In Part 1 of this series, I discussed the business logic and conceptual elements of LTV. In Part 2, I'll be taking a deep dive on the LTV formula and explain how Zuora Insights does all the work of collecting, calculating and maintaining LTV.
III. The LTV Formula
Now that we’ve explained the four elements that go into LTV, you should have a clear conceptual understanding of LTV. These concepts should help you evaluate any proposed LTV formulas you may come across to understand what it includes and what it lacks. For example, if you see no variables for discounting, you know it is prone to overestimate and your caution flag should be raised.
Let’s cover the actual mathematical formula Zuora recommends you use in calculating LTV. Fortunately, for companies running subscription business, the formula only involves simple arithmetic. The formula:
Customer Lifetime Value ($) = Current Recurring Revenue ($) x Gross Profit Margin x Account Retention Rate / (1 + Discount Rate - Net MRR Retention)
The variables in the formula are color-coded to highlight each of the four elements discussed in part II of this paper:
Let’s imagine you operate a B2C monthly subscription business with the following metrics:
Monthly Recurring Revenue = $9.99
Gross Profit Margin = 80%
Monthly Customer Account Retention Rate = 75%
Discount Rate = 5%
Net MRR Retention Rate = 85%
LTV would be calculated as:
Customer Lifetime Value ($) = $9.99 x 0.8 x 0.75 / (1 + 0.05 - 0.85)
= $5.99 / 0.2
Over the course of 4 months (the life expectancy calculated using 1 / Churn Rate or 1/0.25), you can expect $29.97 in profit from the customer.
There are a handful of alternate formulas that analysts use to calculate LTV. One of the most common alternative formulas is the one listed below.
LTV = $ MRR / Churn Rate
Such a formula lacks two of the critical elements: Cost Expectancy and Risk Expectancy. By now you should know looking at this formula to use it at your own caution. While it may offer value in doing a relative future revenue comparison across your accounts, it would not have value as the basis of spending decisions because it aggressively overestimates the actual future value of one of your customers.*
* Note: This formula actually also has a third mistake, which is that it assumes an instant payment before the first opportunity to churn. You have to look at the mathematical derivation to catch that. The formula is exactly equal to the recommended formula with 0% discount rate + one period recurring revenue.
Another concept that I hear when talking about the LTV metric with some companies is that they calculate it as a customer’s net payments lifetime-to-date, or the sum of a customer’s net payments lifetime-to-date plus the LTV metrics described in this paper. These are all interesting and valid ways to look at and segment your customers -- especially from the perspective of developing customer loyalty programs based on rewarding customers for a steady financial relationship with your. That said, such a lens would offer misguided help on estimating the future value of your customers.
IV. Keeping LTV Fresh
Assembling the data inputs and estimates required for calculating the LTVs of your subscribers, and then keeping them up-to-date, is a real challenge. Subscription bookings data is sensitive and can be difficult for managers, and others who need to know, to get. Developing a churn prediction model that reliably estimates churn rates based on all the factors that are indicators of churn in your business -- including subscriber behavior patterns, business demographics, pricing and packaging variables -- requires advanced statistical techniques. And then finally, as if those challenges weren’t enough, keeping your calculation running and fresh every day, and published in format easy for your business to consume -- ideally a shareable KPI dashboard -- requires technology. In short, providing the decision-makers in your business with current LTV for each of your customers is a challenge. Fortunately, technology options are maturing and you don’t have to try to calculate LTV in a spreadsheet, or on a one-off basis to justify a project or pick between a set of projects. There are now a host of analytics tools cropping up designed to help subscription businesses track LTV, among other key indicators of the health of your subscription business, like MRR, Net Retention, Customer Churn Rate, and ARPA.
At Zuora, for customers interested in statistics like LTV and who want to take their subscription business analytics to a more robust level, we often recommend that they look into Zuora Insights -- Zuora’s own subscription analytics product. Inside of Zuora Insights, LTV is a standard statistic and is one of the more desirable metrics that our customers can track about individual subscribers. Zuora Insights does all the work of collecting, calculating, and maintaining the statistic and puts it right at our customer’s fingertips.
To calculate LTV, Zuora Insights uses the recommended formula described in part two of this paper covering each of the four elements. Importantly, a statistical model running inside of Zuora Insights provides individualized life expectancy estimates for each of your customers based on all of your subscription business’s historical renewals and churns that are tracked in Zuora. Customers using Zuora Insights also typically stream in subscriber behavior patterns and demographic information from their CRMs and product databases further enhancing the life expectancy element.
With up-to-date and reliable LTV for each of your subscribers, Zuora Insights lets you explore the average LTV of particular groups of your customers based on common characteristics using a feature calls Segments. Imagine you want to know the average LTV of subscribers with a particular subscription plan and acquisition channel.
Zuora Insights lets you find those customers using Segment Builder. All of the subscribers that fit the criteria are returned in a few seconds and for each matching account, you can see its exact LTV along with account owner, contract end date, product name, recurring revenue, or any countless dozens of other measures being tracked by Zuora.
Customers using Zuora Insights also have the added benefit of being able to save Segments and then study and compare average LTV statistics across customer segments using Segment Dashboards.
Here, we are looking at the trend in average LTV over the last 30 days for the Segment that we had just defined. Technology keeps getting better and better to make fact-based decisions about your subscribers.
In closing, if you’re not already doing it, start calculating LTV for your customers. Make it visible on the dashboards that your teams use. Encourage everyone to start making decisions and setting goals with LTV in mind.
I hope you found this helpful and best of luck growing your subscription business!
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