I’m sure you’re seeing it too. Up until recently, they provided a cursory review and guidance, but now they’re in lock-step with you every step of the way. Finance business partners are now heavily involved in Quota and Commission development for most SaaS sales organizations. This partnership makes sense. Finance is a neutral party focused on the company’s financial position and efficiency while Sales and Revenue Operations understand the sales organization nuances and strategies.

With an increase in scrutiny over efficiency metrics, tightened budgets, and conservative hiring plans the Quota and Commission process is now a joint endeavor between the Revenue/Sales Operations and FP&A functions. As Sales Leadership and Revenue/Sale Operations continue on this path with Finance it’s important to think like a CFO to build like a CFO. This will help you build revenue plans that the company has high confidence in, are built by ensuring certain levels of efficiency metrics are met, are aligned to industry best standards, and most importantly a plan that the CFO will instantly approve.

Setting Quota

Setting sales revenue quotas should be a science, with just a dash of art. With enough historical sales performance data, you can develop FIVE specific models that will ensure you have set quotas grounded in reality and one that aligns to broader company goals.

Model 1 – Historical Performance

In this model, you’ll leverage  actual historical sales revenue performance. The more data you’re able to pull in, the better. More data points will give you confidence in the data and can provide a more broader and complete view. However, be careful that whatever historical time period you’re pulling data from best reflects what the future will be. It wouldn’t be wise to pull historical sales revenue data during a time period when the teams were selling a product or service that is no longer offered.

You’ll also want to remove outlier data points. You can safely remove the top and bottom 1% of data points. At this stage, you’ll want to be conscious of the tenure of the sales reps in your data set. Perhaps omit data from sales reps in their first three months.

You’ll then want to determine if you’re calculating the Mean (aka average) or Median (aka midpoint). The benefit of using Mean is that it’s easier for everyone to understand. However, there’s a risk that the Mean could be pulled down or up based on the distribution of your data. The Mean can be skewed if you have a lot of data points that are either really low or really high. Lastly, if the number of data points you have is small, then consider using Mean over Median.

After you’ve taken an average or midpoint of your sales rep revenue performance, then you’ll have to use some discretion to make adjustments. You’ll want to consider any seasonality adjustments. You’ll want to consider what are the known changes for the upcoming year that are materially different from the prior year to warrant an increase or decrease to your previously calculated average.

A few examples for when to adjust the results from your historical calculation: A new product or service offering that can potentially increase the basket size of your new business sales transactions. A planned price increase or new bundle that can impact the basket price of your new business sales transactions. Additional sales rep headcount into a fixed territory that actually reduces the potential sales pipeline for all sales reps, which reduces the number of new business sales transactions (on a per sales rep basis). Perhaps there’s a new strategic partnership with another company that will allow your sellers to co-sell together, which can increase sales pipeline and the number of new business sales transactions.

You get the point. You’ll leverage historicals to derive a fair average or midpoint, but then make necessary and informed adjustments (up or down) based on a set of known upcoming initiatives. You’ll then land on a sales rep quota. Hold onto this number for now and keep reading.

Model 2 – Bottoms-Up Productivity

In this model, we’ll be taking a slightly more sophisticated approach. You’ll build a granular productivity model using a variety of input assumptions. 

For most new business sales organizations, you can break down the formula as: 

Average Pipeline * Average Conversion Rate * Average Deal Size = Sales Revenue Quota

 A simpler version could be: Average Number of Deals * Average Deal Size = Sales Revenue Quota. 

You’ll likely leverage the simpler formula if you don’t have historical pipeline or conversion rate figures.

In this model, similar to #1, you’ll want to pull a fair amount of historicals and adjust for outliers to derive fair and accurate assumptions to use in your model. Rather than calculating average sales rep revenue performance (#1 approach), the benefit of this approach is that you’ll calculate granular input assumptions that get multiplied against each other to spit out expected sales rep revenue figures. You’re able to make adjustments up or down to each input assumption to get a sales revenue quota that is more precise than the approach used in #1. For example, if you believe the average deal size should increase, then you can adjust that specific input assumption up and see the impact it has on expected revenue.

