Customer Segmentation by using Cluster Analysis & RFM Analysis
Embedded Files
I'm excited to share a project that I've been working on – a deep dive into the world of customer segmentation. In this endeavor, I've harnessed the power of data analysis to understand and cater to the diverse needs of our customer base.
Project Highlights:
Dataset Exploration: Leveraging the Customer Personality Analysis dataset from Kaggle, I've meticulously examined key variables such as demographics, household composition, engagement metrics, and purchase behavior. It's a comprehensive snapshot of our customers' preferences and behaviors.
Objectives:
Segmentation Analysis: Uncovering distinct customer segments based on product purchase behavior, providing valuable insights into preferences for wines, fruits, meat, fish, sweets, and more.
Customer Profiling: Delving into the unique characteristics and preferences of each segment, offering a nuanced understanding of our customers.
Prioritization Strategy: Developing a strategy to prioritize customer segments based on significance or potential, ensuring our marketing efforts are both targeted and effective.
This project is a culmination of hands-on exploration and analysis, aiming not just at numbers but at understanding our customers at a personal level. Join me on this journey into customer segmentation, where data becomes a powerful tool for crafting personalized and impactful strategies, ultimately leading to enhanced customer satisfaction and business success.
Our exploration of customer segmentation involves strategically categorizing our customer base using key factors:
Demographic Segmentation: Analyzing gender, age, income, education, and marital status for tailored product and communication strategies.
Geographic Segmentation: Considering location, language, transportation, climate, and workplace dynamics for authentic engagement across diverse regions.
Psychographic Segmentation: Exploring personalities, opinions, lifestyles, and attitudes to create sophisticated, emotionally resonant marketing messages.
Behavioural Segmentation: Examining purchasing patterns to refine marketing approaches and products based on customer preferences.
This approach enables us to navigate customer segmentation intricacies, foster meaningful connections, elevate satisfaction, and contribute to sustainable business growth.
RFM analysis is a customer segmentation technique that evaluates three key dimensions:
Recency (R): Measures how recently a customer made a purchase.
Frequency (F): Assesses how often a customer purchases within a given timeframe.
Monetary Value (M): Quantifies the total amount a customer has spent.
Customers are scored based on these dimensions and segmented into groups. This analysis helps businesses identify and prioritize high-value customers, tailor marketing strategies, and optimize engagement efforts.
The assessment of clustering tendency using the Hopkins Statistic involves testing the null hypothesis (H0) that the dataset is uniformly distributed against the alternative hypothesis (H1) that it is not. In this case, with a Hopkins Statistic value of 0.76, which exceeds the threshold of 0.5, the null hypothesis is rejected. Therefore, we can conclude that the dataset exhibits a non-uniform distribution and is clusterable. The Hopkins Statistic, in essence, provides a quantitative measure indicating the likelihood that meaningful clusters exist in the dataset, and a value greater than 0.5 suggests a tendency for clustering.
Determining the optimal number of clusters using the Elbow Method and Silhouette score suggests two clusters, while the Gap Statistic indicates three clusters. These methods assess different aspects of cluster formation, and the choice may vary based on dataset characteristics and analysis goals.
In applying the Kmeans Cluster analysis method, three clusters were identified, and the Dunn Index, with a value of 0.05, was used to assess the quality of the clusters. The Dunn Index measures the compactness and separation of clusters, where a higher value indicates better-defined clusters. In this case, a Dunn Index of 0.05 provides an indication of the quality of the clustering results.
Utilizing R-F-M Analysis, customers are categorized into distinct segments:
1. Can't Lose Them (4%): Highly valuable and loyal customers critical to retain.
2. At Risk (19%): Showing signs of reduced engagement, requiring retention efforts.
3. Hibernating (14%): Previously active but now dormant; needs re-engagement strategies.
4. Loyal Customers (10%): Consistently loyal and valuable customer base.
5. Potential Loyalists (13%): Showing potential for future loyalty; targeted efforts may convert them.
6. Need Attention (9%): Indicates signs of dissatisfaction or reduced engagement, requiring prompt attention.
7. New Customers (13%): Recently acquired; represents an opportunity for nurturing into loyal patrons.
This segmentation guides tailored strategies, from retention initiatives to targeted marketing, based on distinct customer behaviours.
The client segmentation analysis reveals distinct customer clusters with specific behaviors and corresponding potential strategies:
- Cluster 1 (New and Hibernating): Encompasses a significant presence of new and inactive customers, suggesting the need for proactive engagement.
- Welcome Program: Implementing a series of welcome communications to educate new customers.
- Engagement Initiatives: Encouraging product usage and offering guidance to integrate products into their lives.
- Personalized Communication: Crafting personalized messages based on past purchases to resonate with customers' interests.
- Cluster 2 (At Risk but Loyal): Indicates a high percentage of loyal customers showing signs of reduced interaction, requiring retention efforts.
- Win-Back Offers: Crafting special offers or discounts to entice these loyal customers back.
- Customer Surveys: Gathering feedback to understand the reasons behind reduced interaction and addressing concerns.
- Exclusive Content or Deals: Providing loyal customers with exclusive access to new products or special deals.
- Cluster 3 (Varied Engagement): Displays a diverse engagement mix across different segments, calling for targeted and segment-specific approaches.
- Segment-Specific Communication: Developing campaigns tailored to the unique needs of each segment.
- Milestone Rewards: Offering rewards for customer engagement or spending milestones.
- Retention Programs: Implementing strategies to make customers feel valued and less likely to churn.
Page updated
Google Sites
Report abuse