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Supervised Machine Learning: A Vital Tool for Modern Business Leaders

Supervised machine learning algorithms have many business use cases. Learn what supervised machine learning is, and how to use it to improve your business.

Artificial Intelligence (AI) is a popular topic that’s swept the world by storm in recent years.

By allowing computers to do things traditionally performed by humans, AI will enable us to process large amounts of data and perform routine, repetitive tasks in less time. AI continues to evolve, and you may have heard of a subset called Machine Learning.

Machine Learning (ML) uses training data and algorithms to make computers perform various complex tasks. But what do AI and ML have to do with your business?

Many businesses already use AI, such as chatbots, segmentation, product recommendations, and analyzing customer sentiment.

Ultimately, you can use machine learning to transform your work, reducing errors and the time required to perform routine, repetitive tasks.

Supervised machine learning is a type of ML that learns based on fed data, allowing you to teach computers, programs, and software how to perform basic tasks. But what is supervised learning, and how can you use it to improve your business? Keep reading to find out.

What is supervised machine learning and how does it work?

Supervised machine learning is a type of algorithm that learns from training data to predict outcomes and perform various tasks.

With supervised learning algorithms, the machine is trained using labeled data, so it already has the correct answers. Supervised machine learning algorithms can be compared to classroom education; students learn various information from a teacher.

Supervised learning algorithms are trained via data, teaching a set of models that give the desired result.

Over time, the supervised machine learning model learns what you want it to do based on the specific data you’ve given it. There are two types of supervised machine learning algorithms:


Classification supervised machine learning is used when the output or result can be categorized with two or more classes.

For example, you can teach the system to sort spam mail by teaching it what spam mail is. You can also teach it to send certain emails using triggers, ultimately telling the machine what to send and when to send it. In these instances, you tell the machine what to look for, and eventually, they learn to look for it over time.


Regression-supervised learning algorithms are used when the output or result is a continuous or real value subject to change.

For instance, it’s typically used for financial projections because if one variable changes, a dependent variable will also change, giving you a completely new figure from before.

A way you might use regression is to predict the price of a product based on inventory if it’s being discontinued, or how an online calculator might tell you how much house you can afford based on variables like income, debt obligations, and down payment.

There are four steps in the supervised machine model:

  1. Data collection & processing
  2. Model training
  3. Model evaluation
  4. Model deployment

For supervised machine learning algorithms to work, they must have a clear desired result or output. Additionally, it must be trained in how to use certain training data.

For example, email clients automatically sort spam emails for you based on data, such as subject lines, sender reputation, and even actions you’ve taken to intentionally put emails in your spam folder. Once the system is trained, you can evaluate its performance before deploying it at scale.

Supervised vs semi-supervised vs unsupervised machine learning

There are three machine learning types to remember: supervised, semi-supervised, and unsupervised machine learning. As we’ve discussed, machine learning relies on a human inputting data to train it how to behave. Supervised learning fully labels and tags the data with the answer.

Unfortunately, fully labeled data isn’t always possible. Unsupervised learning is a learning model with a dataset that comes without instructions. This dataset is a collection of examples without a desired output or outcome. Ultimately, there is no correct answer fed to the machine.

Instead, the machine must find a way to structure the data and extract useful features by analyzing it. The unsupervised machine learning model organizes data in a few separate ways, including:

  • Clustering: Clustering allows machines to draw conclusions based on their given data. By closing groups of training data together, they can draw some conclusions.
  • Anomaly detection: Anomaly detection can sort through data and find instances of a break in the pattern.
  • Association: Association allows machines to determine which new data should be grouped together based on training data.

What makes supervised and unsupervised learning different is that unsupervised learning is less accurate because it hasn’t been trained with the right answers.

The final type of machine learning is semi-supervised learning, which is a happy medium between accurate supervised learning and less accurate unsupervised learning. Semi-supervised learning requires a training dataset with labeled and unlabeled data. Then, it learns what it can from the labeled data and draws conclusions from the unlabeled data.

Benefits of supervised learning models

Machine learning allows you to accomplish more because a machine can analyze large sets of data for you. There are several benefits of supervised machine learning for your business, including the following:

Improved decision making

Supervised machine learning is accurate because it already knows the answers thanks to its training data. Since it’s primarily rules-based, it only matches records that fit the right conditions.

