Customer lifetime value (CLV) analysis is the process of calculating the total net profit a business can expect to earn from a customer over the course of their entire relationship. By reviewing historical purchase data, you can identify which customer segments drive the most revenue over time and predict the future value of newly acquired customers.
Understanding these metrics is important for maintaining healthy unit economics as marketing costs rise. Alice Li, founder of the supplement brand First Day, says on an episode of the Shopify Masters podcast that setting strict targets for customer acquisition costs (CAC) and tracking them alongside CLV is essential to prevent unexpected losses. Failing to align these two figures can lead to a business model where the cost of winning a customer exceeds the revenue they will ever generate.
This article explains how to calculate customer lifetime value using the standard CLV formula, explores different models for predicting future revenue, and provides a step-by-step guide to conducting an analysis using Shopify’s built-in reporting tools.
What is customer lifetime value analysis?
Customer lifetime value, often referred to interchangeably as lifetime value, CLV, or LTV, is the total net profit a business brings in from a single customer over the course of their relationship with the business. Customer lifetime value analysis is the process of reviewing your historical data to understand how different customer segments interact with your brand over time. Rather than the average value of a single order, it’s about predicting the future cash flows a customer will generate.
Customer lifetime value analysis helps you identify your most valuable customers and understand why they stay, spend, or churn. This helps you focus your marketing efforts on certain customer segments that offer the highest long-term value.
Knowing how much your valuable customers are worth also dictates how much you can afford to spend on customer acquisition. If your customer acquisition costs are higher than your customer lifetime value, your business model is fundamentally unsustainable.
Customer lifetime value formula
Although there are complex variations, the basic customer lifetime value formula is:
CLV = (Average purchase value x Purchase frequency) x Average customer lifespan
To calculate CLV accurately, you first need to determine the following:
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Average purchase value. The number of dollars a customer spends per visit.
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Purchase frequency. The number of times an average customer buys from you within a year.
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Average customer lifespan. The average number of years a customer stays engaged.
First, multiply the average purchase value by the purchase frequency. Then, multiply this total by the average customer lifespan to get a clear picture of the customer value. For example, imagine you run an online specialty coffee brand. To determine your CLV, you first look at your historical data for a typical customer:
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Average purchase value. Each time a customer orders, they spend $35 on a bag of beans.
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Purchase frequency. On average, your customer orders once per month, or 12 times per year.
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Average customer lifespan. Your data shows that the average customer stays with your brand for three years before churning.
Now, use the customer lifetime value formula:
1. First, calculate the annual value: $35 (value) x 12 (frequency) = $420 per year
2. Next, multiply by the lifespan: $420 x 3 (years) = $1,260
In this scenario, your customer lifetime value is $1,260. Your aim is to keep customer acquisition costs significantly lower than this number to ensure a healthy net profit.
Types of CLV models
Businesses use different methods to measure the future value of their customers. Depending on your business growth stage and the complexity of your customer data, you might choose one of three primary models.
Simple CLV
The basic customer lifetime model is ideal for businesses with stable, predictable sales. It uses historical business data to calculate a flat average of what a typical customer spends over their entire relationship with the brand. Although it’s easy to calculate customer lifetime value this way, it doesn’t account for shifts in customer behavior or market volatility. It also assumes the future will look exactly like the past. Here are some scenarios in which you might use simple CLV:
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Early-stage startups. When you lack the deep data pools required for complex modeling.
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Consistent retention. If your customer churn is low and your product is a daily or weekly necessity.
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Quick benchmarking. When you need a fast figure to present to stakeholders or to set an initial baseline for customer acquisition costs.
Gross margin CLV
For a more granular look at customer profitability, use the gross margin CLV. Rather than looking at top-line revenue, this model subtracts the cost of goods sold (COGS), shipping, and customer acquisition costs from the total. This reveals the actual net profit left over, or the money you can actually use to pay rent or reinvest in business growth.
Divy Ojha, founder of the produce delivery service Odd Bunch, says on Shopify Masters that he tracks customer lifetime value along with gross margin. If you only look at revenue, you might overlook the fact that your most valuable customers are the ones purchasing high-margin items, rather than those just spending the most dollars.
Here are some scenarios in which you might use the gross margin CLV model:
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Variable margins. If you sell a mix of low-margin items and high-margin luxury items.
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High fulfillment costs. When shipping and logistics significantly eat into your average revenue.
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Sustainability focus. When your goal is to move beyond growth at all costs and ensure every existing customer is contributing to the brand’s long-term customer equity.
Predictive CLV
Predictive CLV is the most advanced model, using AI-powered predictions to forecast future revenue. Unlike historical models that only look backward, predictive CLV uses machine learning to analyze past customer journey patterns to guess which new customers will become loyal customers and which will churn.
