If you want to understand customer behavior through data, SQL analysis is a simple and practical tool. 1. Customer activity analysis can identify active or lost customers by counting the number of orders and the time of the most recent orders, such as filtering customers who still have orders after 2024. 2. Purchase preferences can be counted by linking orders and product tables to provide a basis for marketing and inventory. 3. Identification of lost customers can take timely recall measures by finding customers who have not placed orders in the past three months but have placed orders many times.
Want to understand customer behavior through data? SQL analysis is a simple and practical tool. As long as the data is in the database and a few query statements are used, the customer's access frequency, purchase preferences and even churn signals can be discovered.
Customer activity analysis: See who comes often and who has left
The most direct way to understand customer activity is to check their recent interaction time and frequency. For example, you can group by customers, count the number of orders or visits for each customer, and find out the time of the last operation.
SELECT customer_id, COUNT(order_id) AS total_orders, MAX(order_date) AS last_order_date FROM orders GROUP BY customer_id HAVING MAX(order_date) > '2024-01-01'
This sentence can help you select customers who place orders after 2024, which means they are still active. If a customer hasn't appeared for a long time, it may be on the verge of churn, and it is worth making some reminders or discount recalls.
Purchase preferences are visible: Which products are more popular?
Want to know what type of products your customers like? You can use association tables (such as orders and products) to count the sales volume of each product, or combine classification dimensions for aggregation.
SELECT p.category, COUNT(*) AS total_sold FROM orders o JOIN products p ON o.product_id = p.product_id WHERE o.order_date BETWEEN '2024-01-01' AND '2024-12-31' GROUP BY p.category ORDER BY total_sold DESC
This way you can see which category sells well and provide a basis for inventory management or marketing strategies. If you operate an e-commerce platform, you can further cross-analyze according to user profile, such as the differences in age preferences for categories.
Identification of lost customers: Don’t wait until you leave before you remember to stay
Customer churn is a big problem for many businesses. SQL can help you identify those who are "quickly" in advance. For example, you can find old customers who have not placed orders in the past three months:
SELECT customer_id FROM orders GROUP BY customer_id HAVING MAX(order_date) < '2024-10-01' AND COUNT(order_id) > 3 -- At least a few orders have been placed, not once
This type of customer was once active, but now it doesn’t come often. It may be because of a bad experience or being attracted by competitors. At this time, sending a coupon and doing a follow-up visit is more cost-effective than waiting for them to leave completely before recruiting new products.
Basically that's it. SQL is not used to create complex models, but it is very direct to answering common customer behavior questions. The key is to sort out the logic and understand the data structure. The rest is to write a few conditions and aggregate functions.
The above is the detailed content of SQL Analytics for Customer Behavior Insights. For more information, please follow other related articles on the PHP Chinese website!
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