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The Dangerous Assumptions That Make Most LTV Calculations Wrong

Basing your strategic decisions on an inaccurate LTV calculation can be a costly mistake. So, what can you do about it?

Calculating LTV incorrectly is a costly mistake for B2B SaaS businesses. Yet, unfortunately, it’s a mistake that’s all too common.

Most B2B SaaS businesses look to LTV (Customer Lifetime Value) to understand the total revenue they can expect to receive from any given customer over the course of their relationship.

This metric empowers SaaS leaders to know how much they can spend to acquire a customer (defined as CAC) while still running an efficient business.

That’s why it’s especially dangerous to get this calculation wrong. It can lead to ramping up sales and marketing before you’re ready, or, on the flip side, underinvesting when you should be growing faster.

In this article, we’re going to address some of the biggest challenges with calculating LTV, why your LTV is likely wrong, and what you can do about it. Let’s dive in!

The dangerous LTV assumptions most SaaS businesses make

The basic formula for LTV is to divide your ARPA (Average Revenue Per Account) by your Churn Rate.

LTV = ARPA/Customer Churn Rate

Unfortunately, when you follow this formula, you’re making the dangerous assumption that your Churn Rate is consistent year-over-year and customer-by-customer.

In reality, it’s more complicated than that. In fact, applying a blanket average Churn Rate to any given customer is likely way off base.


There are several factors that make it unwise to assume your churn rate is static and universal across your customer base. As your business grows, you may acquire more customers on one side or the other of each of these factors, which can make a huge impact on your average Churn Rate (and, by extension, your LTV).

1. Size of customer

You likely have a mix of customers of various sizes, even if you specialize in SMB or Enterprise solutions. If you were to break your Churn Rate down by size of customer, you may be surprised at the difference in churn between your smallest and largest customers.

Generally speaking, smaller customers will be more likely to churn simply because of the inherent risks for smaller or newer businesses. They may go out of business, or they may have to dramatically cut their budget.

2. Industry of customer

Customers in more volatile industries are more susceptible to instability based on current events, stock market swings, and supply chain issues. These accounts are more likely to churn. On the flip side, some industries are simply a better fit for your products, and customers in these industries are less likely to churn.

3. Change in relationship with customers over time

The relationship between your business and a customer will naturally change as each business evolves over the years—and as the customer experiences a shift in the value they receive from your company.

Some customers may feel like they’re getting a ton of value from your product during year 1. They’re likely receiving individual onboarding coaching and support from your CS team, and your product is solving a pain point for them.

Over time, however, they may adjust to the new normal of using your product and take it for granted. Or, in some cases, they may find that your product solved the immediate problem, and now they don’t use it as often. This means they could be more likely to churn in years 2 and beyond than they were in year 1 with your company.

Alternatively, the inverse could be true. Your Churn Rate for renewing customers could actually be lower, as customers who make it through their first year using your product are more committed and likely to renew going forward.

4. Percentage of new customers

If your business is growing rapidly (say, doubling your number of customers every year), new customers will have an outsized impact on your churn rate.

If your business has recently added a lot of new products or features, or if you’ve invested a lot into your Customer Success org, your most recent customers are probably having a completely different experience with your company than your older customers had.

Plus, as we just discussed, the first-year customer experience can be quite different from subsequent years.

LTV isn’t usually cohorted, so you’re looking at an average number that is strongly weighted toward the customers who:

  • Are highly enthusiastic about the new product they’ve just purchased
  • Are getting the most frequent attention from CS
  • Haven’t even had the opportunity to churn yet
  • Are not necessarily representative of your older customer or your future customers

There are a few ways companies work around these challenging factors that complicate LTV calculations.

Risky solution 1: For fast-growing businesses, assume a negative Churn Rate

Some businesses that are growing rapidly and focus on upsells within the install base find that the traditional LTV formula simply doesn’t fit their business model. They use modified, more complicated LTV calculations that account for growth within accounts—in essence, they assume a negative Churn Rate.

On the surface, this seems especially helpful for companies with a land-and-expand strategy, a high Net Revenue Retention, and very low customer churn.

On closer inspection, though, basing LTV on a negative Churn Rate can set up highly unrealistic longer-term projections. In short, the numbers get really big, really fast.

For example, let’s say you’ve been in business for a few years, you’re growing quickly, and your NRR is 130%. That’s great for now, but it’s unlikely this will remain true for any given account after more than a couple of years.

Assuming an ongoing 130% NRR implies that an account would be generating 14x this year’s revenue 10 years from now. That could be even higher than the customer’s own revenue! It doesn’t make for a realistic LTV.

Risky solution 2: Make LTV assumptions based on older customers’ data

Another potential workaround is to cohort your customers and simply ignore first-year customer data in your Churn Rate calculation. The advantage of this method is that it’s more stable and not skewed by data from newer customers who haven’t even had the opportunity to churn yet, solving two of our four challenges above.

The new problem is that your business has likely changed a lot since the time your first customers came on board. Many businesses experience more churn in their early years when they’re still working on reaching product-market fit. You’re likely to continue improving your products and strategy with time. Focusing more heavily on older customer data only offers a glimpse of the past, not the future.

So, what do we do about LTV?

We’ve covered a lot of the factors that can make LTV difficult to calculate accurately. So what can we do to understand it better?

First, it’s important to acknowledge that LTV is just one of several important data points to help you triangulate your business’s efficiency. You should never use LTV:CAC as your only source of truth. (In fact, some investors don’t even look at LTV:CAC anymore!)

LTV:CAC should stand alongside your CAC Payback Period, Magic Number, and Burn Multiples to give you a more holistic sense of your efficiency. A broader scope of efficiency metrics can even out some of the ambiguity of LTV alone.

Still, we don’t have to give up on the mighty LTV or accept that it’ll never be accurate! Instead, we just need to look at it in the context it deserves.

Segmenting your customer base can be a game-changer for your LTV calculations. We suggest breaking it down in ways that can make a big difference, such as the four challenging factors we discussed earlier.

For example, try measuring LTV by company size, industry, and cohort. You may notice big variances, but now they won’t come as a surprise.

If you have a clear idea of where your business will be focusing in the coming years (say, selling up-market to larger Enterprise customers), you can use the Churn Rate from that segment to inform your LTV calculations. You may find that calculating separate LTV:CAC ratios for different customer segments is really enlightening—especially when considering where to direct your Sales and Marketing efforts.

Keep your efficiency metrics at your fingertips

Calculating your LTV, CAC, Magic Number, and Burn Multiples doesn’t have to be time-consuming!

Subscript helps you measure all of your key B2B SaaS metrics so you can focus on strategically guiding your business. Plus, with Subscript, it’s easy to break your metrics down by segment to get a more thorough understanding of the numbers in context—without wasting time buried in Excel formulas.

Interested in seeing a demo? Let us show you around.

Looking for clarity around your SaaS metrics? Subscript can help.