The beauty of this approach is you now have a model you can leverage to monitor actual performance against what was expected throughout the year. You can understand if conversion rates, deal size, or deal volume is trending higher or lower than what you modeled. If you find performance is deviating from what was expected, you can quickly see which part of the sales model isn’t aligning. Then you can  either do something about it or understand one of the other levers that needs to be pulled. For example, if pipeline is down and there aren’t clear strategies to improve it, then focus on increasing average deal size to help offset.

With this approach, you’ll land on what potential sales rep quotas could be. Hopefully, they’re similar to what you calculated in #1. Hold onto this number for now and keep reading.

Model 3 – ARR-to-Cost

Imagine you’re paying a sales rep $100,000 in base plus commission provided they close $500,000 in revenue for the year. Therefore, the ARR-to-Cost ratio is 5:1 ($500,000 / $100,000). You simply take the annual recurring revenue you expect one to close and divide it into their base plus commission.

You use this ratio to help you set what the sales rep should cost and what revenue quota you should set based on industry-standard ratios.

  • SMB Sales Reps, which can typically cost $100-150k should have annual quotas of $600-$900k to allow for an ARR-to-Cost ratio of 6:1. 
  • Mid Market Sales Reps, which can typically cost $175-225k should have annual quotas of $875k-$1.1m to allow for an ARR-to-Cost ratio of 5:1.
  • Enterprise Sales Reps, which can typically cost $250-300k should have annual quotas of $1-1.2m to allow for an ARR-to-Cost ratio of 4:1.

Of course your definition of SMB, Mid Market, and Enterprise will vary, as will your costs. But you can still use this framework to ensure your ARR-to-Cost is ranging from 6:1 to 4:1. 6:1 is highly efficient and 4:1 is okay but can make sense for Enterprise sales.

With this approach, you’ll understand at minimum what your sales reps quota should be based on what you’re paying them in salary and commission. Additionally, you’ll be setting a quota that’s aligned to ideal industry best standards.

Now that you have this number, compare this to what you’ve calculated in #1 and #2. In a perfect world, all three of these approaches result in very similar quota numbers. However, what’s likely is that your ARR-to-Cost approach resulted in a higher number than the other approaches. If so, it’s time to revisit approaches #1 and #2 and see what fair adjustments you can make to justify a higher quota. Perhaps when you’re removing outliers you remove the bottom 5-15% of the data to account for really poor performers driving revenue averages down for #1 or driving input assumptions down in #2. Once you clean up the data for truly poor performers not representative of the caliber of AEs you have now, then perhaps this increases the quota numbers to align with #3. The goal here is that you have three quota approaches that you can leverage to validate with each other and triangulate a high confidence quota figure that is also aligned to industry best standards. Let’s now say you have the perfect sales rep revenue quota figure, but you’re not done yet.

Model 4 – Sales Capacity Plan

It’s now time to leverage your sales rep revenue quota from #1-3 to build your sales capacity plan to support the top-line revenue figure provided by Finance.

What this means in practice is you start with your existing sales rep headcount. Some will be tenured (fully ramped) and some will be new (likely on some ramp schedule). You can list them all out in a spreadsheet and forecast out their revenue contributions for the upcoming year based on the sales rep quota you calculated in #1-3. Then you can add future sales rep hires at specific future dates with a quota ramp schedule that also contributes to your revenue forecast. You can then stack all of this together to get a sense of the total new business revenue contributions from your existing and new reps. Ideally, this surpasses the top-line revenue figure provided by Finance.

