Introduction
In the 1990s, when marketing was just beginning to adapt to the internet age, marketers relied heavily on traditional media outlets. Back then, if you wanted to reach people who were interested in your product or service, you had two options: advertise in a print magazine or newspaper—and hope they picked up an issue that included your ad; or run TV commercials during shows that aired at peak hours when everyone was watching (like Saturday morning cartoons). Either way, these strategies were very targeted and limited.
Today’s marketers have much more information at their fingertips than they did back then. Consumers are connected 24/7 through social media platforms and search engines like Google—which means there are far more ways for marketers to reach potential customers than ever before! What’s even better is that new technologies allow us to measure actions taken by consumers online without relying solely on surveys or focus groups. When we combine this newfound ability with unsupervised learning techniques from machine learning (ML), we can gain deeper insight into what makes consumers tick while also helping us understand how our own brands fit into all of this data-driven chaos.
In this post I’ll discuss what unsupervised learning is and how it relates specifically to marketing research today; explore types of unsupervised learning techniques; explain why unsupervised learning matters for businesses big and small; share examples of how companies are using it today; give advice about how marketers can start implementing these techniques in their own workflows tomorrow; and finally outline some ways you might be able to apply them right now even if you’re not a data scientist yourself!
What is Unsupervised Learning?
Unsupervised learning is a type of machine learning that uses algorithms to discover hidden patterns in data. It’s not supervised learning, which means the algorithm learns from examples provided by humans. Unsupervised learning allows you to find patterns in data that are not directly observable. For example, if you have a list of words and their meanings, an unsupervised algorithm might be able to figure out what words are related based on their context (e.g., “cat” and “dog”).
Unsupervised Learning in Marketing
Unsupervised learning is a type of machine learning that allows computers to make predictions about data without being told what to look for. Unlike supervised learning, which uses labeled examples to train the algorithm (i.e., if you want your computer to identify cats in photos, you’ll need thousands of labeled images), unsupervised methods use unlabeled data and find patterns within it–a process known as clustering or segmentation.
Unsupervised learning can help marketers gain valuable insights into their customers’ preferences and behaviors without having to ask them directly. For example: You could use unsupervised methods like clustering and segmentation to identify groups of customers based on demographic factors like age or gender; then compare these clusters against other variables such as product interest or purchase history so you can better understand which segments are more likely than others at converting into paying customers for certain products/services offered by your business
How to Apply Unsupervised Learning to Your Business
Unsupervised learning is a powerful tool for marketers, because it allows you to identify trends in your data without any prior knowledge of what those trends might be. It’s also useful for identifying anomalies in your customer base.
Unsupervised learning can help you discover new segments of customers and understand their behavior, which can lead to more targeted messaging or products that meet their needs better. You may be able to use unsupervised learning to identify patterns in customer behaviors so that you know where they spend most of their time online–and then focus on how best to reach them there. For example: If someone searches “how much does it cost” on Google and then visits several pricing pages before making a purchase from one site over another (like this), it could signal a need for better pricing transparency across all channels; if someone spends hours reading reviews before making an expensive purchase like an appliance or car part but never returns after buying one item (like this), then maybe there’s something missing from the experience itself?
Types of Unsupervised Learning
There are a few different types of unsupervised learning, with each one having its own strengths and weaknesses:
- Clustering: Clusters are groups of similar elements that share common characteristics. For instance, you might have a dataset containing information about people’s favorite colors and then cluster them based on their top two colors (and ignore all other preferences). This can be useful for marketers who want to identify trends within their customer base or find new audiences.
- Dimensionality Reduction: Dimensionality reduction helps reduce the number of dimensions needed in data analysis by identifying redundant attributes or relationships between objects. For example, if you’re working with images from the same scene but taken at different angles, dimensionality reduction could help merge these images into one cohesive whole without losing any visual fidelity or detail from any individual photo shoot
There are many ways marketers can use unsupervised learning.
There are many ways marketers can use unsupervised learning. For example, it can be used to classify data or identify patterns in data. It can also be used to predict future behavior based on past behavior patterns.
Conclusion
Unsupervised learning is an exciting new area of machine learning that can help marketers make better decisions. By using unsupervised learning, you’ll be able to understand consumer behavior at a deeper level than ever before and use that knowledge to create personalized experiences for your customers.
