Lifetime value is one of the most important metrics for subscription-based/SaaS products. It’s a basis for revenue forecasting and a proxy for the value we deliver to our premium users.
Yet, it’s also one of the trickiest metrics to measure and properly interpret. For that reason, many companies focus on more straightforward metrics, such as conversion rates, to evaluate the performance of their experiments. But this comes with its own set of challenges.
Let’s demystify the lifetime value (LTV) and see how you can use it to make better product decisions.
Subscription conversion rate (CVR) is one of the primary metrics SaaS product managers track. But it’s also one of the most problematic ones.
First things first: CVR is an outcome of many other volatile metrics. For example, if your users tend to convert after ~10 days and you experience two weeks of low new traffic, then your CVR would skyrocket (lots of new paying users that joined 10 days ago and finally converted versus a low overall number of users). But that wouldn’t be a reason to celebrate, would it?
Secondly, it doesn’t indicate how much you will actually earn. You could increase your CVR by lowering the price, but it could have a neutral or negative effect on revenue. Or, you could simply push users to premium more aggressively, resulting in higher churn down the road.
Although conversion rate is a good indicator of how compelling your value communication is, when it comes to revenue, it’s only one piece of the puzzle.
Lifetime value (or LTV for short) is an indicator of how much revenue, on average, you can expect from a given type of user during their customer lifetime (as a paying user, from subscription, start to churn).
To calculate the LTV of a single user, multiply the price they pay for a subscription by the number of times they renew that subscription:
LTV = Plan price * Number of renewals
Let’s look at an example to show how the LTV formula works. If you start a Netflix subscription for $30/month and then decide that the service is not for you after all, then:
$30 * 4 renewals = $120
This means you generated a total of $120 in revenue for Netflix during your lifetime as a paying customer.
Now, let’s see an example of how to calculate and project lifetime value for SaaS products at scale. After all, you won’t be calculating the individual LTV of every user you have.
We’ll demonstrate two ways to calculate and project LTV for subscription-based products:
For feasibility purposes, most companies look at 12/13 months ahead when projecting LTV — meaning they seek to answer the question of how much revenue they can get from a particular type of customer within the next 12/13 months. It’s more accurate than looking indefinitely into the future.
As a rule of thumb, use 12 months if you offer only monthly subscriptions and 13 months if you also have yearly plans. This is so you can include your first renewals that happen at 13 months.
To analyze LTV at scale, we usually look at whole cohorts.
For example, let’s say that in January, you sold 100 monthly subscriptions. To assess how much revenue one customer brings you, divide the total revenue you get from the cohort by the starting number of subscriptions:
LTV = Total revenue / Starting number of subscriptions
Let’s look at a more detailed example. Say you need to analyze LTV for a cohort of 100 users that started a monthly subscription for $10 a month but churned month over month with a 12-month retention rate of 5 percent:
| Month | Subscriptions | Retention | Month revenue |
|
1 |
100 |
100% |
$1,000 |
|
2 |
80 |
80% |
$800 |
|
3 |
80 |
80% |
$800 |
|
4 |
70 |
70% |
$700 |
|
5 |
60 |
60% |
$600 |
|
6 |
50 |
50% |
$500 |
|
7 |
30 |
30% |
$300 |
|
8 |
30 |
30% |
$300 |
|
9 |
30 |
30% |
$300 |
|
10 |
20 |
20% |
$200 |
|
11 |
10 |
10% |
$100 |
|
12 |
5 |
5% |
$50 |
As you can see, each month, your total revenue from the cohort is getting lower due to users churning over time.
Now, to determine how much revenue one out of these 100 users brings to the business, divide the total revenue by the number of users in the cohort:
$5,650 / 100 = $56.50
Because, during the last 12 months, you captured a total of $5,650 in revenue from these 100 users, your LTV per user is $56.50. In other words, one user generated, on average, $56.50 revenue during the subscription lifetime (simplified to 12 months).
Now that you know what LTV is and how it’s calculated, let’s look at a few examples of how it can be used in practice to inform business decisions:
Probably the most obvious and common use case for LTV is projecting revenue and assessing how changes in conversion, acquisition, and retention rates can impact your total revenue.
For simplicity, let’s assume that your LTV is constant over the year. Now you can run a simulation of how things like:
…can impact your financial projections.
Although it’s still an estimation, at least it’s not a complete guess.
