This post provides SaaS entrepreneurs with an Excel spreadsheet model and graphs that show the cash flow trough that happens to SaaS, or other subscription/recurring revenue businesses that use a sales organization. These kinds of SaaS businesses face a cash flow problem in the early days, because they have to invest up front in sales and marketing expenses to acquire customers, and only get payments from those customers over a delayed period of time. I refer to this phenomenon as the the SaaS Cash Flow Trough. The model also compares the cash flows of businesses that charge monthly to those that are able to charge their customers for a year’s payment in advance.
The greatest value from this post will come from downloading the model and inputting your own variables. The Excel Spreadsheet and associated PowerPoint file can be downloaded by clicking here. If you store both in the same directory, the PowerPoint graphs can be updated to reflect the data in the spreadsheet by right clicking on each graph, and selecting “Edit data”.
Part 2 of this series can be found here: SaaS Economics – Part 2: Scaling the Business.
Where is this applicable
- This model is applicable to any recurring revenue business that uses a sales force.
- This model does NOT apply to SaaS businesses that don’t use a sales force. I refer to those businesses as having a “touchless conversion”, as there is no sales touch involved. Those businesses usually have a far lower investment in sales and marketing expenses, and become cash flow positive far earlier.
What are the different analyses?
The model looks at the following different analyses, and each is described in this blog post with graphs:
- How bookings accumulate over time
- The effect of churn
- The three components of MRR (monthly recurring revenue)
- Cash flows for an individual sales person
- The marketing costs of providing a sales person with enough leads
- Cost to acquire a customer
- Lifetime value of a customer
In part 2 of this blog post series, The second part of the model looks at what happens when a SaaS company has reached the point of a repeatable, scalable sales model, and wants to start ramping their sales and marketing spend to grow revenues.
The last part of this blog post discusses how the model was built, and how to use it for your own calculations.
How revenue builds for a single sales hire, assuming no ramp up time
For those new to SaaS or other recurring revenue businesses, the graph below shows one of the delightful things about recurring revenue. Bookings made in January, continue to be billed in every subsequent month. The chart on the left shows this effect assuming no churn rate (or loss of customers). The graph on the right shows the impact of a 2.5% monthly churn rate, which slowly eats away amount that billed monthly.
Bookings, Churn, and MRR for a new sales hire
The above graphs assume no ramp up time. Lets take a look at bookings, churn, and MRR for a new sales hire:
The graph on the left shows how new monthly bookings ramp, and how churn builds up over time. The graph on the right shows MRR (Monthly Recurring Revenue) which increases every month by the new bookings, and decreases by the churn. For experienced SaaS business people, this MRR graph is probably obvious, however for those new to SaaS, it is worth clearly understanding the three different components of MRR.
The Cash Flow Trough
Lets now look at the timing of sales expenses for a new sales hire, and how this creates a problem as revenue takes a while to build:
In the left hand graph we can see how expenses stay roughly flat, but MRR grows slowly over time. This creates the SaaS Cash Flow problem that is the main topic of this blog post.
In the right hand graph, we can see how it takes 11 months before that new sales hire breaks even, and starts contributing positively to the profit of the company. (It is very important to note that this number is going to vary greatly from one SaaS business to the next depending on the many variables used in the model.) This timing is slightly later than the cross over point on the left hand graph, due to the model showing a gross margin of 80% after taking out the cost to serve each customer.
Now lets look at how how these expenses and gross profits look on a cumulative basis:
The above chart shows clearly the size of the SaaS Cash Flow Trough ($110k in this case), and the time to recover the investment (23 months in this case).
These losses can be very concerning to an unsophisticated investor. However the chart also shows something extremely important about SaaS businesses, which is that once you have gone through the SaaS Cash Flow Trough, the profitability of that sales investment starts to soar. This graph is the key to understanding the economics of a SaaS business.
In Part 2 of this blog series, I will look at how ramping a sales force where you add multiple sales hires every month affects this trough. I will also look at the impact of collecting payment for a year in advance impacts cash flow.
How the inputs to the model work
The starting assumptions:
This shows the input variables that are used to drive the model. Cells that are colored in Orange are input cells that can be changed to reflect your own situation.
Note the Sales attrition factor, which discounts bookings by 15% to take into account the failed sales hires, and departing sales people. It is pretty common to hear of 30% sales attrition. However since the failed sales people will still do some level of bookings, I have guessed at a number of 15% to take this into consideration.
Marketing Funnel Economics
The next piece of the puzzle that we need to understand is how do marketing costs increase as we add sales people. Lets start by taking a look at an assumed marketing funnel:
The left hand diagram shows the main elements of the funnel, and the right hand diagram breaks down the actual flow of leads. If we look at the very top of the right hand funnel, we see that leads are assumed to come from two different types of sources: paid leads and organic leads (unpaid). The model assumes that organic (unpaid) leads tend to increase at roughly the same rate as paid leads, and allows you to set a variable which is what percentage of overall visitor traffic comes from unpaid sources. In the example data, I set this to 50%.
Below I show the part of the spreadsheet model that computes the cost of leads required to serve a new sales hire ($8,698 per month). This turns out to be very important as it is a significant cost that is frequently ignored.
The model also computes CAC and LTV
The model also computes rough values for CAC (Cost of Acquiring a Customer) and LTV (Life Time Value of a Customer). These values are rough as they don’t include costs for marketing staff, or sales management. (It is not too hard to add those expenses: take the monthly expense values and divide them by the number of customers acquired in that month.)
The primary purpose of this blog post is to provide entrepreneurs who are thinking about SaaS and other recurring revenue businesses with a model that they can use to understand the impact of various different variables. (It is important not to look at the specific data that I have used to populate the example company, as this will vary greatly from one SaaS business to the next.)
The model shows us several important insights:
- How long does it take to get to breakeven
- What is the total amount of investment required (i.e. how big is the bottom of the trough)
- How long does it take to recover that investment
- How profitable the business can be over time after coming out of the trough
What comes next: Part 2
In part 1, I have only discussed the data for a single sales person. In part 2, we will look at what happens when a business reaches the stage where it has a repeatable, scalable sales model and starts to hire multiple sales people every month.
- Cash flow when hiring two salespeople per month
- Comparison of cash flow when hiring one versus two salespeople per month
- Impact on revenue of not stopping sales hiring
- Discussion of the limitations to growth
We will also look at the following additional analyses:
- Effect on cash flow of collecting a year of payments in advance.
- Effect of lower or higher churn rate
Click here to get to Part 2.