Learn how to calculate Customer Lifetime Value (CLV) with profit margins, retention rate, and more. Use LTV on Stripe and Shopify accounts.

Optimizing Customer Lifetime Value is one of the best ways to increase your revenue and overall profitability. By definition, Customer Lifetime Value (CLTV) is “the total amount of money a customer is expected to spend on your products during their lifetime” (thanks, Shopify!). More specifically, we like to look at CLTV as the tool to best identify your most important consumer segments. While the metric seems simple on the surface, it gives valuable insights into areas of future growth and improvement. We'll take you through how to calculate this metric, how to understand it in the context of your industry, and how to apply it to the platforms you're using now.

There are various ways to calculate LTV (here’s 5 right off the bat!). We’ll be going over two popular methods, what we’ve denoted as: traditional and simplified. **First, you can calculate LTV by multiplying yearly profit margin per customer by yearly retention rate, divided by 1 plus the discount rate minus the yearly retention rate. This is the traditional method. **The components of LTV can be derived from various time-periods (i.e. day, week, month, year) as long as they’re converted to year units in your calculations (i.e. you can multiple weekly profit margin per customer by 52 to get your yearly profit margin.) If you’re confused about how to define yearly profit margin per customer, yearly retention rate, and the discount rate, we’ve included some nifty explanations below:

**Yearly profit margin per customer**is your profit margin (revenue minus expenses) divided by total number of customers for the year. This helps estimate how much you make on an average customer per year.**Yearly retention rate**is the number of customers at the end of year minus the number of customers acquired throughout the year, all divided by the number of customers at the beginning of the year. Check out Hubspot’s guide on customer retention for more information.******Discount rate**is the interest rate, used to bring your LTV to present value.

**You can also calculate LTV by taking the product of average customer lifespan, average amount spent per visit, and average number of visits per year.** Once again, components of LTV can be derived from various time-periods as long as they’re converted to year units in your calculations. If you’re confused about how to define average customer lifespan, average amount spent per visit, and average number of visits per year, we’ve included some nifty explanations below:

**Average customer lifespan**is the amount of time, on average, between a customer’s first and final order. Often times, it is difficult to get an exact value for this, especially if your company is just starting out. We recommend to use 1-3 years, as per Shopify’s eCommerce averages.**Average amount spent per visit**is how much, on average, a customer spends per purchase. Find this by taking your total revenue divided by total number of purchases for a given year.**Average number of visits per year**is how much, on average, a customer visits your shop per year. Find this by taking your total number of purchases divided by total amount of customers for a given year.

When choosing what to include in your calculations, remember: consistency is key! That means, if you use the traditional method to calculate LTV based on this year’s numbers, you should probably use the traditional method for future updates to the figure. **Having consistent metrics will allow you to better identify the causes of good or bad performance.** For instance, if, over the past few months, you’ve increased benefits for your loyalty programs and LTV also increases, then you’ll know that loyalty program was effective in improving purchase frequency.

**It can be difficult to calculate this metric in PostgreSQL, but we have a quick and easy solution, Lido.app. Read below to learn more and get started today.**

**Lido allows you to connect, analyze, and visualize all of your data in a single spreadsheet. Don't wait for engineers to build analysis dashboards! Lido provides a simple and easy solution to importing data from numerous platforms. Automatically import data from your favorite platforms such as Shopify, Facebook, Google Analytics, and many more and apply Lido's software to extract meaningful metrics from them. After applying Lido software to your data, you will be left with sleek, attractive dashboards to share with your stakeholders, rather than confusing and jumbled raw data. Furthermore, the dashboards are easily editable to focus on specific data or metrics. **

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