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 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 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:
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.
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.
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.