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How to Calculate Customer Lifetime Value in PostgreSQL

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.

How to Calculate Customer Lifetime Value

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:

simplified customer lifetime value equation = average customer lifespan * average amount spent per visit * average number of visits per year

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:

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.

How to Calculate Metric in PostgreSQL

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.

What is Lido?

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.

Sign up for free and get started today.

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