For example, you can use it to segment customers based on age and know that it’s correct because you’ve already told it what to do and how to do it. Supervised machine learning can be used to make more accurate financial predictions because it consumes unlimited data that it can sort through much faster than a human, allowing you to make better decisions based on error-free data.

Better customer insights

Supervised machine learning can improve your customer insights to help you learn more about customer behavior. Machine learning allows you to analyze information you’ve collected on customers, including recent behaviors and purchases, and interpret them.

Saves time

Sifting through data is time-consuming, and humans easily make mistakes. Supervised machine learning simplifies data entry to mitigate risks associated with accounting or bookkeeping errors. It can also help you calculate customer lifetime values.

For example, you can use data to learn about customer behaviors and predict the probability of conversions.

Challenges with machine learning

Unfortunately, supervised machine learning isn’t perfect. There are several challenges, including the following:

Data bias

There are many types of bias in statistics that can be built into machine learning and AI. Since supervised learning depends on a dataset for answers, it’s easy to build bias into it without realizing it.

Machine learning bias, or AI bias, occurs within supervised learning models because of assumptions they’ve made while learning.

Unfortunately, this bias can cause imbalances in data and issues evaluating the data to provide accurate predictions. Additionally, humans evaluate the outcomes and can create their own biases when reading data compiled by machine learning.

Poor quality data

Data plays a significant role in how supervised learning models behave. If you feed them poor-quality data, they’ll have a poor-quality output.

For machines to learn, there has to be enough data. Machine learning is sophisticated, but it’s not as sophisticated as the human brain (yet). Therefore, it requires tons of data to learn, including thousands of examples and answers.

Not having enough data means getting inaccurate results because the computer relies on examples and must be fed the answer to get it right every time.


Machine learning is expensive, and it can be challenging to find a data engineer. Supervised machines rely on millions of examples, which is time-consuming and expensive.

Using supervised machine learning for your business

Now that you understand supervised machine learning and how it works, you might wonder how your business can use it to improve its internal and external processes. A few use cases of machine learning for businesses include:

Security & fraud detection

Machine learning can also protect your business by filtering spam emails, detecting phishing attacks and informing IT departments, and detecting fraud based on anomaly detection.

Customer segmentation

Supervised machine learning in marketing can create customer segments or groups based on various factors such as recent purchase history, behavior, age, gender, geographical location, and so forth.

With customer segmentation, you can target customers based on behavior, demographics, psychographics, geographics, and more, to ensure your offers reach the right people at the right time.

Customer feedback analysis

Your customers give you feedback across the web. They might write Google, Yelp, or various website reviews, give you star ratings or write blogs about you.

Unfortunately, it's difficult to analyze the amount of customer feedback data your company gets, especially if it's on the web. Supervised machine learning algorithms can help you monitor and track the data while categorizing feedback based on set rules to help you track customer sentiment.

Sales & customer service predictions and recommendations

Your churn rate is the number of customers who don’t complete actions and cease to become customers.

It might be engaged and loyal customers who stop shopping with your ecommerce business or new customers who don’t finish the checkout process during any given period. With supervised machine learning, you can sort customers into groups most likely to churn, separating them from engaged and loyal customers to analyze them more closely and find new ways to market to them to prevent them from churning.

Additionally, supervised machine learning allows you to analyze your customer behavior and recent purchases to provide them with recommendations of products they’re most likely to purchase. Of course, many businesses already do some form of recommendation.

For instance, your favorite streaming service might display shows you’re most likely to enjoy based on movies and shows you’ve already watched.

Predictive analytics

Predictive analytics can help you make more informed business decisions by predicting everything from the likelihood of prospects to convert to certain financial figures. You can anticipate results based on various output variables to help you make better decisions.

Use machine learning as a tool, not a crutch

Machine learning is a tool meant to be used to help you improve efficiency within your organization. However, it still requires human intervention.

If you use automation or machine learning software, you must ensure you provide it with enough high-quality data that it can learn from to produce the most accurate results.

Mailchimp allows you to take advantage of machine learning through automation. Segment your customers, learn from data, and create more effective marketing campaigns today.

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