you’re reacting to data that already exists.

That begs the question: what about proactive analytics?

What is Predictive Analytics?

As the name indicates, predictive analytics predicts potential outcomes based on data.

These predictions come from the information gathered from your descriptive analytics.

Prescriptive and predictive analytics are “proactive analytics.” Meaning, the information they provide can be help businesses:

• Determine the best course of action in a given scenario
• Find opportunities faster than their competitors
• Avert potential problems before it’s too late to do anything about them

What’s the Difference Between Prescriptive and Predictive Analytics?

You may be wondering: “what’s the difference between predictive and prescriptive analytics?”

In short, prescriptive assigns specific action steps to predictive insights.

Remember, predictive analytics inform you of where things are heading based on 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:

• Prevent option-overwhelm
• Reveal many paths to your destination
• And help remove some of the guesswork and “gut feelings” that factors into many decisions

SEE ALSO: What is Business Analytics?

Examples of Prescriptive Analytics in Seven Different Industries

You should now have a robust understanding of the three main “species” of analytics.

When you think of analyzing huge chunks of data, you may imagine giant corporations in the retail and financial sectors.

But prescriptive analytics can be hugely beneficial to companies in any field. Let’s dive into specific examples of prescriptive analytics across a bevy of verticals.

Examples of Prescriptive Analytics in Healthcare

In 2018, the healthcare industry was worth $8.45 trillion. It’s no doubt grown since then and will keep growing still

Healthcare is one of the markets most ripe for an analytics revolution. But like any multi-trillion dollar industry, healthcare can be a bit sluggish when it comes to technological evolution.

One of the reasons healthcare is so well-suited for predictive analytics is because of the sheer amount of collected data.

Think about all the electronic healthcare equipment like EKG machines, blood pressure monitors, and digital thermometers. All these things have the potential to connect to a closed intranet and send information.

Robust prescriptive models could prevent disease and even save lives. But alas, that’s still a ways into the future. For now, we have to start with baby steps.

Consider this example from Sajan Kuttappa, a product marketing manager at IBM.

A health insurance company analyzes its data and finds 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 increase, decrease or hold steady.

The business can make informed decisions about how to proceed in a cost-effective way that also serves its customers.

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

Consider how much data hospitals collect in the following categories alone:

• Patients
• Treatments
• Appointments
• Surgeries
• Radiologic techniques

Now imagine if that data got fed into a prescriptive analytics model. The insights could help ensure the proper staffing of hospitals and help triage patients.

Utilizing data rather than gut feelings can save costs on everything from medical supplies to transport fees to food budgets.

Examples of 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 your descriptive, diagnostic, and predictive data and then analyzing it with a prescriptive methodology can impact every step of the sales process.

Consider the following real-world applications of prescriptive analysis:

• Maximizing first-contact success rates
• Sending the perfect piece of collateral that’s had the most historical success with this demographic
• Drivings customers to 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 their customers are in the purchasing process.

By analyzing a wide range of factors, it can then help them rank their leads.

They can focus first on who’s most likely to complete the sale, then address the “fence-sitting” prospects.

It can even offer up suggestions for how to keep specific customers moving through the funnel.

Sales managers can also examine prescriptive data on each sales rep 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, there’s an issue with how they’re opening with clients or showcasing the product.

If they’re losing sales at 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. This level of insight into customer and sales rep behavior is a game-changer.

Prescriptive analytics in sales is our area of expertise. Check out this interview with Accent CEO Pete McChrystal and Subject Matter Expert Nicholas Scahill as they answer questions about the state of Sales AI and how it’s shaping the Sales Enablement industry. 

Examples of Prescriptive Analytics in Retail

We’re willing to bet you’ve already had firsthand experience with prescriptive analytics and you 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.

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.

Examples of 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 number-crunching data scientists.

Their job is to find out things like:

• Which free agents offer the most return on investment
• Which up-and-coming players could be the next superstar
• The projected profitability of certain franchises

It doesn’t stop there, though. Teams are using prescriptive analytics on an even more granular level.

Coaches can suss out 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. These organizations have multimillion-dollar contracts and even more 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.

Examples of Prescriptive Analytics in HR

According to Zenefits, employee payroll is by far the biggest expense incurred by most businesses. Over 68% of the average company’s overhead goes to paying their employees.

While a good hire is costly, a bad hire is far more costly (both to resources and company culture). Therefore, there’s a high motivation to use analytics to streamline and simplify common HR processes.

There’s a pretty clear challenge with prescriptive analytics in HR. Namely, you’re translating traits that make a good employee into a language computers can understand.

But every great journey begins with a small step. Wherever businesses track data in HR, they can implement analytics.

For example, the multi-national credit institution Experian is dipping its toes into some machine learning when it comes to HR.

Specifically, they’re using prescriptive AI to track and prevent employee attrition. They’ve developed a model that flags “high-risk” employees based on predetermined factors.

These factors come from behaviors of former employees preceding their departure or termination.

An example would be several supervisor changes within a small timeframe.

Final Thoughts

By this point, you’ve seen how important data science in general — and prescriptive analytics in particular – can be to business.

We’re still in the early stages of prescriptive analytic adoption in the business world. (Most experts think it will be another few years before full integration occurs.)

But that’s good news. It 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 gain unprecedented visibility into your business.

Further, you’ll figure out new paths 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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