How to Create a Customer Segmentation Model in SQL

Customer segmentation is a powerful marketing strategy that enables businesses to categorize their customers into distinct groups based on shared characteristics and purchasing behavior. This approach helps organizations tailor their marketing campaigns, improve customer engagement, and maximize revenue.

By utilising SQL based customer segmentation, businesses can analyze large datasets, gain actionable insights, and implement data-driven strategies for better customer retention, personalized marketing, and optimized resource allocation. In this article, we will explore how to implement customer segmentation using SQL, with step-by-step queries to calculate RFM scores and categorize customers into meaningful groups.

What is Customer Segmentation?

Customer segmentation is a marketing strategy that divides customers into distinct groups based on shared characteristics. This approach enables businesses to tailor their marketing efforts, optimize customer engagement, and improve overall revenue.

One of the most effective segmentation techniques is RFM (Recency, Frequency, Monetary) Analysis. It categorizes customers based on their purchasing behavior:

• Recency (R): Measures how recently a customer made a purchase. Customers who have purchased more recently are more likely to buy again.
• Frequency (F): Tracks how often a customer makes purchases. Frequent buyers tend to be more loyal and engaged.
• Monetary (M): Represents the total amount a customer has spent. Higher spenders are valuable customers who contribute significantly to revenue.

By analyzing RFM scores, businesses can segment customers into groups such as loyal customers, high-value customers, at-risk customers, and potential churners.

For example, a customer with high recency, high frequency, and high monetary value is a Champion Customer while a customer with low recency and frequency may be at risk of churn.

SQL-based customer segmentation helps businesses make data-driven decisions, improve customer retention, and enhance personalized marketing strategies.

How Can Customer Segmentation Help Your Business Grow?

Customer segmentation plays a crucial role in business growth by enabling companies to tailor their strategies to different customer groups. Here’s how it helps:

• Personalized Marketing Campaigns: By segmenting customers based on their behavior, businesses can send targeted promotions, personalized recommendations, and customized email campaigns. This increases engagement and improves conversion rates.
• Improved Customer Retention: Identifying high-value and at-risk customers allows businesses to implement retention strategies, such as loyalty programs and special discounts, to keep customers engaged and prevent churn.
• Better Product Development: Understanding customer preferences helps businesses develop products that meet specific needs. For instance, feedback from loyal customers can guide product enhancements and innovations.
• Optimized Pricing Strategies: Segmenting customers based on spending behavior enables businesses to offer dynamic pricing models, discounts, and premium services to maximize revenue.
• Efficient Resource Allocation: By focusing marketing and sales efforts on high-value customer segments, businesses can allocate resources effectively and achieve higher ROI.
• Enhanced Customer Experience: Segmented customer insights help businesses provide better customer support and personalized services, leading to increased satisfaction and loyalty.

Customer segmentation ultimately drives business growth by fostering stronger relationships, improving decision-making, and boosting overall profitability.

How to Create a Customer Segmentation Model in SQL

Step 1: Setting Up the Data

To build our segmentation model, we need a sample table named transactions that contains customer purchases as shown below:

Step 2: Calculating Recency

Recency measures the number of days since a customer’s last purchase.

SELECT customer_id,
       DATEDIFF(CURRENT_DATE, MAX(purchase_date)) AS recency
FROM transactions
GROUP BY customer_id;

Output:

Explanation: This query calculates the recency of each customer’s last purchase by finding the difference between the current date qnd their most recent purchase date. It groups data by `customer_id` and uses `MAX(purchase_date)` to get the latest purchase. Businesses use this to identify active and inactive customers for targeted engagement strategies.

Step 3: Calculating Frequency

Frequency represents the total number of purchases made by each customer.

SELECT customer_id,
       COUNT(purchase_id) AS frequency
FROM transactions
GROUP BY customer_id;

Output

Explanation: This query calculates the purchase frequency of each customer by counting the number of purchases (`purchase_id`). It groups data by `customer_id`, ensuring each customer has a single frequency value. Businesses use this to identify repeat customers and analyze buying behavior for retention strategies.

Step 4: Calculating Monetary Value

The monetary value represents the total amount spent by each customer.

SELECT customer_id,
       SUM(purchase_amount) AS monetary
FROM transactions
GROUP BY customer_id;

Output:

Explanation: This query calculates the total amount spent by each customer by summing their purchase_amount. It groups transactions by customer_id using GROUP BY, ensuring each customer has a single total value. Businesses can use this data to analyze customer spending patterns for targeted marketing and sales strategies.

Step 5: Assigning RFM Scores

We categorize customers by ranking them into high (3), medium (2), and low (1) based on Recency, Frequency, and Monetary values.

Explanation:We categorize customers by ranking them into high (3), medium (2), and low (1) based on Recency, Frequency, and Monetary values

Step 6: Categorizing Customers

Based on the RFM scores, we segment customers into different categories:

Step 7: Using SQL for Automated Segmentation

To automate the process, we can create a stored procedure to calculate RFM scores dynamically.

WITH rfm AS (
    SELECT customer_id,
           DATEDIFF(CURRENT_DATE, MAX(purchase_date)) AS recency,
           COUNT(purchase_id) AS frequency,
           SUM(purchase_amount) AS monetary
    FROM transactions
    GROUP BY customer_id
)
SELECT customer_id,
       CASE WHEN recency <= 3 THEN '1' WHEN recency <= 7 THEN '2' ELSE '3' END AS recency_score,
       CASE WHEN frequency >= 3 THEN '3' WHEN frequency = 2 THEN '2' ELSE '1' END AS frequency_score,
       CASE WHEN monetary >= 1500 THEN '3' WHEN monetary >= 800 THEN '2' ELSE '1' END AS monetary_score
FROM rfm;

Output:

Explanation:

1. Recency Score: Customers who made recent purchases get a lower score (1 is best, 3 is worst).

Example: A customer with a purchase in the last 3 days gets a recency score of 1, while one who purchased more than 7 days ago gets a 3.

2. Frequency Score: Customers who purchase frequently receive a higher score.

Example: A customer who made 3 or more purchases gets a score of 3, while those with only 1 purchase get a 1 Monetary Score:

3. Monetary Score: A customer who spent ₹1700 gets a 3, while one who spent ₹600 gets a 1. This segmentation helps businesses identify high-value customers for personalized marketing and engagement strategies.

Conclusion

Customer segmentation plays a crucial role in business growth by helping companies identify high-value customers, potential churners, and loyal buyers. By implementing RFM Analysis in SQL, businesses can segment their customer base efficiently and make data-driven decisions to improve engagement and retention.

With SQL queries, companies can automate the segmentation process, analyze purchasing behavior, and develop targeted marketing strategies. Whether it is offering personalized promotions, optimizing pricing models, or enhancing customer experience, customer segmentation ultimately leads to increased profitability and stronger customer relationships. By leveraging the power of SQL, businesses can take their customer segmentation efforts to the next level and achieve long-term success.