Customer spending analysis using hierarchical clustering can provide valuable insights into customer behavior and help businesses make informed decisions to optimize their marketing and sales strategies
Hierarchical clustering is a type of unsupervised machine learning technique that groups similar objects or data points together based on their characteristics or features. It is commonly used in customer segmentation analysis to group customers with similar spending behavior and patterns.
To perform a customer spending analysis using hierarchical clustering, you can follow these steps:
Collect and preprocess the customer spending data: Collect the necessary data on customer transactions and preprocess the data by cleaning and formatting it for analysis. This may include removing duplicate records, handling missing values, and transforming the data to a suitable format.
Determine the similarity metric: Choose an appropriate similarity metric to calculate the distance or similarity between customers. Commonly used similarity metrics include Euclidean distance, Manhattan distance, and cosine similarity.
Choose the clustering method: Select a hierarchical clustering algorithm that best suits the data and similarity metric. Commonly used clustering methods include agglomerative and divisive clustering.
Determine the number of clusters: Determine the optimal number of clusters using techniques such as dendrogram visualization, elbow method, or silhouette analysis.
Interpret the results: Once the clusters have been formed, analyze the characteristics of each cluster to gain insights into customer spending behavior and patterns. This may include identifying the most profitable customer segments, understanding the key drivers of customer spending, and developing targeted marketing strategies for each cluster.