What is Prescriptive Analytics?
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Kelli Folse

What is Prescriptive Analytics?

How to use technology to predict and respond to the behavior of your future customers.

The wide world of business analytics

Business analytics is using data to inform business decisions. It is also sometimes referred to as business intelligence.

The scope of business analytics has grown significantly. Especially as technology and artificial intelligence (AI) have become more powerful and accessible.

There’s a reason data scientists are consistently in high demand. They are professionals trained to wrangle, visualize, and interpret large amounts of data.

There are full-time positions (sometimes even whole departments) dedicated to data science. They analyze customer behavior, browse patterns, and buying sentiment. Almost everything can be tracked, analyzed, and acted upon to gain a competitive edge. From broad purchase decisions to granular website behavior.

Before we dive into how prescriptive analytics works, we need to take a brief look at three other modes of analysis. These serve as the fundamental building blocks of prescriptive analytics.

Types of business analytics

The scope of this post will stay within the bounds of prescriptive analytics. But if you want a deep dive into all four of the main types of business intelligence, be sure to read this comprehensive post on the topic.

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In summary, here are the four main types of business analysis:

  • Descriptive analytics (cleaning and visualizing data of past events)
  • Diagnostic analytics (teasing out correlations and creating hypotheses based on past events)
  • Predictive analytics (using conclusions from past events to try to predict future events)
  • Prescriptive analytics (prescribing or executing the best possible action based on the predicted future)

The first two types of analysis (descriptive and diagnostic) are reactive analytics. Nothing in the future is changed, and the data is strictly rear-facing. They deal strictly with the past.

The second two types of analysis (predictive and prescriptive) are proactive analytics. These two forms of analysis look to the future in an effort to align business objectives with a projected reality. This is where business intelligence gets very powerful.

Both predictive and prescriptive analytics are necessary and helpful in the business world. Prescriptive analytics offer up an action-oriented solution. Rather than simply identify patterns or issues in customer data.

It’s important to note that reactive analytics are not inferior to proactive analytics. They’re foundational and necessary to build upon. They don’t provide answers for actions to take in the future, but without them predictive and prescriptive analytics could not exist.

What is prescriptive analytics?

Prescriptive analysis involves running predictive data through complex simulation algorithms. Then outputting the best result for the end user. Daniel Bachar from Logility summarizes it well:

“At their best, prescriptive analytics predict not only what will happen, but also why it will happen, providing recommendations regarding actions that will take advantage of the predictions.”

Prescriptive analytics is the highest and most coveted form of business analytics. It is the ultimate goal of all prior analysis.

Not only does this form of analysis study the past and predict the future, but as the name implies, “prescribes” the most appropriate actions to take.

On its face, it seems like a superpower. All we need to do is input data and some sort of machine spits out exactly what action we need to take to be successful? If it’s that easy, why isn’t everyone doing it?

There are challenges to implementing this kind of analysis. It’s certainly one of the most up-and-coming ways businesses are edging out the competition.

Gartner anticipates the prescriptive analytics software market will continue to grow. They predict it will experience a 20.6 percent CAGR between 2017 and 2022. This suggests that nearly 37 percent of businesses will start using prescriptive analytics.

Certain organizations are even beginning to process unstructured data. Examples of unstructured data include photos, videos, or music files. They do this to inform and predict user behavior. Google Photos, for example, has facial recognition software. It will detect the persons present in your uploaded photos and sort them into specific albums.

As you continue to manually sort faces into albums, the app “learns” how best to analyze the faces of your friends and family for future sorting.

But how does it all work? And how can it help your organization best respond to the future?

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How does prescriptive analytics work?

There are many ways to accomplish prescriptive analytics. But all technologies and methods boil down to three crucial mechanisms:

  • A mechanism to store and visualize past data
  • A mechanism to use that data to predict the future
  • A mechanism to interpret this predicted future and best respond to it

This is a simple summary. Obviously, a lot of technology, manpower, and effort go into executing these three steps.

It’s clear why computers and AI are the tools of choice for prescriptive analytics. Trying to compute all the relevant data and produce accurate results manually is unfathomable. Even for the most advanced marketing, sales, or business intelligence team, it’s a nearly impossible task.

Another culturally relevant example of prescriptive analytics in action is Waymo. Waymo is Google’s self-driving car. Even the tagline of their website is “We’re building the World’s Most Experienced Driver.” This language is no accident.

The incredible thing about AI is that it’s constantly learning, growing, and — as Google says — “building”. That is true both in the world of self-driving cars and sales and marketing. It never stops, much how humans never stop learning as they grow older.

You may hear the term “neural networks” thrown around as you dive deeper into prescriptive analysis. This refers to the way a machine learns, which mirrors how a child learns. Every new piece of data creates a virtual connection in the “brain” of the machine. This exponentially increases the machine’s ability to interpret new data.

This “machine learning” technology isn’t a specific coding language or application. It takes many different forms, from self-driving cars to sales enablement platforms.

It helps determine when a car should slow down, brake, or accelerate, or when a customer is most primed for an inside sales rep to call. Every piece of input fed into modern AI models exponentially increases the probability of success in the output.

