Can multiple parameters be used for performing clustering?

Yes, absolutely! In a clustering solution, you can simultaneously use multiple parameters (or features) to segment your data. This is precisely how customer segmentation (and many other clustering applications) is typically done.

How it Works:

Feature Selection:

You identify the relevant parameters or features that are likely to influence the clustering.

In your example, "frequency of purchase," "value of purchase," and "recency of purchase" are excellent choices for customer segmentation.

Data Preparation:

You prepare your data by:

Handling missing values.

Scaling or normalizing the features (to ensure that features with larger ranges don't dominate the clustering).   

Encoding categorical features if necessary.

Clustering Algorithm:

You choose a clustering algorithm (e.g., K-Means, hierarchical clustering, DBSCAN).

K-Means, for example, calculates the distance between data points based on all the selected features.   

Clustering:

The algorithm groups customers based on their similarity across all the selected features.

Customers with similar purchase frequency, purchase value, and recency will be grouped into the same cluster.

Cluster Profiling:

You analyze the characteristics of each cluster by examining the average values of the selected features for the customers in each cluster.   

This allows you to understand the distinct customer segments.

Example with Your Parameters:

Let's say you're using K-Means clustering with "frequency of purchase," "value of purchase," and "recency of purchase."

Cluster 1 (High-Value Loyalists):

High frequency of purchase.

High value of purchase.

Recent purchases.

Cluster 2 (Occasional Spenders):

Low frequency of purchase.

Moderate value of purchase.

Less recent purchases.

Cluster 3 (New or Low-Value Customers):

Low frequency of purchase.

Low value of purchase.

Potentially recent purchases.

Benefits of Using Multiple Parameters:

Comprehensive Segmentation: Provides a more holistic view of customer behavior.

Improved Accuracy: Leads to more accurate and meaningful customer segments.

Actionable Insights: Enables targeted marketing and customer relationship management strategies.   

Therefore, using multiple parameters is not only possible but also essential for effective clustering and customer segmentation.