Behavioral segmentation allows customers to be segmented based on their shopping behavior. This type of segmentation is very important to develop strategies based on customer behavior and increase customer satisfaction. In this blog post, we will perform behavioral segmentation with RFM (Recency, Frequency, Monetary) analysis using customer data and analyze the obtained 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 RFM 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: Calculating RFM Scores
We will assign RFM scores to each customer and segment customers using these scores.
R Score: It is determined according to the customer's last shopping date (Recency).
F Score: It is determined according to the customer's shopping frequency.
M Score: It is determined according to the customer's total spending amount (Monetary).
These scores are combined to create an RFM score unique to each customer.
RFM Segments
Some common segments created with RFM segmentation are:
Champions (555): Customers in this segment are those who have shopped most recently, shop frequently, and spend high amounts.
Loyal Customers (544, 545, 554, 555): These customers shop frequently and spend high amounts. They are loyal customers.
Potential Loyalists (344, 345, 355, 445, 455): Customers in this segment may have made recent purchases and are potentially loyal customers.
Recent Customers (511, 521, 531): These are customers who have shopped recently but have low shopping frequency and spending.
Promising (411, 421, 431): These are customers who have recently shopped and have spending potential.
Need Attention (311, 321, 331, 411, 421, 431): These customers are customers who have low shopping frequency and spending, but have shopped recently.
At Risk (211, 221, 231, 311, 321, 331): These are customers who have not shopped for a long time but have made high expenditures in the past.
Can't Lose Them (111, 112, 113, 121, 122, 123): These are customers who have not shopped for a long time and are about to be lost as loyal customers.
Business Benefits and Insights
The insights gained as a result of RFM segmentation can help develop valuable strategies for the business. For example:
Champions: Customers in this segment can be encouraged by including them in loyalty programs.
Loyal Customers: These customers may be offered special offers and discounts.
At Risk: Re-acquisition campaigns can be organized for these customers.
Can't Lose Them: These customers can be regained by establishing special communication.
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