Credit Suisse SaaS Unit Economics Index

LTV/CAC: Credit Suisse approach to SaaS Customer Unit Economics

LTV/CAC calculations can be complex for SaaS startups! Do you know how to calculate them for you startup or how to calculate them for public traded SaaS companies so you can get comps for your fundraise? It’s always useful to have benchmarks to spar with VCs!

Fortunately, Credit Suisse have written down their approach for your educational benefit. Have a read!

What does the output look like for public companies?

So here you can see that all the companies publicly traded for which there is sufficient data have been plotted on the x-axis. On the Y-axis you can see the LTV/CAC multiples. The average = 2x. Simply, this means that the value of customers is twice that it costs them to acquire them. This is the ‘simple’ explanation, the full version is more complicated as it involves discounting etc. Read on to get the details.

Note: Do not use this calculation if you are early-stage! It’s too complicated and will distract you. Read it to learn and note for the future when you are more series-B and have some stability and gathering better data sets.


Why is this important? 

Intuitively, we expect software companies that can demonstrate superior unit economics prospects to warrant higher valuations (all else equal), and vice versa. We believe this exercise is important as it could:

  1. provide a glimpse on the current state of SaaS companies and overall financial stability in the universe,
  2. identify vendors with superior unit economics / SaaS models currently, and
  3. help determine potential compelling investments opportunities for investors who have a longer investment horizon appetite.

As well, this analysis could help SaaS companies identify areas where they could potentially accelerate investments to fuel even stronger, more profitable growth.

How is this different/unique versus other valuation frameworks? 

The study of unit economics (customer life time value/customer acquisition cost, or LTV/CAC) is nothing new and is instrumental when determining the overall health of any business, especially subscription software companies.

By definition:

  • LTV = discounted net profits / cash flows attributed to the customer over the entire expected lifetime with the companyLTV/CAC

  • CAC = associated costs incurred by the company to acquire the customer

Key financial metrics (e.g., revenue growth rates, operating margins, cash flows) and relative multiples (e.g., EV/Revenue, EV/FCF, EV/EBITDA, P/E) are all useful, but they share one common shortfall: they only tell us the current/near-term state of the business and are poor indicators on how well the company will do in the years ahead.

Clearly, this prediction model can have varying levels of accuracy and precision depending on the input variables, of which we attempt to simplify and normalize for with our unique methodology detailed below.

What is the methodology? 

Striking the proper balance between simplicity and complexity is crucial given asymmetric information. While it would be optimal to fully allocate applicable costs when determining customer acquisition cost (e.g., sales and marketing expense related to new customers, costs of onboarding, allocated overhead and support costs), this complexity adds too much uncertainty and too many variables into the mix, in our view, and ultimately limits the usefulness of, and the ability to reproduce, this exercise. Our simplified methodology assumes the following inputs:


(Δ Recurring Revenue x Recurring Gross Margin) × (r / (1 − r + ?))

Sales & Marketing Expense


  • r = recurring revenue retention rate, and

  • ? = discount rate

What happened to gross customer additions? What’s your “unit”? 

Our methodology is an improvisation on the traditional customer unit economics model, given that less than a handful of subscription software companies openly disclose gross customer additions.

Essentially, we removed the “unit” (i.e. customer) in both the numerator and denominator from the standard formula, and evaluated it using a total net change in recurring gross profit dollar versus sales and marketing expense framework.

Why use recurring revenue retention and not customer retention? 

Both recurring revenue retention and customer retention rates are important as each reveals something different on the overall health of the business. However, for our analysis, we believe recurring revenue retention is a superior metric as it looks at revenue churn (which encompasses the percentage of recurring revenue lost due to churned customers).

Customer churn alone could be misleading as some companies could see a high recurring revenue retention rate, but a lower customer retention rate simply due to the nature of their business (e.g., lower-lifetime value subscribers churning off due to failure to launch).

Ultimately, we believe SaaS vendors that demonstrate stronger recurring revenue growth should warrant a premium valuation to the peer group average, rather than software companies that might have strong customer growth but lack potential for further monetization.

Why include the entire allocation of sales and marketing – what about “land and expand” sales strategies? 

We account for all sales and marketing expenses because we evaluate the total incremental change in recurring revenue, which could include revenue from:

  1. new customers, and/or
  2. existing customers from cross-sells / upsells.

We believe this is a fair assumption as it would properly reward SaaS companies that either

  1. focus exclusively on landing new accounts (i.e. hunters),
  2. emphasize upsell opportunities (i.e. farmers), or
  3. a combination of both.

What discount rate did you use? 

We assumed a discount rate of 10%.

More importantly, we note that the discount rate isn’t as essential and wouldn’t alter our final conclusion (no material impact to the correlation analysis and R2 using a discount rate of 15% versus 10%), given the inherent nature of our unit economics analysis (relative framework, which benchmarks subscription software companies to other SaaS peers in the universe, rather than on an absolute basis).

Company X has cited a much higher LTV/CAC ratio – why is yours different? 

There could be numerous reasons, including the methodology (there are many viable alternative models) Company X used to derive its CLTV/CAC ratio and the different assumptions (e.g., on-boarding costs, allocated overhead / support costs, adding new customers vs. up-selling), which could be vastly different versus our model. Additionally, some companies do not incorporate a discount rate into their customer unit economics analysis, of which we assume to be 10%.

Why is Company Y missing from your analysis? 

We tried to include and account for every SaaS name in the universe. Clearly, some SaaS models may not be fit for this unit economics analysis while others have limited information available.

What are the shortcomings of this analysis? 

While the simplified model is helpful to approximate and estimate the customer lifetime value, the methodology assumes that the contribution margin, retention rates, and discount rates are held constant in perpetuity. Any meaningful changes to these variables will have a material impact on the LTV/CAC ratio.


Hopefully, you found that interesting! If you have the data, try applying the model and let me know how you get on!


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