SaaS Metrics 2.0 – Detailed Definitions


This page is a supplement to the the SaaS Metrics 2.0 blog post. It provides detailed definitions for each of the key metrics used in that post.


Calculating LTV and CAC for a SaaS startup

Unit Economics is a very powerful way to analyze the long term profitability of a SaaS business.

I am often asked for the details of how to compute the various elements, such as CAC and LTV. This post gives the formulae.

CAC – Cost to Acquire a Customer

CAC is defined as follows:
There is a problem with using this formula in the early days, as you may several expensive people in the team that should scale to handle a fair number of customers as you grow. In that case, your CAC will be too high. I suggest doing a very simple adjustment to the Sales & Marketing expenses to take only a portion of those salaries and expenses in the early days. That will give a better indication of how CAC will look in the future when you are at scale.

Customer Lifetime

If you start with a cohort of 100 customers and apply a constant churn rate every month, you’ll get an exponential decay, as shown in the following graph (which uses a 3% monthly churn rate):

Mathematically this can be simplified to the following formula to find the average Customer Lifetime:

Note that if the Customer Churn rate is a monthly % or yearly %, then the Customer Lifetime will be for the same time period. Here is a monthly and annual example to illustrate the point:

a) If the Monthly customer churn rate is 3%, then the Customer Lifetime will be 1/0.03 which is 33 months.

b) if the Annual customer churn rate is 20%, then the Customer Lifetime will be 1/0.20 which is 5 years.

Lifetime Value of Customer

In the situation where there is no expansion revenue expected over the lifetime of a customer, you can use this simple formula:

which can also be expressed as follows:
Once again if ARPA is monthly, the churn rate should be monthly.

To truly get an accurate picture of LTV, you should take into consideration Gross Margin. i.e.


However in most SaaS businesses, the gross margin % is high (above 80%), and it’s quite common to use the simpler version of the formula that is not Gross Margin adjusted.

Ron Gill, NetSuite: I’m surprised at how often I see a SaaS product architected in a way that means they’ll never clear a decent gross margin. Including GM in the calc is a great way for you to see there is a big lever on LTV/CAC that is worth focusing on.

For NetSuite, we’ve not only calculated LTV/COCA, but also calculated r-squared of each of the components (to see what has driven improvement) and sensitivity analysis on them (to see what might drive it in the future). GM is an important component.

More complex case
In the specific situation where you expect ARPA to change over the lifespan of the customer due to expansion revenue, this simple version of the formula will not work. We ran into this situation with ZenDesk, where there is a pretty reliable increase in revenue over the life of a customer.

Here’s a graph showing what would happen if you had a cohort of 100 customers that initially started paying you $100 a month, but increased their payment by $5 every month. The monthly Customer Churn Rate is 3%:


As you can see the expansion revenue initially is greater than the losses from churn, but over time the churn takes over and brings down the value of that cohort.

I asked my partner, Stan Reiss, to help with the math to calculate LTV in this more complex situation. Here is what he came up with:

a = initial ARPA per month ( x GM %, if you prefer)
m = monthly growth in ARPA per account
c = Customer Churn Rate % (in months)


(This formula makes an assumption that revenue increases at a roughly fixed rate every month for the entire lifetime of the customer. That probably doesn’t hold true for many SaaS businesses, but the goal is to get a rough idea, not to have the absolute perfect answer.)

LTV : CAC Ratio


Our guideline for a successful SaaS business is that this number should be higher than 3.

Ron Gill, NetSuite: It is most important to track this metric over time to make sure you’re driving improvement. And, look at investment and how it will impact.

(The guideline assumes you are using the simpler LTV formula that does not include a Gross Margin adjustment, and that you have a Gross Margin of 80% or higher.)


Months to recover CAC

To be perfectly accurate, this should include a Gross Margin adjustment as follows:
However in our guideline which states that Months to Recover CAC should be less than 12, we are assuming that you are using the simpler formula, and have a Gross Margin of 80% or higher.

