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How to calculate the Discount Rate to use in a Discounted Cash Flow (DCF) Analysis




We look at how to compute the right discount rate to use in a Discounted Cash Flow (DCF) analysis. This post is a supplement to a blog post titled “What’s your TRUE customer lifetime value (LTV)?  – DCF provides the answer“.

My thanks to my partner Stan Reiss, who co-authored this piece with me, providing all the expert math help.

In that blog post, we discuss why it is valuable to apply discounts to future cash flows when calculating the lifetime value of a customer (LTV). This discounted cash flow (DCF) analysis requires that the reader supply a discount rate. In the blog post, we suggest using discount values of around 10% for public SaaS companies, and around 15-20% for earlier stage startups, leaning towards a higher value, the more risk there is to the startup being able to execute on it’s plan going forward.

The Discount Rate should be the company’s WACC

All financial theory is consistent here: every time managers spend money they use capital, so they should be thinking about what that capital costs the company. There can be many sources of capital, and the weighted average of those sources is called WACC (Weighted Average Cost of Capital). For most companies it’s just a weighted average of debt and equity, but some could have weird preferred structures etc so it could be more than just two components.

To calculate WACC, one multiples the cost of equity by the % of equity in the company’s capital structure, and adds to it the cost of debt multiplied by the % of debt on the company’s structure. Because interest in debt is a pre-tax expense, the cost of debt is reduced by the tax rate (it’s effectively tax deductible).

The formula is


Ke =  the cost of equity.  This comes from the Capital Asset Pricing Model (CAPM), described below.

Kd = cost of debt. This is the average interest rate on the company’s debt. To be completely correct, it’s the coupon divided by the market value of debt, since the value of company bonds fluctuates, but generally this is too complicated for the exercise at hand and, unless the company is in distress, just looking at the book value is close enough.

T = corporate tax rate.  The right number to use is the marginal tax rate since you’re trying to make a marginal decision, and that’s typically 35% in the US.

Ve = value of equity.  Company market cap less cash plus debt. For a private company, best estimate – probably based on last round price.

Vd = value of debt.  As described before, the proxy is book value.

Simplifying this for Startups

For most startups, equity is the primary method of financing, so it may be helpful to simplify things and state that WACC equals Ke (the cost of equity), which effectively also means that the Discount Rate should be equal to Ke.

Computing the Cost of Equity – The Capital Asset Pricing Model (CAPM)

The cost of equity, Ke, comes from the CAPM.  What investors expect to earn on their investment in the stock. If they conclude they won’t get this return they’ll sell the stock and the price will go down, if they conclude they’ll get more than this return additional investors will buy the stock and the price will go up, eventually driving the return to Ke in equilibrium.

The basic CAPM formula for Ke is


  • Rf = Risk free rate of return.  A good proxy is a US government bond of a duration that’s commensurate with the time frame an investor would think of when owning the stock.  The 5 year T-bill is a good proxy. Today the 5 year T-bill yields 1.7%, the 10 year 2.2%, so a 2% risk free rate is a good proxy.
  • B (Beta) = Sensitivity of the expected stock return to the market return. Have to use history to estimate.  Mathematically it’s the covariance of the historical return of this particular stock and the market divided by the variance of the market. So B = Cov (Rs, Rm)/Var(Rm). The best way of getting at this is to look at the beta of similar public stocks. For public SaaS companies, the beta today seems to be about 1.3.
  • Rm = Market rate of return – what the investors expect the market to return. The public markets have returned around 8% per year over the last decade, and one would think that that’s a reasonable rate expected by investors.  There could be different opinions (for example the 5 year rate of return is a lot higher).  If a company is private, one would expect a much higher rate of return.

Plugging all this in for a SaaS company, one would get

Ke = 2% + 1.3 (8% – 2%) = 9.8%      ~ 10% for a public SaaS company.

For a private, or higher risk company, Ke will depend on the assumption on Rm (the market rate of return). Reality is this is highly volatile and situation specific – sometimes one can raise cheap money and sometimes one can not.  While a lot of situational judgment should be applied, Cambridge Associates, which tracks the stronger venture firms, claims a 30 year venture return of 17.7%, and that’s probably the best proxy.

So for a private SaaS company one could assume

Ke = 2% + 1.3 (17.7% – 2%) = 22.4%      ~ 20% would be a good estimate to use

For reference our Beta calculation came from averaging Google Finance Betas for a selection of public SaaS companies:

  • – 1.33
  • Workday – 1.53
  • ServiceNow –  1.11
  • Netsuite – 1.5
  • LogMeIn – .96
  • Liveperson – 1.35
  • Demand ware – 1.31.

The newer SaaS public cos (ZEN, HUBS, MKTO) haven’t been public long enough to calculate a good Beta.


For SaaS companies using DCF to calculate a more accurate customer lifetime value (LTV), we suggest using the following discount rates:

  • 10% for public companies
  • 15% for private companies that are scaling predictably (say above $10m in ARR, and growing greater than 40% year on year)
  • 20% for private companies that have not yet reached scale and predictable growth

Is there an argument to be made that startup SaaS companies shouldn’t be using a different discount rate to public SaaS companies, as their goal is to show that they have the needed unit economics to become a public company? Yes, there probably is. We are charting new territory with this analysis, so it will be interesting to hear readers’ feedback on this question.