If your sales capacity plan comes short of the required top-line revenue requirement, then you have a few options:

  • If budget allows for it, you increase the number of future sales rep new hires in the model.
  • If you have the training and tools to facilitate, then you can increase the ramp time (i.e. speed up time to productivity) of new hires.
  • Revisit your sales rep revenue quota approach in #1-3 to see if you were too conservative anywhere.
  • Revisit the “ideal” ARR-to-Cost ratio you landed on and see if you were too aggressive in your assumptions about how efficient your sellers can be.
  • Work with cross-functional partners to develop incremental pipeline and productivity (generate new pipeline sources, new pricing strategy to increase deal size, tools to improve sales rep workflows and processes, new trainings to help improve conversion rates, etc.).


Model 5 – Magic Number

You’ve now calculated the sales rep revenue quota in #1-3 and then calibrated it with #4. It’s now time to perform the final step that ensures you deliver a quota plan proposal to your CFO that makes them blush. You want to now calculate the Magic Number of your sales and marketing organization.

The SaaS Magic Number measures the company’s sales efficiency. Said differently, it answers “For every dollar spent on Sales and Marketing, how much in incremental new business recurring revenue was earned?”. This article clearly outlines the Magic Number formula. 

What you can do is take your annual capacity plan you’ve built in the sections above and calculate the Magic Number of your sales organization once you obtain your Sales and Marketing costs. Your Sales and Marketing costs should include all your S&M salaries and bonuses, all program and performance marketing spend, and any other overhead required to generate sales pipeline and convert them into sales.

If your Magic Number is at least 1.0, then this indicates the company can pay back the quarter in question’s sales and marketing spend via the incremental new business revenue generated across the next four quarters. Generally speaking, a Magic Number of at least 1.0 is a positive sign that the company is efficient, while anything less suggests the Sales and Marketing costs may need some downward adjustments.

Following these five steps should allow you to develop sales rep quotas and a sales capacity plan that supports the top-line revenue plan. And if you’re able to do all of this while spitting out a 1.0+ Magic Number, your CFO will have no problem signing off on your plan.


You’ve built your sales revenue quotas and it ladders up to the top-line revenue plan all with a stamp of approval on sales efficiency. Before you ship these quotas to the floor, you want to ensure the incentives are aligned. The last thing you want is a rock-solid revenue plan to become useless when your sales commission costs are out of whack and negatively impact the sales efficiency you worked so hard on building.

The good news is that most of the work is done. You can leverage the ARR-to-Cost model to back into the effective commission rates you should be paying. 

If your blended ARR-to-Cost ratio is 5:1 in your sales org, then that’s essentially a 20% effective cost of revenue. Assuming a 50/50 pay mix across base salary and variable compensation, then that suggests a 10% commission rate on revenue brought in. Commission rates will be lower if you have a highly efficient sales org (6:1) and commission rates will be higher if you have a less efficient sales org (4:1). Said differently, let’s say you have a 4.5:1 ARR-to-Cost where your sales reps have a 60/40 pay mix. 4.5:1 suggests a 22.22% cost of revenue and a 60/40 pay mix suggests an 8.88% commission rate. Therefore, you can safely pay your new business sales reps almost a 9% commission rate on the annual recurring revenue they bring in.

Now that you have an idea of what the ideal effective commission rate should be, you can build out an effective variable compensation plan. If your incentive design is an afterthought, simply carried over from the prior year, or a simple flat commission rate, then you’re doing it wrong. It’s a real injustice to all the planning you’ve done.

Imagine you’ve sourced the best ingredients, rented a state-of-the-art restaurant grade kitchen, and secured a secret and revered recipe for your favorite dish. And when you go to cook you’re using dull blades and broken spatulas. A perfect plan can be rendered useless if you’re not using the right tools to execute it. Therefore, I place great importance on variable compensation plans and policy design.

This is why I’ve built a comprehensive and practical guide to help anyone build incredibly simple and effective compensation plans. I’ve taken the guesswork out of how to set OTEs, how to set pay mixes, how to structure payout curves, how to design contests, how to formulate bulletproof policies, and so much more.

This is the compensation training every Sales Manager, Ops Analyst, Comp Analyst, Recruiter, and HR Specialist wished they had. I love teaching people, so if you’re interested check out the video below or click here for more details.