The Metrics to help understand Bookings

MRR The Monthly Recurring Revenue at the end of each month. Computed by taking the MRR from the previous month and adding Net New MRR.
ARR Annualized Run Rate = MRR x 12ARR is annual run-rate of recurring revenue from the current installed base. This is annual recurring revenue for the coming twelve months if you don’t add or churn anything.
ACV Annual Contract Value of a subscription agreement.
New MRR/ACV The increase in MRR from new customers in the current month.
Churned MRR/ACV The lost MRR from churning customers in the current month.
Expansion MRR/ACV The increase in MRR from expansion in your installed base in the current month.
Net New MRR/ACV Net New MRR = New MRR + Expansion MRR – Churned MRRThis is the sum of the three different components that will change MRR during each month.
Bookings The total dollar value of all new contracts signed. Usually taken as an annualized number even if the contract period is longer than one year.Since the bookings number might have a mix of different durations (e.g. month-to-month;  6 months; 12 months) this number is not very helpful for understanding the business.To really understand what is going on in your SaaS Business, you should look at the following components:a) What happened with new customers:

  • New MRR/ACV from new customer contracts

b) What happened in your installed base:

  • Renewals
  • Churned MRR/ACV
  • Expansion bookings

The sum of all of the above:

  • Net New MRR/ACV
Billings Billings is the amount that you have invoiced that is due for payment shortly.
Revenue Revenue is amount of money that can be recognized according to accounting policy. Even if it is paid for upfront, usually subscription revenue can only be recognized ratably over time as the service is delivered.If more money has been paid than can be recognized, the difference goes into a balance sheet item called Deferred Revenue.
Average Contract Length Assuming a mix of different contract lengths, this gives you the average duration in months or years.
Months up front Average of months (or years) of payment received in-advance with new bookings. Getting paid in advance has a big positive impact on cash flow. This metric has been used at both HubSpot and NetSuite in the past as a way to incent sales people to get more paid up front when a new customer is signed. However asking for more money up front may turn off certain customers, and result in fewer new customers, so be careful how you balance these two conflicting goals.
ARPA – Average monthly recurring Revenue per Account This number is tells you the average monthly revenue per customer. It is useful to look at this for just the new customers booked in the month. Plot a trend line to show you the average price point that your new customers have chosen.

Bookings, Billings and Revenue – An example

Since there can be some confusion around the difference between bookings, billings and revenue, here is a simple example to help clarify them: Imagine you signed a new contract with a customer with a one year term, specifying that you provide your service to them for $1,000 per month, with an upfront payment of six months:

  • Your bookings would be $12,000 (the entire contract value)
  • You would bill $6,000 in the first month, then $1,000 per month from the seventh month onwards.
  • You would recognized $1,000 in revenue for each month of the contract. (This is dictated by GAAP accounting policy.)

For the example above, the balance sheet and income statement impact of these items is as follows:

  • Bookings do not affect either the balance sheet or the income statement.
  • When you bill $6,000 in the first month, but can only recognize $1,000 in revenue (income statement), and the other $5,000 goes into deferred revenue on the balance sheet (a liability).
  • Each month thereafter until another $1,000 can be recognized as revenue (income statement), and that reduces the deferred revenue liability on the balance sheet.

The Metrics for Churn

The following shows the metrics to understand Churn:

# of new Customers The number of new customers added this month
# of churned Customers The number of customers lost due to churn this month
Net New Customers Net New Customers = # of new Customers – # of churned CustomersThis is the net number of new customers added once lost customers due to churn has been taken into account.
% Customer Churn image
% MRR Churn Defined as lost revenue due to churned customers as a percentage of total recurring revenue.image(See below for a description of why this is different to % Customer Churn.)
% MRR Expansion Defined as the expansion revenue from existing customers as a percentage of total revenue.image
% Net MRR Churn imageThis is the number that will go negative if the Expansion revenue from existing customers starts to outstrip the lost revenue from churn.Getting to negative Net MRR Churn is a great goal for a SaaS company.
% Renewal Rate (Customers) renewal-rate-custs It can be confusing to look at both your renewal rate (which should be just 1- Churn) in addition to churn. However in a model where you have yearly contracts being renewed, the two numbers can actually be different. For example, in the early days of a startup, you might have low churn because many of your customers have not yet reached the point where they could drop your service because of the length of their contract. In that situation, your churn number will not accurately predict what is really going to happen when you reach steady state. So a better number to look at is how many of your customers are renewing at the point where their contract expires. That is what this number measures.

When you reach steady state, this number should be equal to 1 – % Customer Churn.

% Renewal Rate ($’s) renewal-rate-dollars
Similar to the number above, but instead of looking at the number of customers, it looks at the dollar value of the renewed contracts. It’s important to look at both, as they each tell an useful part of the story. If you were losing a lot of customers, you’d want to know why. Similarly, if you were only losing a few customers, but they were your biggest $ value customers, you’d also want to know that as well.