These suggestions or prescribed solutions are based on ever-evolving predictive models and historical data. This data is constantly being compared against historical behavior to produce the best possible result.

It truly is like something out of a science fiction novel. But it’s also great for business.

Common prescriptive analytics technologies

We’ve already briefly touched on neural networks. Other common technologies that are used for prescriptive analytics include the following:

  • Linear, time-based, and logistic regression simulations
  • Complex event processing
  • Recommendation engines
  • Heuristics
  • Machine learning
  • Naïve Bayes conditional probability

If you’re primarily concerned with sales, it’s rare that you’ll run across any of these “in the wild.” These are mostly back-end technologies baked into software solutions or platforms.

For example, our sales content management platform Accent Connect offers intelligent content suggestions based on individual sales situations. Our technology utilizes machine learning to sift through situational variables. It also locates the most relevant content for the prospect.

Examples of prescriptive analytics

To show how common prescriptive analytics is in today’s marketplace, here are a few industry-specific examples.

Prescriptive analytics in banking

You’ve likely received a text or phone call alert from your bank notifying you of potential fraudulent charges. This process isn’t usually monitored by humans. It’s monitored by complex AI technologies that study, predict, and prescribe solutions based on prior charges.

Companies like American Express and CitiBank use sophisticated predictive models. They use them to forecast and prevent customer churn. Once again, using machine learning, these banks and others are able to detect when cardholders are most likely to switch to a different bank. Thus, they can respond accordingly with discounts, promotional credit, and other incentives.

Prescriptive analytics in retail

There is one form of prescriptive analytics that is so commonplace, you’ve likely seen it multiple times this week without even realizing it. It is, of course, Amazon’s recommendation engine. Everything you see on the Amazon.com homepage is motivated by your buying and browsing patterns.

Carousels are displayed with titles like “Inspired by your Wish List” and “Books You Might be Interested In.” And 99% of the time, they’re spot on. How can this be?

Amazon analyzes your data and cross-references your purchasing patterns against other like-minded buyers. It does this to create upsell and cross-sell opportunities. This system is so intelligent, it can even transcend whole categories.

If you purchase a book on landscape photography, it won’t just offer up another book on photography. It will offer up a new camera travel bag and wide-angle lens.

When it comes to retail, many businesses are replicating what Amazon has pioneered. Still, Jeff Bezos’ creation still remains the gold standard for how to use data to drive business.

Prescriptive analytics in healthcare

Prescriptive analytics are commonly used by hospitals and large healthcare networks. They’re used to improve patient outcomes. Historical data and predictive models are analyzed to determine the cost-effectiveness of certain medical procedures. They can even determine which patients have the highest risk of readmission or relapse.

Everything from medication prices to the efficacy of high-risk surgeries can be analyzed. They can even be leveraged to create better solutions for patients and healthcare organizations alike.

Prescriptive analytics in education

Public schools and universities have a more challenging time utilizing prescriptive analysis. But the online education space is way ahead of the curve. Online education marketing is projected to reach $350 billion by 2025 globally. This is no surprise. The inexpensive, scalable model of sites like Skillshare, Lynda.com, Khan Academy, Udemy, Teachable, and countless others are beginning to make traditional college degrees look obsolete.

These sites can analyze user behavior and determine gaps in their skillset that they need to succeed in the workforce. For example, if a user shows interest in web development or JavaScript programming, the AI may recommend a course on data science next.

If the user shows no interest in the recommendation, the platform may recommend a course on app development. All this serves to help the AI learn interest patterns and common career trajectories for future users.

Sites like this can also reduce churn by detecting when users are inactive and recommending a new and exciting course to keep them engaged.

Prescriptive analytics in travel

Airline companies are often masters of prescriptive analytics. This is why there is such a dramatic variance in the cost of air travel. AI systems are constantly analyzing variables such as weather, customer demand, time of year, even gasoline prices. The resulting data affects ticket prices on a day-by-day basis.

It’s far more complex than ticket prices being high during the holidays and low during the working months. There is an entire AI-powered economy happening under the hood of most airline organizations.

How you can utilize prescriptive analytics

When it comes to sales enablement and marketing, prescriptive analytics is your best friend.

Data is the lifeblood of customer behavior. The most successful sales teams understand this. They embrace prescriptive analysis as a powerful competitive advantage.

Imagine being able to articulate prospects’ pain points better than they can. Imagine knowing exactly when to place a phone call to a potential buyer at the precise time that they’re the most likely to buy.

However, the further up the hierarchy you go (from descriptive, to diagnostic, to predictive, to prescriptive), the more challenging the analysis becomes to execute manually. Unaided by technology and software, scaling your business intelligence beyond just one customer is a fool’s errand.

That’s why we’ve built AI-driven sales enablement tools that automate the hard work of business analytics. Additionally, they provide prescriptive solutions that help you sell more efficiently.

The software does the hard work of predictive models, algorithms, and computations. That way, your team can get back to selling.

To learn more about how you can utilize prescriptive analytics to understand your future customers better and close more deals, sign up for a free demo of our platform.

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