Customer Stories in Data: Measuring Lifetime Value with SQL

Every customer has a story, and together, these stories shape the trajectory of your business. Understanding these stories—why some customers stay, why others leave, and how they contribute to your growth—is the foundation of a customer-centric strategy. At the heart of this understanding lies Customer Lifetime Value (LTV), a metric that quantifies not just what customers are worth today, but what they can bring to your business over time.

LTV is more than a calculation—it’s a lens through which businesses can identify opportunities, craft retention strategies, and build loyalty. By using SQL, you can bridge the gap between raw data and actionable insights, turning customer behavior into a roadmap for long-term success.

What Is Customer Lifetime Value?

Customer LTV measures the total revenue a customer is expected to generate over the course of their relationship with your business. It answers key questions like:

• How much is the average customer worth?
• Which customers are high-value outliers, and why?
• Where are the opportunities to improve retention or engagement?

LTV helps us tell two complementary stories:

1. The Baseline: An average view of how customers contribute to your business.
2. The Outliers: A deeper look at who exceeds or falls short of expectations—and what behaviors drive those trends.

The first story helps you forecast revenue and set benchmarks for acquisition costs. The second provides the foundation for action, helping you uncover opportunities to enhance loyalty and address customer pain points.

A Customer-Centric Approach to Measuring LTV

Imagine running an e-commerce business and wondering what makes a customer truly valuable. For some, it’s repeat purchases that compound over time. For others, it might be a single high-value order. To understand these differences, you start by grouping customers into cohorts based on their first interaction with your business and tracking their behavior over time.

Step 1: Preparing the Data

CREATE TABLE orders (
    order_id INT,
    customer_id INT,
    order_date DATE,
    revenue DECIMAL(10, 2)
);

-- Example data
INSERT INTO orders VALUES
(1, 101, '2023-01-15', 100.00),
(2, 102, '2023-01-18', 50.00),
(3, 101, '2023-02-20', 120.00),
(4, 103, '2023-03-10', 75.00),
(5, 102, '2023-03-15', 60.00);        

This data captures the essence of your customers’ interactions—how often they engage, how much they spend, and when they return.

Step 2: Segmenting Customers into Cohorts

Cohorts group customers by their first interaction, providing insights into their long-term behavior. For example, a customer who joined in January 2023 might behave differently than one who joined six months later.

CREATE TEMP TABLE customer_cohorts AS
SELECT
    customer_id,
    MIN(DATE_TRUNC('month', order_date)) AS cohort_month
FROM orders
GROUP BY customer_id;        

This step establishes the starting point of each customer’s journey.

Step 3: Tracking Cohort Revenue Over Time

Next, aggregate revenue by cohort and month to see how spending evolves over time:

CREATE TEMP TABLE cohort_revenue AS
SELECT
    c.cohort_month,
    DATE_TRUNC('month', o.order_date) AS revenue_month,
    SUM(o.revenue) AS total_revenue,
    COUNT(DISTINCT o.customer_id) AS unique_customers
FROM orders o
JOIN customer_cohorts c
ON o.customer_id = c.customer_id
GROUP BY c.cohort_month, DATE_TRUNC('month', o.order_date);        

This reveals how each cohort contributes to revenue growth month by month.

Step 4: Calculating Cumulative LTV

Finally, calculate cumulative revenue and LTV for each cohort:

SELECT
    cohort_month,
    revenue_month,
    SUM(total_revenue) OVER (PARTITION BY cohort_month ORDER BY revenue_month) AS cumulative_revenue,
    SUM(total_revenue) OVER (PARTITION BY cohort_month ORDER BY revenue_month) /
    SUM(unique_customers) OVER (PARTITION BY cohort_month) AS cumulative_ltv
FROM cohort_revenue
ORDER BY cohort_month, revenue_month;        

Cumulative LTV provides a snapshot of how much value each cohort generates over time, revealing trends and benchmarks for growth.

Turning Data into Strategy:

Imagine uncovering that your January 2023 cohort generates 50% more revenue than any other. What behaviors set them apart? Are they more engaged? Are they responding to specific promotions? These insights guide actions to replicate success in other cohorts.

Conversely, if a cohort struggles to contribute, you can investigate why. Are they churning early? Ignoring promotions? LTV provides the tools to ask these questions and find actionable answers.

However, every analysis has its boundaries. LTV insights are most valuable when paired with context:

• Incomplete Data: Missing purchase history or multi-channel activity can distort results.
• Static Assumptions: Failing to account for seasonality or lifecycle changes can lead to oversimplified conclusions.
• Short Timeframes: New products or businesses may lack the historical depth needed for accurate predictions.

Acknowledging these limitations allows you to view LTV as one piece of a broader strategic puzzle.

Why SQL is the Right Tool for LTV Analysis

SQL brings LTV analysis within reach through an intuitive language, combining scalability with accessibility. With SQL, you can:

• Segment customers into cohorts to analyze trends over time.
• Use window functions to calculate cumulative metrics efficiently.
• Turn raw data into insights that seamlessly integrate with operational workflows.

SQL isn’t just about retrieving data; it’s about empowering action.

Building a Customer-Centric Future

LTV is more than a number—it’s a window into the relationships you build with your customers. By identifying high-value customers, addressing the needs of low performers, and crafting targeted strategies, you can strengthen loyalty, improve retention, and drive sustainable growth.

The insights LTV provides aren’t just about maximizing revenue. They’re about creating a customer experience that fosters connection and builds trust. Every customer story matters, and with LTV as your guide, you can make every story a success.

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The views and opinions expressed in this post are my own and do not reflect the views or positions of Amazon or any other organization I am affiliated with.