SaaS companies are incredibly metric driven, but when it comes to settings quotas there seems to be a lot of guess work. The typical response is “A good ratio for setting a quota is 4x-5x the AE’s OTE and the best companies do somewhere between 6x-8x”. Industry benchmarks are great, but every company is different and purely basing quota on a multiple of OTE doesn’t take into account a lot of important factors. Also, the difference between a compounding 4x, 5x, and 6x multiple can be huge when you have more than one or two AEs. So how can management make this process a little more scientific?
If you want to skip to the model example and follow along, here it is.
Common Methods Used to Set Quotas
Let’s start with the two most common approaches companies are taking today: 1) top down quota and 2) bottoms up quotas.
- Top Down Quota. This is one of the most common approaches that I described earlier. You go and pick some industry benchmarks of what the quota should be in relation to OTE and maybe calibrate for what your AEs are achieving so the quotas aren’t too easy or not achievable. SaaStr has lots of great content on the topic so you might go with a Quota:OTE multiple that Jason Lempkin recommends here.
- Bottoms Up Quota. Determine what a reasonable number of deals each AE can close for a given period and then multiple it by the average contract value (ACV). Sales management can look at historic data by AE, geography, vertical, etc. and see what they have historical been capable of achieving and set quotas based the data.
Both of the above methods for setting a quota kind of work but they both have their flaws. Relying on a top down quota based on industry benchmarks is too narrowly focused by relying on just the relationship between OTE and quota. There are a lot more expenses that quota needs to cover than just the AE. Since every company is different, relying too heavily on industry benchmarks for this ratio can lead to bad overall unit economics or bad unit economics for specific sales teams within the organization. Or it can lead to setting way too aggressive quotas when the unit economics at the company would still be great with much lower quotas without disincentivizing the sales team and creating a bad sales culture.
A bottoms up quota has similar issues. While a bottoms up quota is likely going to be reasonably attainable since they are built from historical sales data, the quota might not be at a level required to even recoup its costs.
Setting Quota with Fundamental Unit Economics
It is time to be more scientific with quota setting. This is not to be a replacement for tops down or bottoms up quota setting, but a calibration tool to enable sales management better insight into the key KPI and SaaS metrics that will be generated as a result of the quota assigned.
What is a fundamental unit?
Tomasz Tunguz at Redpoint describes a fundamental unit is his blog:
“A fundamental unit is the atomic go-to-market team: the minimum number of people in the marketing, sales and support roles to be able to support X customers and generate Y in revenue.”
If we take this concept of a fundamental unit and expand it a little bit then we can use it to back into the Quota:OTE ratio required to meet the unit economics required to run a successful SaaS company. Below is an example of a fundamental unit build out to mathematically determine the required quota. Note that the fundamental unit is built with the AE as the foundation.
This concept can then be further disaggregated based on AE market (SMB, Enterprise, etc), location, vertical, etc. These different dimensions will likely have different unit economics and management might be OK with that but understanding at the beginning those differences can help drive decision making of quotas and where dollars are spent.
How do I set a quota with a fundamental unit?
The fundamental unit described above should theoretically be a subset of overall customer acquisition cost (“CAC”) at the AE level. With 70-80% of SaaS spend being on payroll, you should have a pretty good idea what your headcount spend will be since market factors drive headcount spend. The remaining 20-30% of non-headcount spend will vary by company so this can vary depending how efficient you spend.
Once you have the fundamental unit CAC, you can determine what the required fundamental unit customer lifetime value (“LTV”) needs to be in order to achieve overall company goals for an LTV:CAC ratio and ensure a sustainable SaaS business model.
Now you have CAC, your LTV:CAC ratio goal, and most of LTV (just missing the required quota). It is just math now to back into the required quota to obtain your LTV: CAC ratio goal.
Access our simplified model here. The information can be further disaggregated and made as complicated as you want. I recommend trying to keep it simple when first trying this approach so you can really understand the underlying math.
How to Use the Model
With all of this information you now have everything you need to back into the required quota to meet your objectives on an LTV:CAC basis. SaaS companies thrive or fail based on their long-term LTV:CAC ratio because it is an indicator of eventual profitability and sustainability of your business model. With this model for determining quota assignment, you can more easily tailor your Quota:OTE ratio based on your company specific factors. Industry benchmarks are great, but if you have a 6x quota to OTE ratio, but your marketing spend is 10x your AE’s OTE then you may still be out of business in 18 months.
This is just another tool and way to look at the reasonableness when setting quotas and not a replacement for your current methodologies. Remember, SaaS metrics are important and can help drive long-term success, but they are not perfect and do not always take into account your specific business.