Tech & Data Diary - Entry #017: Calculating Customer Lifetime Value Using BigQuery

Customer Lifetime Value (CLV) is a crucial metric for businesses, as it helps determine the total revenue a business can expect from a customer over the duration of their relationship. Understanding CLV allows businesses to optimize marketing strategies, allocate resources effectively, and improve customer retention efforts. In this article, we’ll explore how to calculate CLV using Google Analytics 4 (GA4) data in BigQuery, and how to segment users for targeted digital marketing campaigns.

Understanding CLV and Its Importance

Customer Lifetime Value (CLV) is the estimated net profit attributed to a customer throughout their entire relationship with a business. CLV takes into account the revenue generated from repeat purchases, the average order value, the purchase frequency, and the customer lifespan. By calculating CLV, businesses can:

• Optimize Marketing Spend: Identify high-value customers and allocate marketing resources to acquire and retain similar customers.
• Personalize Marketing Efforts: Tailor marketing messages based on customer value, increasing the relevance and effectiveness of campaigns.
• Improve Customer Retention: Identify at-risk customers and implement retention strategies to extend their lifespan.

Step 1: Setting Up GA4 and BigQuery Integration

Before calculating CLV, ensure that your GA4 property is integrated with BigQuery. This integration allows you to export GA4 event data to BigQuery for advanced analysis. Here’s how to set it up:

1. Link GA4 to BigQuery:
2. Enable Data Streaming:

Step 2: Extracting Relevant Data from BigQuery

Once GA4 is linked to BigQuery, you can start extracting the necessary data to calculate CLV. The key data points needed include:

• User ID: A unique identifier for each user.
• Transaction ID: A unique identifier for each transaction.
• Transaction Revenue: The revenue generated from each transaction.
• Transaction Date: The date when the transaction occurred.

Here is a sample SQL query to extract this data from your BigQuery dataset:

SELECT user_pseudo_id AS user_id, event_bundle_sequence_id AS transaction_id, event_timestamp AS transaction_date, CAST((SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'value') AS FLOAT64) / 1000000 AS transaction_revenue FROM your-project.your-dataset.events_* WHERE event_name = 'purchase'This query selects the user ID, transaction ID, transaction date, and transaction revenue for all purchase events. Replace your-project.your-dataset with your actual project and dataset names.        

Step 3: Calculating CLV

With the data extracted, you can now calculate CLV. The basic formula for CLV is:

CLV = Average?Purchase?Value × Purchase?Frequency × Customer?Lifespan

Here’s how to calculate each component:

1. Average Purchase Value: The average amount spent by a customer per transaction.
2. Purchase Frequency: The average number of transactions made by a customer over a specific period.
3. Customer Lifespan: The average duration a customer remains active.

Here’s a sample SQL query to calculate CLV:

WITH purchase_data AS ( SELECT user_id, COUNT(DISTINCT transaction_id) AS total_purchases, SUM(transaction_revenue) AS total_revenue, MAX(transaction_date) - MIN(transaction_date) AS lifespan FROM your-project.your-dataset.events_* WHERE event_name = 'purchase' GROUP BY user_id ) SELECT user_id, total_revenue / total_purchases AS avg_purchase_value, total_purchases / COUNT(DISTINCT transaction_date) AS purchase_frequency, (total_revenue / total_purchases) * (total_purchases / COUNT(DISTINCT transaction_date)) * (lifespan / 86400000000) AS clv FROM purchase_data        

This query calculates the average purchase value, purchase frequency, and customer lifespan for each user, and then uses these values to compute the CLV.

Step 4: Segmenting Users for Targeted Marketing Campaigns

Once you have calculated CLV, you can segment your users based on their value. Segmentation allows you to tailor your marketing efforts to different customer groups, ensuring that high-value customers receive personalized and relevant messages, while lower-value customers are targeted with retention strategies.

Example Segmentation Strategy

Let’s create three segments based on CLV:

1. High-Value Customers (CLV > $500):
2. Medium-Value Customers (CLV between $100 and $500):
3. Low-Value Customers (CLV < $100):

Step 5: Creating a Segmentation Table in BigQuery

Here’s how you can create a table in BigQuery to segment users based on their CLV:

CREATE TABLE your-project.your-dataset.user_segments AS SELECT user_id, clv, CASE WHEN clv > 500 THEN 'High-Value' WHEN clv BETWEEN 100 AND 500 THEN 'Medium-Value' ELSE 'Low-Value' END AS segment FROM (SELECT user_id, (total_revenue / total_purchases) * (total_purchases / COUNT(DISTINCT transaction_date)) * (lifespan / 86400000000) AS clv FROM purchase_data)        

This query creates a new table user_segments that categorizes each user into one of three segments based on their CLV.

Conclusion

Calculating Customer Lifetime Value (CLV) using GA4 and BigQuery provides valuable insights into your customer base, allowing you to optimize marketing strategies and improve customer retention. By segmenting users based on their CLV, you can deliver targeted and personalized marketing campaigns that maximize ROI and foster long-term customer loyalty.

Understanding and leveraging CLV empowers businesses to make data-driven decisions, ensuring that every marketing dollar is spent effectively and that high-value customers are nurtured for continued growth.