Cluster analysis is a statistical method used to identify and group similar data points together while also highlighting differences between groups.
Imagine a clothing retailer grouping customers based on purchasing habits — frequent buyers, seasonal shoppers, or one-time purchasers. Cluster analysis helps businesses identify these groups and tailor marketing strategies, from targeted ads to personalized offers.
The purpose of cluster analysis in marketing is to segment consumers into distinct groups with similar characteristics, allowing businesses to understand their target audience better and tailor their marketing strategies accordingly.
What you’ll learn:
- What is cluster analysis, and how does it work?
- What is the purpose of clustering datasets?
- Why is cluster analysis important for business strategy?
- What are the different types of clustering and when do you use them?
- What are the characteristics of a good cluster analysis?
- What are the disadvantages of cluster analysis, and how can companies avoid problems?
- How do you perform cluster analysis?
- What do you do with the results of a cluster analysis?
- How to ensure accurate, actionable cluster results
- Practical steps to get started with cluster analysis
What is cluster analysis, and how does it work?
Cluster analysis is a type of unsupervised classification, meaning it doesn’t have any predefined classes, definitions, or expectations up front. It’s a statistical data mining technique used to cluster observations similar to each other but unlike other groups of observations.
An individual sorting out the chocolates from a sampler box is a good metaphor for understanding clustering. The person may have preferences for certain types of chocolate.
When they sift through their box, there are lots of ways they can group that chocolate. They can group it by milk chocolate versus dark chocolate, nuts versus no nuts, nougat versus no nougat, and so on.
The process of separating pieces of candy into piles of similar candy based on those characteristics is clustering. We do it all the time.
For instance, an ecommerce platform may group customers by purchasing habits—such as budget-conscious shoppers, premium product buyers, and occasional browsers. This segmentation allows the platform to create tailored promotions for each group, driving engagement and sales.
Understanding cluster analysis.
Cluster analysis is at the forefront of data analysis. It’s no wonder fields like finance, insurance, retail, ecommerce, and marketing find it useful to identify patterns and relationships within their data.
There are five main clustering approaches. The most common are k-means clustering and hierarchical (or hierarchy) clustering. The clustering approach an organization takes depends on what is being analyzed and why. With visualization techniques such as scatter plots and dendrograms, businesses can effortlessly showcase their cluster analysis results in a clear and understandable way.
What is the purpose of clustering datasets?
The general purpose of cluster analysis in marketing is to construct groups or clusters while ensuring that the observations are as similar as possible within a group.
Ultimately, the purpose depends on the application. In marketing, clustering helps marketers discover distinct groups of customers in their customer base. They then use this knowledge to develop targeted marketing campaigns.
For example, clustering may help an insurance company identify groups of motor insurance policyholders with a high average claim cost.
The purpose behind clustering depends on how a company intends to use it, which is largely informed by the industry, the business unit, and what the company is trying to accomplish.
Why is cluster analysis important for business strategy?
Cluster analysis can benefit a company in multiple ways, including how they market their products.
It can affect whom they market those products to, what retention and sales strategies might be employed, and how they might evaluate prospective customers.
They can cluster current customers and determine their lifetime value relative to their propensity for attrition, and that can inform how they communicate with different customers and how to identify new high-value customers.