Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

A Simple Guide to Unsupervised Learning

AI models can make decisions and predictions quicker than any human operator. In order to do so, they have to first learn from data – they can do this through either supervised or unsupervised learning.


What Is Unsupervised Learning?

Unsupervised learning is a machine learning technique in which the AI needs to find patterns and correlations from a set of inputs without being given outputs to the learning algorithm. For example, when trying to define a target market for a new product type.

Unsupervised learning is used when the outputs are unknown or unavailable, either because of scarcity of data, or because training data is too expensive to collect.

Think of AI like a child. Supervised learning would involve teaching the child something we as humans already know, like colors, numbers or vocabulary. Whereas unsupervised learning leaves the child free to solve problems and find inferences by himself, for example by letting him pursue imaginative play or creative activities like writing and drawing.

Compared to supervised learning, unsupervised learning: 

  • · deals with unlabeled data
  • · enables users perform more complex processing tasks
  • · is more unpredictable
  • · can be used to discover the underlying structure of the data
  • · happens in real time


Common Types of Unsupervised Learning Algorithms


Clustering algorithms can help find structures or patterns out of uncategorized data. If you had a child’s ball pit as your raw data, a clustering algorithm would be able to sort all the blue balls into one cluster (or group), the red into another, and so on.

You can modify how many clusters your algorithm will identify, and that will help you control the granularity of these groups.

There is a downside to clustering though – it can overestimate the similarity between groups, and doesn’t treat each data point as an individual. For example, the red balls in the ball pit may be of wildly different sizes, but the algorithm would class them all as the same. So, you need to exercise caution if using cluster algorithms for applications like customer segmentation and targeting, which need to focus on the individuality of each data point.

Anomaly detection

This will automatically detect anomalies (i.e. outliers) in your dataset, and bring them to your attention. It is helpful in discovering fraudulent transactions, hardware faults, or erroneous data points resulting from human error.

Latent variable models

If your data is enormously complex, it can be a bit daunting. Simplifying it by removing ‘noisy’ data can make it more approachable, and help you make sense of it. Latent variable models can help in the preprocessing stage, by reducing the number of features in a dataset (known as dimensionality reduction), or breaking up the dataset into multiple components.


Using Unsupervised Learning in Marketing

1. Customer prediction

People’s behavior is largely unpredictable – as a marketer, you are mostly finding your way in the dark. However, unsupervised learning can be a huge help by filling in the blanks when you have incomplete data sets. For example, one page of your website may have both a high bounce rate and conversion rate. Unsupervised learning can help you figure out why this page is proving so divisive for customers: by analyzing the data and discovering hidden stats and knowledge within it – rather than relying on one correct answer you are training the algorithm to find. Unsupervised learning can unearth trends within the data that will shed light on what is causing customers to behave in such unusual ways.

2. Customer segmentation

Unsupervised learning is often used to create customer segments, dividing your audience into different tiers so you can prioritize targeting more valuable customers. You can segment customers based on four types of factors:

  •   · Demographic (age, gender)
  •   · Geographic (where they are located)
  •   · Psychographic (attitudes, aspirations)
  •   · Behavioral traits (what they do on your site, and what their external interests are)

Leveraging unsupervised models can help you segment your customer base using metrics like lifetime value or probability of conversion. Once you have identified these segments, you can prioritize accordingly depending on your business goals.

3. Finding lookalike audiences

An unsupervised AI model can also be used to find prospects very similar to high-value customers that a business already has. For example, a skincare brand can leverage data collected on all customers who purchased a serum or registered to receive a sample. Using an unsupervised model, it will be able to determine another set of customers who are very similar to those already known, in order to target them with relevant marketing materials.

With the kind of deep insights on these customers’ habits, behaviors and inclinations generated by AI, marketers can create highly personalized and targeted advertisements that are more compelling to this customer base, drastically increasing the chances of success.


* Want to learn more about how AI learns to make predictions and decisions through unsupervised learning, and how it can be applied in advertising? Download our white paper ‘From Supervised to Unsupervised Learning: How AI Is Reshaping Advertising’ for more insights. Still got a question? Contact our team today for more information!


Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.


Applying AI to Global Cross-Border E-commerce and Digital Marketing

Author | Alban Villani, SVP of Sales for Europe, Appier Marketing global means thinking local. The world is getting smaller, and brands can market their products all over the world. Physical borders are no longer an obstacle, and consumers have a wealth of goods at their fingertips. However, it’s vital to clearly understand how other countries and regions operate to sell to them successfully. Access to the internet has spread as individual regions have gained better technology infrastructure, and Southeast Asia in particular has seen an explosion in internet penetration. According to a 2019 Google-Temasek report, the Southeast Asia internet economy will be worth US$240 billion by 2025. What does this mean for cross-border e-commerce into and around Asia and the digital marketing that supports it? Shoppers in countries like Japan, South Korea and China typically prefer to purchase from local e-commerce platforms. According to DataReportal, Southeast Asia is seeing an increasing number of people shopping outside their home countries, including via Amazon, Shopee, Lazada and Tokopedia, offering shoppers more variety. The pandemic means that people have been restricted to online shopping almost exclusively, leading them to look for new products in new places both in terms of platforms and

8 Onboarding Techniques You Need to Know for Long-Term App Engagement

It is great that people are installing your app, but install alone doesn’t guarantee your app success: 25 percent of apps are only ever used once. So, how to keep those users sticking around and continuing to engage with your app? Onboarding is not only the start of your relationship with an app user, but also a vital step to building long-term engagement. Think of onboarding as a first impression. Get it right, and you will have a higher chance to keep a customer for the long run. Get it wrong, however, and you might lose that customer forever. So, how to leave that lasting first impression? Here are eight onboarding techniques that can help you get users hooked on to your app for a long term.   1. Simplify Your Sign-Up Process A customer’s time is precious. They don’t want to spend any longer than necessary on digital admin like signing up for a service. If you can simplify the process, perhaps by letting them sign in with their Google or social media account, you will save them time and make them enamored of your brand.   2. Send a Tutorial Loading an app for the first time can

What Is Churn Prediction?

Customer churn prediction can help you see which customers are about to leave your service so you can develop proper strategy to re-engage them before it is too late. This is a vital tool in a business’ arsenal when it comes to customer retention. Wondering what churn prediction is, and how it actually works? Read on, and all will be explained…   What Is Churn Prediction? Churn quantifies the number of customers who have left your brand by cancelling their subscription or stopping paying for your services. This is bad news for any business as it costs five times as much to attract a new customer as it does to keep an existing one. A high customer churn rate will hit your company’s finances hard. By leveraging advanced artificial intelligence techniques like machine learning (ML), you will be able to anticipate potential churners who are about to abandon your services.     Why Is It Important? The truth is you probably already have more customer data than you know. By leveraging this data, you are able to identify behavior patterns of customers who are likely to churn. This knowledge will enable you to segment those customers and take the appropriate measures