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Business utilities of Machine Learning & Predictive Analytics

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What’s the first thing that comes to mind when you hear “artificial intelligence” (AI)? While I-Robot was a great film, it doesn’t count. Many don’t realize how deep the rabbit hole goes, either. There are dozens, if not hundreds, of subsets of AI.

They all work in their own unique way with different benefits and uses. Unfortunately, 37% of executives struggle to understand how the technologies work. This confusion naturally leads to the question: “Which one should my company use, and how do we deploy it?”

In my opinion, two of the most effective forms of AI are machine learning and predictive analytics. Don’t worry if you’re stuck on the fence about them — I’ll be explaining how businesses can apply these two forms of AI, what separates them, and what’s suitable for different types of organizations.

What is machine learning?

Machine learning is a class of artificial intelligence that takes current data to train models and algorithms. It’s the poster child of AI: a technology that gets smarter the more you use it.

It also comes in two forms: supervised and unsupervised. The first requires the operator to set the desired output, label the data, give it parameters and look over its shoulder. This option ensures it doesn’t make mistakes and is heading in the right direction. This is ideal for companies that haven’t deployed it before, as you have increased control.

Unsupervised machine learning is where no training data is provided. It’s like a bird leaving the nest and left to learn (no pun intended) on its own. It analyzes a given body of data and creates its datasets. You’ll find this form of machine learning great if you don’t have vast amounts of data to leverage in the beginning.

What is predictive analytics?

Predictive analytics is very similar to machine learning. Albeit, it is slightly different. It uses both current and historical data to make — as you could guess — predictions about future outcomes. It automates forecasting, so organizations can place their focus on critical daily tasks versus creating predictions manually.

Now, of course, this requires a sizeable amount of historical data. Otherwise, the predictive analytics software may not have enough information to find specific patterns and trends and visually display them.

Machine Learning Use In Business

The first significant use of machine learning in business is cybersecurity. Seeing as there are hacker attacks every 39 seconds, it’s more important than ever to secure online assets. However, it’s time-consuming. You don’t have time to sit around waiting for bad guys to show up.

Machine-learning technology is capable of scanning business assets to locate security risks and where possible threats originate. Organizations can then patch these up and keep an eye on vulnerable areas.

Secondly, machine learning is effective at analyzing advertising performance for deep insights. It detects what creatives, sales copy, channels and other components are moving the needle and which aren’t.

To get the most out of machine learning, ensure that your business has enough data to make a dent. Otherwise, the pattern recognition and algorithms won’t have much to work with.

IBM Watson is one of the industry’s best machine-learning platforms to build and scale AI models. When choosing a machine-learning tool, be on the lookout for cloud environments, easy integration into existing tools and fast deployment.

Predictive Analytics Use In Business

The first way predictive analytics is used within a business is by predicting the ROI and performance of marketing campaigns. After all, who wants to spend months creating a campaign only for it to fizzle out? You can find out and secure success sooner than later with this technology.

This option is accomplished by taking a campaign’s previous data and forecasting it into the future. The software can display foresight on KPIs, including revenue, churn rate, conversion rate and other metrics.

Similarly, predictive analytics thrives in finances. It is capable of forecasting expenses in the future, allowing a company to adjust its spending. Companies should leverage this to reduce costs while continuing to generate a steady return.

The next option is customer behavior. Each segment is wildly unique and behaves in different ways. Naturally, it’s difficult to understand what they might do during various processes.

That’s where predictive analytics comes into the picture. It takes historical customer behavior to determine what they likely will do in the future, such as:

• What products they may purchase.

• Where they will navigate on a website.

• What content they will interact with.

• Where they will churn.

• And more.

Knowing this information is like having a crystal ball. You can then focus on what customers want and position offers in the right spots to maximize conversions.

Predictive analytics software like SAS Advanced Analytics is ideal for companies that have a large number of campaigns, existing data and projects.

I suggest looking for fast integrations to begin reaping the benefits quickly, as well as visual dashboards and easy-to-read reports, so your team can take action sooner.


AI comes in many shapes and forms. It also has mountainous benefits for companies in 2020. Two of the main types of AI to adopt are machine learning and predictive analytics.

The former uses supervised rules and settings to produce a specific outcome. It learns the best next action or opportunity by continually analyzing incoming data. Alternatively, you may opt for unsupervised machine learning once your organization is experienced enough with it.

Predictive analytics, on the other hand, is a technology used to predict how marketing, finances and other components of business will perform. This option allows a company to forecast whether or not their efforts will generate a return, next best actions and budget allocation. Companies with large amounts of historical data will find it the most beneficial.

This article has been published from a wire agency feed without modifications to the textx. Only the headline has been changed.

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