IKH

Practical Example of Clustering – Customer Segmentation

In the last segment, you got a basic idea of what clustering is. So let’s consider a real-life application of the unsupervised clustering algorithm.

Customer segmentation for targeted marketing is one of the most vital applications of the clustering algorithm. Here, as a manager of the online store, you would want to group the customers into different clusters, so that you can make a customised marketing campaign for each of the group. You do not have any label in mind, such as good customer or bad customer. You want to just look at patterns in customer data and then try and find segments. This is where clustering techniques can help you with segmenting the customers. Clustering techniques use raw data to form clusters based on common factors among various data points. This is exactly what will also be done in segmentation, where various people or products will be grouped together on the basis of similarities and differences between them.

As a manager, you would have to decide what the important business criteria are on which you would want to segregate the customers. So, you would need a method or an algorithm that itself decides which customers to group together based on these criteria.

Sounds interesting? Well, that is the beauty of unsupervised learning, especially clustering. But before we conclude this introductory session, it would be best to get an industry perspective on the application of clustering in the world of analytics.

You saw that, for successful segmentation, the segments formed must be stable. This means that the same person should not fall under different segments upon segmenting the data on the same criteria. You also saw that segments should have intra-segment homogeneity and inter-segment heterogeneity. You will see in later sessions how this can be defined mathematically.

Now you will see what types of market segmentations are commonly used.

You saw that mainly 3 types of segmentation are used for customer segmentation:

  • Behavioural segmentation: Segmentation is based on the actual patterns displayed by the consumer
  • Attitudinal segmentation: Segmentation is based on the beliefs or the intents of people, which may not translate into similar action
  • Demographic segmentation: Segmentation is based on a person’s profile and uses information such as age, gender, residence locality, income, etc.
  • You will also learn in later sessions about the different types of behavioural segmentations used in the industry.

Report an error