All SaaS founders need to know about LTV or Life Time Value, says Shripati Acharya of Prime Venture Partners
In this episode of Prime Knowledge Series, Shripati Acharya, Managing Partner at Prime Venture Partners, talks about a key SaaS metric — Life Time Value or LTV.
Sunday August 09, 2020,
6 min Read
“Calculated wrongly, metrics can provide a grossly incorrect diagnosis of business health than the ground reality.”
These words by Shripati Acharya, Managing Partner at, explains that metrics and indicators exist to provide a quantitative estimate of one’s business’ health. In this week’s Prime Knowledge Series, he talks about a key SaaS metric — Life Time Value or LTV.
The objective of LTV is to ascertain how valuable a customer is to the business once they start using the service or the product. The higher the LTV, the lesser the number of customers the business has to acquire to hit its revenue targets.
“More importantly, the higher the LTV, the more the business can spend to acquire the customer,” Shripati says.
Customer acquisition cost (CAC) is, thus, intimately tied to LTV. Conversely, a low LTV business needs to maintain a low CAC to be viable. LTV comprises two elements — value and lifetime.
Calculating the value
How can a business measure the value that accrues from a customer? Shripati says, “A common error is using the revenue to calculate the value.”
He illustrates the problem by using two imaginary SaaS companies — Crunch and Munch. Both companies sell similar enterprise data analytics software.
Let’s assume that the entire analysis is done within the cloud that these companies operate, and the revenue per enterprise customer in each case is $1,000 per year (annual contract value or ACV).
Crunch has designed its product in such a manner that a lot of computing occurs on the client-side and the requirements for cloud computing and storage are small. As a result, its Amazon Web Services (AWS) cost for serving the customer is only $100 for the year.
Munch, on the other hand, does heavy-duty computation on the cloud, and its server costs are $600 for the same $1,000 ACV.
Therefore, Crunch clearly has a superior business. But calculating value using revenue loses this critical distinction. Value needs to be calculated using effective gross margin for the customer. In this case, Crunch’s customer value is $900 ($1,000-$100) as compared to Munch’s $400 ($1,000-$600).
One can also include other direct cost items such as onboarding and service costs to get a more precise figure for customer value.
“The key point to note is that customer value equals the contribution margin of that customer, not revenue,” Shripati says.
Customer lifetime refers to how long a customer continues contributing to a business’ revenue. For a SaaS business, it is how long the customer keeps paying for the subscription. Thus, it is directly linked to customer churn.
“The lower the churn, the higher the customer lifetime,” Shripati explains.
The usual way of calculating lifetime is by taking the inverse of churn. Supposedly, if the monthly churn is 10 percent, customer lifetime would 10 months. This implies that in 10 months, all the customers acquired today would leave.
Shripati says that startups usually arrive at attractive customer lifetime figures in their initial days. If a service launches and in the first six months, only five of customers churn, it appears that the startup has achieved a 10 percent annual churn for a 10-year customer lifetime.
This would seem like a highly attractive business but the problem is twofold.
- The kind of customers the business has acquired initially are not indicative of the vast majority of customers it is likely to acquire down the road. “The later customer cohorts will be more mainstream customers, likely to be more demanding or at a minimum, behave differently than the early adopter cohort,” says Shripati.
- How customers behave initially is not a great indicator of how they would behave after 12 to 18 months. It is important to extrapolate long-term customer use from initial data. The same customer, once they roll out the product widely or after sustained use, can churn for reasons that are not obvious initially.
Shripati says that early-stage startups have sparse customer data. After all, these businesses have not been around for 10 years to prove that the customer lifetime is indeed that much.
He adds, “Blindly presuming that the lifetime of customers in several years based on initial churn data can lead to modelling a much higher target for customer acquisition cost (CAC) than it is sustainable.”
It can lead to all kinds of disastrous downstream effects, such as investing in expensive sales channels that soon prove to be uneconomical.
Calculating LTV as a young SaaS company
A user will realistically commit to paying for a product for 12 months or less. One should also estimate what the likely renewal rate would be. If this seems fuzzy, one should start by using 24-months as a placeholder. As a business, one would want to start recouping their customer acquisition spend as soon as possible.
Thus, a 24-month customer lifetime would mandate a 12 to 14-month payback period, that is, CAC is recouped in 12 to 14 months, to make it an attractive business. In case customers stick around a lot longer, one can modify their acquisition models accordingly.
Shripati says, “Instead of worrying about calculating customer lifetime exactly, use simplifying assumptions with a goal of coming up with targets for recouping the CAC in the shortest possible time.” He further suggests that for enterprise customers, it is around 18 months, with 24 months at the outer end.
To sum it up, to get most out of the LTV metrics, one should watch out for the following traps:
- Calculate customer value using contribution margin, not revenue. In high margin businesses, revenue and contribution margin can be close but costs can change over time and pegging value to revenue can lead to costly errors in customer acquisition.
- Calculating customer lifetime by inverting churn can lead to sky-high customer life-times. Early data does not truthfully reflect customer behaviour over the long-term and also suffers from skew due to early adopter behaviour being very different from mainstream users.
- Early-stage startups should focus more on customer payback, that is, the time period for recovering customer acquisitions cost (CAC), than calculated LTV. In the absence of customer data, using a sub-24-month payback to inform the choice of sales and marketing strategies is prudent.
- Doing cohort wise analysis, using data from longer serving cohorts can be more useful for LTV guidance than averages that are skewed by newer cohorts. Even then, you should have enough users in the cohorts before drawing meaningful conclusions.
(This piece originally appeared in Shripati Acharya’s. is republishing it with minor modifications).
Edited by Saheli Sen Gupta