Share and Enjoy


  • Vance

    Hey David,

    We had someone break their contract two months in. We’ve also had a company go out of business part way through their contract. How do you suggest we deal with that?

  • Ellen Pfeiffer

    Hi David, that’s helpful.
    I have more more question. We’ve begun segmenting our customers into different groups and analyzing these metrics for each. We have a few special groups of customers that are very loyal and we may go several months without any churn. How would we go about calculating Customer Lifetime and Lifetime Customer Value? The calculation would assume 0 churn = infinite Value, correct? But that’s not usable, so what would you suggest?

  • David Skok

    Hi Ellen, for that group, I’d recommend using a time period like 6 months or 12 months where you think that the churn is representative. Then use the revenue for that period and divide by the churn for that period to calculate LTV. I hope that helps.
    Best, David

  • David Skok

    Hi Vance, sorry for the delayed reply. Both of those are churn events, so should be counted as churned customers. It is useful to try to understand why the first customer wanted to break their contract. A customer like that costs you a lot of money. Ideally you’d either like to find out what you could fix to stop them from churning. But sometimes the right answer is that they were never a good fit for your product, and shouldn’t have been sold it. That means changing your qualification criteria, and marketing targeting.

  • ZAgold

    David, thanks for this post. Very useful, indeed. My company provides a form of SaaS that involves quite a bit of upfront development and customization. Once a 12 month contract is signed, 50% of the startup fee is paid. Then it takes around 6-8 weeks for their dev to be completed. Once that is done, they pay the remaining 50% startup fee, and we begin the monthly billing. At what point should we count a new customer? Upon signing of contract or once billing is started? Thanks for your insight.

  • David Skok

    I’m not sure that there is a single clear answer here. For purposes of talking to investors, etc. I would count them as a customer as soon as you start doing billable work for them. For the purposes of looking at Churn metrics, I would be inclined to consider them a customer at the point where you start billing the monthly payment. Did this answer your question?

  • Alun@MarketDojo

    Hi David. We sell two types of SaaS licences. Annual and monthly. For the monthly there is no contract but most customers renew every month. However some customers dip in and out. So a customer will buy a month licence, then wait 4 months and buy another month, then maybe wait 6 months before buying another. Would you consider these second two sales here as growth revenue (as it wasn’t forecast or predicted) or renewal revenue (as it was the same as they had before)?

  • David Skok

    Hi Alun, You have an interesting situation that I have seen
    occasionally elsewhere. The difficulty that your situation presents is how do you know if a customer has churned and will never come back, or if they have just left temporarily and plan to come back again later. Ideally if I were in your shoes, I would try to figure out if they are really churning or not, with some kind of simple questionnaire, perhaps using an incentive to answer like offering to keep all their information alive if they tell you the right answer. Then you could get a clear picture.

    Then to answer your question, I would look at those who restart
    their usage, not as upsell, but in a category of “restarts” or some similar name.

    I would keep the name renewal for true renewals, where someone
    who was on a one year contract signed up for a second year. That way you will have clarity in the company when people talk about renewals. This is important as you are likely to want to have a team dedicated to making sure that all those annual contracts do renew.

    To compute the lifetime value of this kind of user, I would look
    at across the whole group of them and come up with the average amount of revenue you could expect to see in a year of partial usage. Then divide that by the churn rate of that type of user.

    I hope this helps.
    Best, David

  • Henry

    Hello David,

    This post has been wonderful for my calculations but I need to know what you think of my personal take on our CAC.

    We have a SaaS/IaaS freemium model with a small premium tier.

    I want to calculate our CAC.
    Undoubtedly we should include all the costs that the free users that burden us with;

    Server Costs
    Onboarding Costs
    Support Costs
    Maintenance Costs (Salaries of the upkeep of the servers?)

    When a free user signs up, we can estimate the life cost on average.
    (We have an Active Free User Churn)
    In other words, how much they will burden us from now until they stop using the product.

    Should this all be included?
    If so, Feb CAC’s would include costs that will occur in June.

    or should I only consider the costs that the free users (could be not just the new ones) occur in the month of creation?


  • David Skok

    Henry, do you know how long it takes on average for a free user to convert to a paid user? If for example, the average time taken was three months, then what I would do is look at the costs of all the free users three months ago, and include that in the CAC for this months customers. Ignore future costs as that is over complicating things. I hope this is clear. If not, let me know.

