Unsupervised Machine Learning in Business

What is unsupervised machine learning?

Unsupervised learning is an algorithm that allows machines to learn independently. Machine learning algorithms analyze unlabeled datasets to discover patterns and groupings without human intervention. Unlike other types of machine learning, unsupervised learning algorithms don't rely on training data and instead draw conclusions based on the data they have in front of them. It discovers similarities and differences in datasets and relays that information to us to improve business functions.

There are two main types of unsupervised learning algorithms:

Clustering

Clustering is a learning model where the machine groups data into clusters based on similarities. Cluster unsupervised machine learning finds similarities in datasets and categorizes them based on common features.

An unsupervised learning example of clustering would be if you're analyzing customer data. In this case, it might group all women into one group and men into another.

Association

Association in supervised machine learning is a method used to find relationships between several variables in a large dataset. It's used to determine a set of data that occur together, which can make your marketing more effective.

For example, you can determine how likely customers who purchased one product are to purchase another product.

Unsupervised vs semi-supervised vs supervised machine learning

As we've mentioned, there are several types of machine learning: unsupervised, semi-supervised, and supervised.

Supervised learning differs from supervised learning in that a supervised machine learning model produces outcomes based on training data it uses as examples. These machines already have the correct answers given to them by human data scientists.

Meanwhile, unsupervised learning algorithms use unlabeled data input to draw conclusions without human intervention.

Semi-supervised machine learning models are a combination of the two. They may have some labeled data and unlabeled data points, allowing them to draw conclusions and solve problems.

For example, a machine might look at labeled data or images of different fruits and have to label the rest based on the various features of each fruit.

Supervised machine learning is expensive because teaching machines takes millions of examples. However, semi-supervised machine learning requires fewer examples and allows machines to draw conclusions based on limited training data.

Unsupervised learning algorithms evaluate completely unlabeled data points to find information without help from humans, allowing them to find hidden patterns and group labeled data effectively.

In addition, unsupervised learning algorithms can technically handle more complicated problems than supervised models because they don't rely on training data.

How to use unsupervised machine learning for marketing

Clustering is the most common unsupervised learning model because it can easily group data sets without defining them beforehand.

Supervised ML models find patterns and similarities within unlabeled and uncategorized data points without human intervention.

So what does this mean for your business? Here are a few ways you can use unsupervised machine learning for marketing:

Customer segmentation

Machine learning in marketing can improve personalization efforts with customer segmentation.

Customer segmentation is the practice of grouping customers together based on similar features, such as location, age, gender, interests, and behavior, allowing marketers to create customer personas for each target market.

With customer segments, you can send more personalized email marketing campaigns and find new ways to market to various types of customers based on the labeled data you have on them.

Data exploration and visualization

Data exploration and visualization allow you to see your data points rather than staring at a bunch of numbers in a spreadsheet.

Unsupervised learning can create visualizations, such as graphs and dashboards, to help you learn about various types of data within your organization. Additionally, they can create interactive dashboards that allow you to explore your data more in-depth to better understand what the data means.

Anomaly detection

Clustering makes it possible to detect outliers or differences within the data, also known as anomalies. For example, a financial company might use unsupervised machine learning to alert it of any strange charges on the company card, which indicate fraudulent transactions.

Anomalies occur in data regularly and can help businesses identify times when they need to take action, whether it's financial or not. For example, they may identify outliers in their customer data, which can be grouped to help them uncover a hidden market segment they didn't know they had.

Recommendations

Remember, unsupervised learning can easily group items together based on data. Item recommendations when shopping online or television and movie recommendations on your favorite streaming site are common use cases of machine learning.

Unsupervised learning learns about customers based on their behavior. For example, if you watch a lot of horror movies on Netflix, they recommend more horror movies and television series, thanks to unsupervised learning.

Businesses can use this feature for more effective up-selling and cross-selling to provide suggestions based on products viewed or purchased. For example, if someone opts to buy jeans, the system might recommend a t-shirt to go with them.

Benefits of unsupervised machine learning

Unsupervised learning techniques don't require human intervention, making it more appealing to businesses that don't have the time or resources to spend giving computer systems millions of examples. There are several benefits of unsupervised machine learning for businesses, including the following:

Improved decision making

Businesses rely on data to make informed decisions that can impact the business's success. Unfortunately, companies with large amounts of data will never be able to process it all without the help of machine learning.

Unsupervised machine learning algorithms can extract the relevant data and help companies make better decisions by turning data points into actionable insights into operations, marketing, and money management.

Better customer understanding

Unsupervised learning techniques can predict customer behavior, allowing you to forecast which customers are more likely to purchase a product or service.

It enables predictive data to expand beyond customer geographic and psychographic data and include purchase history and brand engagement. For example, unsupervised learning can be used to predict when customers will drop out of the funnel, allowing you to adjust marketing efforts to cater to at-risk customers.

In addition, it can help you correctly segment and target customers based on their behavior and buying patterns rather than demographics alone, allowing you to create more personalized marketing campaigns.

Improved security

Unsupervised machine learning won't just enhance your marketing campaigns. It can improve security by analyzing network behavior and executing various procedures to prevent cyberattacks. Machine learning can identify potential threats before human IT consultants, allowing it to adjust to changes and eliminate the need for manual analysis.

No training

Unsupervised machine learning doesn't require millions upon millions of training examples to help it perform various data analysis and categorization tasks, allowing you to save time on manual actions.

Additionally, this can cut costs because it requires less time to learn since it's much easier to get unlabeled data than labeled data.

Reduces bias

Bias in statistics is common, especially when machines require human intervention. However, because the data used for unsupervised machine learning is unlabeled, it can help avoid some human errors and biases. However, bias is still possible because humans are still interpreting the data.

Challenges of unsupervised learning algorithms

Unfortunately, unsupervised machine learning isn't perfect, and there are several limitations, including:

Data quality

Results provided by unsupervised machine learning models are less accurate than supervised machine learning models because the data input doesn't contain labels to be used as answers.

Instead, machines are left entirely on their own and must find patterns in data to group data sets together without human intervention.

Additionally, the quality of the result largely depends on the quality of the data. Data quality can be a concern because there's no corresponding output, and machines can't determine which data should and shouldn't be used to draw conclusions.

Scalability

Unsupervised machine learning is less scalable than supervised machine learning because it's less accurate and still requires validation by humans.

Additionally, the training process is time-consuming because unsupervised machine learning algorithms must analyze and calculate various possibilities to provide you with the best output.

Algorithm complexity

Unsupervised models may need to be fed more data as projects progress, which can make the outcome less accurate. To improve accuracy, you must have a complex model that requires experience and advanced skills to determine if your model works.

The future of unsupervised machine learning in marketing

Unsupervised machine learning can help your business succeed by segmenting customers, providing recommendations, and improving data analysis.

With the help of machine learning and AI, you can now optimize your messages and offerings to attract the right customers at the right time to increase sales and revenue.

Ready to use data and machine learning to improve your marketing campaigns? Try Mailchimp's smart recommendations to start optimizing your marketing campaigns.