How to Track Customer Lifetime Value (CLV) on Shopify
CLV is average order value times purchase frequency times customer lifespan, all from Shopify's reports or a customer export. For a truer figure, group customers by first-order month and follow what each group spends.
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By Joris van Huët, Founder & CEOUpdated 7 min read
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How to Track Customer Lifetime Value (CLV) on Shopify
Quick Answer: Tracking customer lifetime value (CLV) on Shopify involves using native analytics, integrating third party apps, or employing custom spreadsheet calculations to understand the long term profitability of your customer base, providing important insights for strategic growth and resource allocation.
Understanding customer lifetime value (CLV) is not merely a metric; it is a foundational principle for sustainable growth in direct to consumer (DTC) ecommerce. For Shopify merchants, particularly those operating in competitive markets like Europe or the Netherlands, a precise grasp of CLV is paramount. It shifts the focus from transactional gains to enduring customer relationships, enabling more informed decisions regarding marketing spend, product development, and customer retention strategies. This article will meticulously detail how to track customer lifetime value on Shopify, from basic methods to more advanced analytical approaches, ensuring you gain a complete understanding of this vital metric.
The Significance of Customer Lifetime Value for Shopify Brands
Before looking at the how, it is important to reiterate the why. Customer lifetime value represents the total revenue a business can reasonably expect from a single customer account throughout their relationship with the company. A higher CLV means your customer acquisition costs (CAC) become more justifiable, allowing for greater investment in marketing and customer experience. It also highlights the efficacy of your retention efforts, indicating whether your products and services resonate deeply enough to foster repeat purchases.
Consider a beauty brand on Shopify. If their average order value (AOV) is €50, but their CLV for a segment of customers is €300 over two years, it implies those customers are making six purchases. This insight allows the brand to invest more in acquiring similar customers or in nurturing existing ones to reach that €300 threshold. Without CLV, the brand might mistakenly view a €60 CAC as unsustainable, when in reality, it could be highly profitable. This strategic perspective is what differentiates thriving brands from those struggling with churn and inefficient marketing.
Stage 1: Practical Methods for Tracking CLV on Shopify
Tracking customer lifetime value on Shopify can be approached through several methods, each offering varying degrees of complexity and insight.
1. using Shopify's Native Analytics for CLV Components
Shopify's built in analytics dashboard provides a wealth of data that, while not directly presenting a CLV figure, offers the essential components needed to calculate it.
Key Metrics from Shopify Analytics:
Average Order Value (AOV): Found under "Reports" > "Sales" > "Sales by product". This is the average amount spent per order.
Purchase Frequency: Also available in "Reports" > "Customers" > "Customers over time". You can segment customers to see how often they purchase.
Customer Retention Rate: While not explicitly a single metric, you can infer this by tracking repeat customer rates over specific periods in the "Customers" section.
Average Customer Lifespan: This requires a bit more manual analysis. By observing customer cohorts over time, you can estimate how long customers typically remain active.
Calculating Basic CLV with Shopify Data:
A simplified CLV formula is:
CLV = Average Order Value x Purchase Frequency x Average Customer Lifespan
Purchase Frequency: Over a year, an average customer makes 2.5 purchases.
Average Customer Lifespan: Based on historical data, you estimate customers remain active for 3 years.
Using these figures:
CLV = €75 x 2.5 x 3 = €562.50
This calculation provides a foundational understanding. While basic, it is a significant step beyond simply looking at individual transaction values. The limitation here is that "Average Customer Lifespan" is often an estimate and does not account for changes in customer behavior over time.
2. Using Third Party Shopify Apps for Automated CLV Tracking
For a more automated and granular approach, numerous Shopify apps specialize in CLV calculation and customer segmentation.
Popular CLV Apps for Shopify:
Lifetimely: This app provides detailed CLV metrics, cohort analysis, and profit reporting. It helps identify your most valuable customers and segments.
