What is it?
Hierarchical clustering is a technique in market research that organizes similar items into clusters based on their shared characteristics. This method helps reveal underlying structures and relationships within a dataset. Clusters fall into a hierarchical order allowing the resulting output to be visualized as a tree-like diagram called a dendrogram.
Imagine for example you are researching different flavors options for a new line of products. Hierarchical clustering would help you group similar flavours together, resulting in an output that might look like the dendogram below.
Simple flavour grouping example including reach. Click on groups to hide.
How does it work?
Although there are many different options for hierarchical clustering, the overall process is similar for all variations. The algorithm begins by treating each item as an individual cluster. Then, based on the similarity method chosen, clusters are compared and rejoined into larger clusters. This process is repeated until everything is grouped together.
Why use it?
Hierarchical clustering is an essential tool for understanding the relative relationships between sets of items, whether large or small. It can look into attributes like features, price range, or customer reviews to shed light on natural groupings in consumer preferences. These insights translates into actionable outcomes for your business, facilitating tasks such as efficient inventory management, strategic pricing, targeted marketing efforts, much more.