Behavioral segmentation allows customers to be divided into segments based on their shopping behavior. This type of segmentation is very important for developing strategies based on customer behavior and increasing customer satisfaction. In this blog post, we will perform behavioral segmentation with the K-Means clustering algorithm using customer data and analyze the resulting insights. The data sets we will use include customer information, shopping details and customer acquisition information.
The goal of the project
The aim of this project is to analyze customers' shopping behavior and segment them according to similar behavioral patterns. In this way, special marketing strategies can be developed for each segment and customer satisfaction can be increased.
Data Sets to Use
Customers: Contains customer information. (CustomerID, Age, Gender, Region)
Orders: Contains shopping information. (OrderID, CustomerID, PurchaseDate, PurchaseAmount)
OrderDetails: Contains shopping details. (OrderID, ProductCategory, CustomerType)
CustomerAcquisition: Contains customer acquisition information. (CustomerID, AcquisitionChannel, AcquisitionDate)
Step 1: Loading and Combining Data
First, we will load and merge these datasets. This step ensures that the data is brought together and prepared for further analysis.
Step 2: Cleaning Missing and Outliers
We will detect and clean missing and outlier values in the data sets. This step is critical to increase the accuracy of the analyses.
Step 3: Calculating Behavioral Features
We will calculate customers' behavioral characteristics such as shopping frequency (Frequency), total spending amount (Monetary) and number of days according to the last shopping date (Recency).
Frequency: The number of purchases made by the customer in a certain period of time.
Monetary (Total Expenditure): The total expenditure amount of the customer's purchases.
Recency (Last Shopping Date): The number of days that have passed since the customer's last purchase.
Step 4: Scaling the Data
We will scale behavioral features. This allows the K-Means algorithm to perform better. Scaling is necessary to keep the data within a certain range and to give equal weight to different features.
Step 5: K-Means Clustering
What is K-Means Algorithm?
K-Means is a clustering algorithm that divides data into a certain number of clusters. The algorithm assigns each data point according to the cluster center (center point) to which it is closest and repeats this process until the clusters are fixed. The K-Means algorithm works with the following steps:
Selecting Initial Cluster Centers: K number of cluster centers are selected randomly or by a certain method.
Assigning Data Points to Cluster Centers: Each data point is assigned to the cluster center to which it is closest.
Update Cluster Centroids: New centroids for each cluster are updated by averaging all data points in the cluster.
Repeating Assignment and Update Steps: The process of assigning data points to cluster centers and updating the centers is repeated until the centers are fixed (until they do not change).
Determining the Optimal Number of Clusters with the Elbow Method
Elbow Method is used to determine the optimal number of clusters. In this method, the intra-cluster error sum of squares (WCSS) is calculated for different cluster numbers and a graph is drawn. The "elbow" point in the graph indicates the optimal number of clusters. At this point, the benefit of increasing the number of clusters begins to diminish.
Step 6: Analysis and Visualization of Segments
We will analyze and visualize the segments we have determined with the K-Means algorithm. We will examine the characteristics and customer behavior of each segment.
Characteristics of Segments and Benefits for Business
High Spending and Frequent Shoppers: This segment can be encouraged by including loyalty programs.
Customers Who Spend Highly But Shop Infrequently: Special offers and discounts can be offered to these customers.
Customers Who Spend Low and Shop Infrequently: Re-acquisition campaigns can be organized for these customers.
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