Repeat Customer Insights: Focuses on repeat purchases and customer segmentation, offering insights into customer retention and loyalty.
Retention.com: While broader in scope, it offers tools that contribute to understanding and improving CLV by identifying anonymous website visitors and converting them into customers.
Benefits of Third Party Apps:
Automation: Reduces manual data extraction and calculation.
Sophisticated Models: Many apps use more advanced predictive models for CLV, offering forward looking insights.
Segmentation: Allows for CLV analysis by customer segment, product category, or acquisition channel. This is important for targeted marketing.
Findings: Often provide recommendations based on CLV data, such as identifying at risk customers or high value segments.
When choosing an app, consider its pricing model, integration capabilities, and the depth of its analytical features. Ensure it aligns with your brand's specific needs and budget.
3. Custom Spreadsheet Calculations for Advanced CLV Analysis
For brands with specific analytical requirements or those who prefer full control over their data, custom spreadsheet calculations using exported Shopify data can offer the deepest insights. This method requires a solid understanding of data manipulation and statistical concepts.
Steps for Spreadsheet Based CLV Tracking:
Export Customer Data: From your Shopify admin, navigate to "Customers" and export your entire customer list. Include order history, dates, and total spend.
Clean and Organize Data: Use spreadsheet software like Google Sheets or Excel to clean the data. Ensure consistent formatting for dates and currency.
Calculate Individual Customer Metrics:
- Total Revenue per Customer: Sum all order totals for each unique customer ID.
- Number of Orders per Customer: Count the orders for each customer.
- First and Last Purchase Dates: Determine the span of their activity.
- Average Order Value per Customer: Total revenue divided by number of orders.
Implement a Predictive CLV Model:
CLV = (Average Order Value per Customer x Average Purchase Frequency per Customer) / Churn Rate
The churn rate can be estimated by observing the percentage of customers who do not make a repeat purchase within a certain timeframe.
- Probabilistic Models (e.g., BG/NBD, Gamma-Gamma): These are more advanced statistical models that predict future purchasing behavior based on past transactions. They require specialized statistical software or advanced spreadsheet functions. While beyond the scope of a simple guide, understanding their existence is important for brands seeking highly accurate predictions.
Example Spreadsheet Calculation for a Cohort:
| Customer ID | Acquisition Date | Total Orders | Total Spend (€) | Last Order Date |
|---|---|---|---|---|
| C001 | 2023-01-15 | 3 | 180 | 2024-03-20 |
| C002 | 2023-01-22 | 1 | 60 | 2023-01-22 |
| C003 | 2023-01-28 | 5 | 300 | 2024-05-10 |
| C004 | 2023-01-30 | 2 | 120 | 2023-08-15 |
Average Total Spend per Customer (for this cohort): (€180 + €60 + €300 + €120) / 4 = €165
Average Orders per Customer: (3 + 1 + 5 + 2) / 4 = 2.75
Average Customer Lifespan (for active customers): (429 days for C001 + 467 days for C003 + 228 days for C004) / 3 = 374 days (approx 1 year).
This cohort based analysis provides a more realistic picture of CLV trends over time
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Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Churn RateChurn Rate measures the number of individuals or items moving out of a collective group over a specific period. It is a key metric for subscription-based businesses and the opposite of customer retention.
- Customer acquisitionCustomer acquisition attracts new customers to a business. For e-commerce, this means driving the right traffic to the website.
- Customer ExperienceCustomer Experience is the overall perception customers form from all interactions with a company.
- Customer Lifetime Value (CLV)Customer Lifetime Value (CLV) predicts the net profit from a customer's entire future relationship. It identifies valuable customers and directs marketing spend toward long-term profitability.
- Purchase FrequencyPurchase frequency measures how often customers buy from a business. It is a key metric for understanding customer behavior and lifetime value.
- SegmentationSegmentation divides a broad market into sub-groups based on shared characteristics. This allows for more targeted marketing campaigns.