Although plenty of stable, simple businesses use historical models, you could choose to pivot to a predictive model in the following scenarios:
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Scaling paid acquisition. If you are spending heavily on customer acquisition, you can’t wait years to see if a cohort is profitable. Predictive models identify high-value customers early, allowing you to optimize your marketing efforts in real time.
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Managing complex inventories. If you offer a wide range of products or multiple price plans, a simple average will skew your data. AI can forecast which customers are likely to level up from entry-level items to premium offerings.
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Predicting non-linear customer behavior. In industries like fashion or wellness, customers don’t always buy on a fixed schedule. Predictive models account for seasonal spikes and varying purchase frequency better than a flat historical average.
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Operating at scale. Once you have a high volume of customer data, AI can spot subtle “microbehaviors,” such as a specific sequence of page views or a quick second purchase, that could signal a high long-term value before the customer even reaches their second month of engagement.
How to conduct customer lifetime value analysis
- Calculate your baseline CLV and establish benchmarks
- Gather quantitative and qualitative behavioral data
- Analyze patterns and drivers of high vs. low CLV
- Segment CLV to uncover actionable insights
Performing a customer lifetime value analysis is a sequential process. It requires moving from raw business data to actionable insights that can improve customer lifetime metrics across your entire customer base.
1. Calculate your baseline CLV and establish benchmarks
To increase customer lifetime value, first know where you stand. Use the customer lifetime value formula to find your current average. Look at your average order value and purchase frequency over the past 12 months. This baseline acts as your North Star for all future marketing efforts.
If you’re on the Shopify platform and want to pull this data quickly, look at your Shopify customers reports. Specifically, look at the customer cohort analysis report. It tracks customer acquisition, retention, and spending over time, showing you exactly how revenue trends fluctuate based on when a customer first joined your ecosystem.
2. Gather quantitative and qualitative behavioral data
For quantitative data, Shopify’s RFM analysis automatically analyzes customer behavior based on recency, frequency, and monetary value, categorizing your entire customer base into 11 segments like “champions,” “loyal,” or “at risk.”
Qualitatively, the best data often comes from early engagement patterns. Divy found that, for Odd Bunch, the most predictive metric for long-term value was customer engagement via referrals in the first four weeks. Customers who referred a friend early on had a significantly higher customer retention rate six months later. To track this, Divy used loyalty and referral tools like Smile and Referral Candy that monitor those early behavior drivers.
3. Analyze patterns and drivers of high vs. low CLV
Analyze the entire customer lifetime cycle to determine why some customers stay while others leave. More specifically, examine your predicted spend tier and your product quality.
Shopify’s AI-powered predicted spend tier helps you categorize customers into high, medium, and low tiers based on their predicted spending potential. This lets you look for commonalities and examine whether the high-tier customers are buying a specific product category.
Ellen Bennett, founder of the apron brand Hedley & Bennett, argues that customer retention flows entirely from product quality. In other words, your customers must believe that the product will perform as promised. “They have to trust you,” Ellen says on Shopify Masters. “[If] the quality’s bad, they’re never going to come back.” If your analysis shows a high customer churn after the first purchase, the driver may be the physical product experience rather than your marketing.
4. Segment CLV to uncover actionable insights
Don’t treat your customer base as a single entity. Instead, segment customers to understand the nuances of your revenue. Shopify’s customer segmentation tools allow you to build dynamic segments based on CLV metrics that auto-update as behavior changes. For example, you can:
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Find high-value audiences. Grace Lee Chen, founder of the bridesmaid dress brand Birdy Grey, discovered her most valuable customers were bridal parties rather than individuals. She was then able to spend her marketing budget more efficiently after identifying this segment.
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Expand through categories. Sean Frank, CEO of the accessories brand Ridge, used customer segmentation data to identify what else his loyal customers might need. Then he expanded into phone cases, luggage, and rings to increase customer lifetime value.
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Target the decision makers. Alice from First Day identified household purchasers as a high-value segment. By focusing marketing efforts on this group, she expanded the brand’s share of wallet (SOW) as these customers began purchasing for their whole families.
Customer lifetime value analysis FAQ
How do you analyze customer lifetime value?
Start by gathering customer data and using Shopify’s RFM (recency, frequency, and monetary value) analysis to determine which customer segments are your most loyal. Segment customers by acquisition channel and product type to see which variables impact customer lifetime the most.
How do I calculate customer lifetime value?
The customer lifetime value formula is: (Average purchase value x Purchase frequency) x Average customer lifespan. Multiply your average purchase value dollars by the average number of times a customer buys within a year. Multiply this total by the average number of years a customer remains engaged before churning. To find customer profitability, subtract your customer acquisition costs and COGS to find the net profit per customer.
Is customer lifetime value LTV or CLV?
They generally mean the same thing. LTV stands for lifetime value, while CLV stands for customer lifetime value. Both refer to the total dollar amount a customer brings to your business over the course of their entire relationship with your brand.
