Predicted LTV

Lots of customers means lots of revenue, right? But what happens if you spend more and more to acquire new customers, only to find that their quality is so low that they cost more than they contribute?

The Predicted LTV report shows how the quality of your customers evolves over time. By comparing cohorts, you can see if newer customers are expected to generate more or less profit over their lifetime than earlier cohorts. A declining predicted LTV may indicate that your marketing efforts are attracting lower-value customers as your brand scales.

LTV vs. Predicted LTV

Unfortunately, many tools calculate LTV incorrectly by relying on averages and estimates, which leads to several problems. Assuming that customers make a fixed number of purchases at an average order value ignores individual buying patterns. It doesn't take into account how factors such as the products purchased or demographics influence customer behavior. It also overlooks shifts in customer behavior over time or during seasonal periods.

Using historical, time-bound data, you can identify early trends in customer quality and gain insight into past performance. However, because it focuses on historical results, it does not predict a customer's future potential or long-term behavior. This is where predictive LTV comes in handy!

The predicted LTV is a forward-looking approach and predicts the future value of a customer, taking into account dynamic and individual behaviors such as retention, purchase frequency, and likelihood of repeat purchases.

How it Works

As soon as you add Forecast Plus to your plan, RetentionX predicts the LTV of customers who have not yet completed the lifetime under consideration. For example, a customer who placed their first order 7 months ago hasn't reached 1 year yet, RetentionX predicts their LTV after 1 year, 2 years and 5 years.

The predicted LTV based on RX Prediction™, a machine learning algorithm that identifies data twins in your customer base based on the following criteria:

• Purchase Frequency
• Product Preferences
• Average Order Value
• Return Behavior
• Order Quantity
• Demographics
• Customer Lifespan

This approach finds similar customers who have completed the lifecycle in question to those who haven't, helping to predict their future behavior. You'll find the predicted LTV under Customers > All Customers:

• The average LTV; for cohorts that have completed the lifetime in question, it’s based on actual data, while for those that haven’t yet completed it, the LTV is predicted.
• The deviation of the cohort's LTV from the average LTV across all customers.
• The cohort size, representing the number of customers analyzed.

Looking at the following example, 1,319 customers were acquired in January 2025. On average, the LTV of a customer in this cohort is predicted to be $687.22 after their first year, which is 15.2% lower than the average LTV 1 Year of all customers.

Use Cases

As you scale your marketing efforts, it's important to make sure you're not sacrificing customer quality for quantity. This analysis helps you evaluate whether your marketing efforts are bringing in high-value customers or whether you're attracting a less profitable customer base over time.

• Monitor predicted LTV over time to identify trends in customer behavior. If you see a drop in predicted LTV for new cohorts, adjust your strategies before it negatively impacts your overall performance.
• Identify which cohorts are producing the highest LTV and try to replicate the conditions that led to these results: prioritize spending on strategies that target similar customers.
• Identify cohorts that are lagging in quality and try to bring them up to healthy levels. Start your retention efforts before it's too late.

Not using RetentionX yet?

Read our full blog post about LTV analysis for Shopify stores here.