One of the most important use cases for subscription LTV is the ability to compare plans’ performance and the overall effectiveness of your plan mix.
For the sake of simplicity, let’s imagine you have only two subscription plans:
Since we’re considering yearly plans, we should take into account at least 13 months. Ootherwise, we won’t be able to compare these two plans.
Let’s now take a look at how these hypothetical plans perform.
Monthly plan:
| Month | Subs | Retention | Month revenue |
|
1 |
100 |
100% |
$1,000 |
|
2 |
80 |
80% |
$800 |
|
3 |
80 |
80% |
$800 |
|
4 |
70 |
70% |
$700 |
|
5 |
60 |
60% |
$600 |
|
6 |
50 |
50% |
$500 |
|
7 |
30 |
30% |
$300 |
|
8 |
30 |
30% |
$300 |
|
9 |
30 |
30% |
$300 |
|
10 |
20 |
20% |
$200 |
|
11 |
10 |
10% |
$100 |
|
12 |
5 |
5% |
$50 |
|
13 |
3 |
3% |
$30 |
Yearly plan:
| Month | Subs | Retention | Month revenue |
|
1 |
30 |
100.00% |
$2,400 |
|
13 |
4 |
13.33% |
$320 |
Now, let’s compare the LTV of the monthly versus year plan:
What’s interesting is that even though the yearly plan is 8 times as expensive as the monthly one, from a 13-month perspective, it generates “only” 60 percent more revenue.
Speaking of which, it’s not uncommon to see a more expensive plan drive less revenue in the long run.
How can this knowledge of LTV per plan help you? Let’s now imagine you ran an A/B test on your checkout page and you noticed that different variants tend to drive users to different plans. Namely:
If you were to judge only by the total number of subscriptions, then Var C is a no-brainer. But when you take the differences in 13m LTV of these plans, you get a projected revenue of:
As it turns out, the projected revenue for Variant A is 6 percent higher than for Variant C (assuming no changes in retention) — which could make a million-dollar difference in the long run.
In a similar manner, LTV allows you to compare the performance of different plans. You can also compare different price points for different plans.
By experimenting with plan prices and measuring their CVR rate and 12/13m retention, you can quite precisely assess which plan will bring you the most revenue in the long run. The most expensive plan doesn’t always win.
Lifetime value informs you how much customer acquisition costs (CAC) you can afford. It’s especially critical for businesses that rely on performance marketing.
For example, if your LTV is $30, then investing in a growth channel with an average CAC of $26 is probably a no go due to thin margins.
On the other hand, if your LTV is $300, you might be leaving money on the table by using a low-cost CAC like SEO. Purchasing a Google Ad ad driving CAC costs to $50 might still be a revenue-positive direction.
LTV can be a great indicator of which growth channels work best for your business.
Let’s say that you are still figuring out your growth model and trying out different approaches. If you calculate the average user LTV based on the channel they came from, you might learn that customers coming from different channels have different LTVs.
For example, let’s say the LTV associated with SEO, Facebook ads, Google ads, and referrals are as follows:
And while the highest LTV isn’t necessarily a winner (it also depends on CAC, and the visitor→customer CVR of a specific channel), it can help you make a more informed decision about which growth channels to focus on.
The hardest part of accurate LTV estimations is how much time it would take to be 100 percent precise.
The most proper wait to measure 13m LTV would be to wait for 13 months and measure CVR and month-by-month retention rate. But who can wait 13 months?
For this reason, we often use historical data to make well-informed predictions. Ask your data analysts for help with details, but usually, you can predict how retention will evolve over the next 13 months by:
If you train your data model well, you will be able to quite accurately guestimate how the retention — and, thus, overall LTV — will unfold.
As a general rule of thumb:
Although developing a sound approach to forecasting LTV is difficult and time-consuming, it will help you make better product decisions. However, deep-dive into data forecasting is out of the scope of this article.
Lifetime value estimates are a powerful tool for any subscription product. By measuring how much revenue a particular type of customer can generate, you can make more informed decisions regarding your plan composition matrix, pricing strategy, customer acquisition costs, and much more.
Without actually thinking about LTV, it’s easy to make a revenue-negative decision based on wrong metrics (like conversion rate changes or just a number of subs). LTV is one of the most accurate indicators of how changes can affect overall business health, not just pretty vanity metrics.
LTV can help you with
However, given the time it takes to measure LTV post-factum, most teams should consider forecasting LTV based on historical data.
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