Customer Spending Analysis with BigQuery

In today's business landscape understanding your customers' spending habits is crucial. It allows you to:

• Identify your most valuable customers: Knowing who spends the most helps you tailor marketing efforts and loyalty programs to retain them.

• Develop targeted promotions: Analyze spending patterns to create personalized offers that resonate with different customer segments.

• Predict future trends: By understanding historical spending patterns, you can anticipate future buying behaviors and adjust your inventory or product offerings accordingly.

BigQuery, a powerful data analysis tool from Google Cloud, empowers you to unlock these insights from your customer data. BigQuery handles large datasets efficiently, making it ideal for analyzing vast customer purchase information. This combination of understanding spending habits and BigQuery's capabilities allows you to make data-driven decisions that can significantly impact your business growth.

STEP 1: Identifying Your Best Customers

Imagine you want to identify your most loyal customers who consistently spend the most money with your business. BigQuery can help you achieve this by analyzing your user purchase data.

We'll assume you have two tables:

Users: This table stores information about your users, including their ID, name, and email address.

CREATE TABLE users ( 
	id SERIAL PRIMARY KEY, 
	name VARCHAR(255) NOT NULL, 
	email VARCHAR(255) NOT NULL UNIQUE 
);

Orders: This table stores details about each placed order, including the user ID who placed the order, the order date, the shipping destination, the product model number, and the price.

CREATE TABLE orders ( 
	id SERIAL PRIMARY KEY, 
	user_id INTEGER REFERENCES users (id) ON DELETE CASCADE, 
	date TIMESTAMP NOT NULL, 
	shipping_destination VARCHAR(255) NOT NULL, 
	model_number VARCHAR(255) NOT NULL, 
	price DECIMAL(10,2) NOT NULL 
);

STEP 2: Building the BigQuery Query

BigQuery allows you to write powerful SQL queries to analyze your data. In this case, we'll use a Common Table Expression (CTE) to simplify our query. A CTE is a temporary named result set that you can reference within your main query.

Here is BigQuery to identify your top spenders:

WITH UserOrders AS ( 
	SELECT u.id AS user_id, u.name, u.email, SUM(o.price) AS total_spent 
	FROM users u 
	INNER JOIN orders o ON u.id = o.user_id 
	GROUP BY u.id, u.name, u.email 
) 

In the above query,

1. INNER JOIN to connect the users and orders tables based on the user_id field. This ensures we only consider orders placed by valid users in our system.

2. SUM function to add up the price for each user's orders. This gives us the total amount each user has spent.

3. Finally, the user_id, name, email, and the calculated total_spent for each user. This creates our UserOrders CTE.

STEP 3: Retrieving the Top 10 Users

Now that we have the UserOrders CTE containing the total spending information for each user, we can retrieve the top 10 spenders. The main query will:

SELECT user_id, name, email, total_spent 
FROM UserOrders 
ORDER BY total_spent DESC 
LIMIT 10;

In the above query:

• FROM UserOrders clause to access the data prepared in our CTE.

• ORDER BY total_spent DESC clause to sort the results in descending order based on the total spent amount. This ensures the users who spent the most appear first.

• Finally, the LIMIT 10 clause restricts the output to only the top 10 users in the sorted list.

Result -

Benefits and Use Cases

BigQuery offers a powerful solution for analyzing customer spending habits. Here's a quick recap of its benefits:

• Effortless Handling of Large Datasets: BigQuery efficiently processes massive amounts of customer purchase data, allowing you to gain insights from your entire customer base.

• Flexible Data Exploration: Its SQL-based interface lets you ask complex questions about your customer spending patterns.

• Actionable Insights: You can make informed business decisions by identifying top spenders and understanding overall spending trends.

This report on top spenders exemplifies the power of BigQuery. The information it provides can be used for various purposes, such as:

• Targeted Marketing Campaigns: Tailor marketing efforts towards your high-value customers with personalized offers and promotions that are more likely to convert.

• Loyalty Programs: Reward your top spenders with exclusive benefits and incentives to show appreciation and encourage continued loyalty.

• Inventory Management: Analyze spending patterns to optimize inventory levels and product offerings based on customer demand.

Conclusion

By leveraging BigQuery for customer spending analysis, you gain a deeper understanding of your customer base, enabling you to make data-driven decisions that drive sales and customer satisfaction.