  • Arun Abraham

    Hi David,

    Great post and have to tell that I’ve been referring your metrics for a while.

    However, what ONE value of churn that you would refer when someone,(such as an investor) asks you? We’ve been using the (# of Cancels)/(# of paid users) each month. However, the churn rate has been varying over the last few months. 4% – 14% – 10%.

    I came up with a formula to do a weighted average of this value for the last few months and wanted to check your thoughts. I’m doing a weighted average of the churn for the last 3 months with the number of paid users as a weight.

    eg. C1, C2, C3 are the # of cancels last 3 months. P1, P2, P3 are # of paid users at the beginning of each month.

    So the weighted average would be

    (P1 * (C1/P1) + P2 * (C2/P2) + P3 * (C3/P3))
    P1 + P2 + P3

    Which is nothing but (C1+C2+C3)/(P1+P2+P3).

    Do you think this makes sense?

  • real7a

    back to school %)

  • Victor Belfor

    The formula below the Cohort chart is missing a factor. Should be a/c+A*(m*(1-c)/c^2)

  • David Skok

    Thanks Victor. I will look into it.

  • Sam Shiah

    Hi David/Victor,

    Would you mind explaining the logic behind this formula in a bit more detail? Particularly the second part of it where it factors in the expansion rate. Why is it m(1-c)/c^2?

    Victor, in your version of the formula, are “a” and “A” the same? Wasn’t sure if the capitalization of the letter meant something different.


  • Alun@MarketDojo

    Hi. I am not sure if this helps but I had a question on the same area below and Stan Reiss posted some detail there. In effect the formula comes from summing the revenue over time to infinity.

  • Tony Zhang

    Hi David,

    Huge fan of your work on SaaS metrics and have been using it extensively. I wanted to ask you a question on the 3:1 LTV to CAC ratio and the guidelines attached to it where you stated: “The guideline assumes you are using the simpler LTV formula that does not include a Gross Margin adjustment, and that you have a Gross Margin of 80% or higher.”

    What exactly is the precise calculation behind that? Are you factoring in upsell driven LTV into that as well or is the calculation simply New MRR/ARR Per Customer / Churn Rate?

    Thank you and appreciate any insight you can share.


  • David Skok

    Hi Tony, this does factor in upsell driven LTV in the guideline. That is one of the most powerful elements of a good SaaS model: cancelling out customer churn with upsells.
    Best, David

  • Ada Yeo

    Hi David, Thanks so much for your posts – they have been the best guidance in learning about SaaS as a VC analyst! Was wondering:

    1) For SaaS models where the subscription plans are a mix of 3, 6, and 12 months at two price points, say $750 and $1500 (so 6 plans available), where the customer pays upfront and is effectively locked in for that period, how do you calculate the churn %? I see that the traditional method would be to take “no. of customers churned in month/customers at the end of last month”, but I also read that it makes sense to calculate “discretionary churn”, defined as “no of customers churned/customers up for renewal at the end of last month” since the locked-in customers can’t actually churn.

    2) Getting the customer churn rate is important so that I can calculate customer lifespan, defined as 1/churn rate. But how do I do this based off discretionary churn? Would it be right to say that the correct modified CLTV formula in this case would be:

    ARPA (where Avg MRR across all accounts for month*Avg contract length across all accounts for month)
    *(1/discretionary churn %)

    3) Does it make sense to segment all thes SaaS metrics, alongside CLTV into the $750 and $1500 price points since the variables could be different for both buckets?

    4) I have looked through a lot of your posts, but couldn’t find a dedicated post about forecasting in a data-driven way for a high-touch sales SaaS company (i.e. needs an on the ground sales force).
    Any best practices or tips? E.g.
    – Do I use a 3 months moving average to forecast the growth of paying customers, esp if past sales performance has been seasonal?
    – Should it be built ground up (from no of sales people and prospects per mo, conversion % into sales, etc.) or top down – a blanket growth assumption of 20% more paying customers per month (but this doesn’t seem tied to reality and key inputs)
    – If I used discretionary churn, how do I forecast the lost customers due to churn per month since I then have to forecast the number of renewals in each month (given that they have 6 available plans, it seems impossible)? or is it okay just to use the traditional churn % here?

    Apologies for having so many questions – but it’d be so amazing if you could share your thoughts!