Psychographic segmentation allows customers to be segmented based on lifestyle, interests and personality traits. This type of segmentation is used to gain a deeper understanding of customer behavior and develop more effective marketing strategies. In this project, we will perform psychographic segmentation using customer data and analyze the insights obtained. 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' lifestyles and interests and segment them based on their similar psychographic 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, Lifestyle, Interests)
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 Psychographic Characteristics
We will analyze customers' psychographic characteristics such as lifestyle and interests. These features will be used to segment customers.
Lifestyle: Factors that determine the customer's lifestyle.
Interests: Areas and activities in which the customer is interested.
Step 4: Scaling and Categorizing Data
We will scale and categorize psychographic characteristics. This allows segmentation algorithms to perform better.
Step 5: Segmentation with K-Means Clustering
We will segment customers using the K-Means algorithm. First, we will use the Elbow Method to determine the optimal number of clusters.
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
Customers with an Active Lifestyle and Sports Interest: This segment can be targeted for sports equipment and events.
Technology and Innovation Enthusiasts: These customers can be targeted for new technological products and innovative services.
Family and Home Oriented Customers: This segment can be targeted for household goods and family oriented products.
Social and Entertainment Enthusiasts: These customers can be targeted for social events and entertainment services.
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