Tom Tunguz

Luckily, SaaS sales compensation has been largely figured out and documented. I implore you to NOT benchmark numbers to inc.com guides or sales jobs in general.

If you do, you will likely frame your salary targets poorly against the wrong baselines and your reps will figure this out and quickly get jobs at other SaaS companies.

"Sales Rep" is a bit ambiguous to SaaS orgs which are usually made up of "Sales Development Reps" (entry level, openers, demo-bookers) and "Account Executives" (demo-performers, closers, opportunity managers).

I would search for the title "Account Executive" in your region on Glassdoor for hints on base salaries and total compensation.

Angellist is also a great place to get a sense for how similar start-ups are positioning themselves in job listings. (maybe look up your competitors)

Sales salaries are typically presented in OTE (On Target Earnings) or essentially, Base Salary + Commission for the year. OTE is usually around 20% of annual quotas. SMB annual quotas are usually in the range 300k - 500k quotas = so $60k-$100k OTE packages.

Best Practices:
Cost of living should guide a guaranteed base salary. Don't be stingy on the commission, read the Jason Lemkin article at the bottom to get a sense for what those %'s should be.

Hyperbolizing a bit, but you generally get what you pay for, no SaaS company ever went under because it paid too much commission, assuming it did so with cash-flow in mind and had minimal churn. Also, uncapped commission…

Props to succeeding on growth. However, pretty sure your success doesn't mean that one-time purchase models are unreservedly better than subscription or that subscription products are subpar.

Pricing and monetization models are often strategic decisions that can have a number of different short and long-term implications for a company and for its product development.

@maxpupmax has a great point about aligning incentives with referral structures.

@compumike has another good point about b2b customers many times preferring a recurring subscription in return for certain enterprise type features like Support and SLAs.

on the P&L side, @lukebennett makes a great point about ongoing cost with cloud services. This is generalizable to any sort of ongoing cost incurred to provide the service (e.g. support, guarantees on service, security, or even processing costs in ecommerce).

Additionally, subscription contracts provide some means making cashflow predictable as some flexibility in creating more immediate cash flow via prepayments or in letting companies borrow against existing contracts. Tomasz Tunguz (http://tomtunguz.com/) has a bunch of fantastic posts on this.

Finally, on the product side, I'd actually argue that in many cases a subscription model actually better aligns incentives between customer and company. In one-time purchase models, new customers are king. In subscription models, churn is a huge concern and customer success eventually becomes paramount and I think…

It's all dummy data which makes the metrics sometimes look a little wacky, but I tried to model everything based on real-world examples I've seen.

The Customer Acquisition Cost (CAC) is high, but so is the ARPC (Average Revenue per Customer), so you can't just look at the one number in isolation to see if it's realistic or not.

In this case, I'd look at the CAC Payback time and the CAC:LTV ratio to see if these acquisition costs would be realistic in any kind of a company. The payback time climbs from 6-8 months to 19 over time, which is a pretty clear indication that this is unlikely to be a bootstrapped company. They just wouldn't have the capital to sustain it long-term, unless they sell mostly multi-year deals paid in advance.

For a VC Company, 19 months would be on the high end for sure, but not unheard of. Dated, but still great article from Tom Tunguz[1] says the SaaS median is 15 months, indicating there are a lot of companies with longer payback times.

Finally, if you look into the future projections you'll notice that the payback time grows to 26 months. Unless you have clear understanding why this should happen, this is more likely an indication that your forecast isn't very good. (Which, I'm realizing typing this, is quite ironic). Either your revenue growth is not fast enough in your forecast, or if it is the maximum you think you can do, your S&M spending is too high and your business just isn't sustainable. Or something else.

[1]…

The funding market dropoff for Series A+ startups is a big deal for startup employees assuming they can keep high 6 figure salaries at vc-dependent companies where, in recent years, fundraising has far exceeded revenue. Ex: https://tomtunguz.com/fundraising-seasonality/ . Keeping large salary for many will be HARD, eg, seeing folks have to go to wall st, and junior (bootcamps etc) was already hard without this.

We are still waiting for the other shoe to drop. Many high-burn co's did not cut, and of companies that did a round of layoffs, most followed a statistically suspicious trend of 10-20% that suggests a bunch should have done more but didn't. With hiring freezes everywhere.. a lot of pain for job seekers even before more cuts. Worse, even a regular 2-year downturn would flush a lot of startups as fundraises are generally for 12-24mo payroll, and many raised with historically horrible fundamentals (vs valuation) over the last few years.

I'm not saying end of world, but I wouldn't want to be a new grad at many seemingly shiny series b/c startups right now. When I was a new grad, I wouldn't have even known the right questions to figure out if my unicorn was really a donkey.

This is a really nice summary of some of the technical components required. You also need to know how to do different kinds of analysis to answer different kinds of questions. A few more things:

0. Scientific method - probably true for all domains. Not really a kind of analysis, more an approach to doing analysis.

1. Cohort analysis - used in aquisition and retention analysis.

2. Model building - used in all kinds of financial analysis.

3. A/B/... testing - determining the difference between 2 or more populations.

4. Exploratory - understanding the relationships in your data to develop intuition about it.

There are plenty of analysis techniques in use. You can learn more about these and others if you survey blogs and other literature. One that I find interesting is Tom Tunguz. He has a particular theme, but his analysis is very good. The methods and way of thought are transferrable.
http://tomtunguz.com/

Tomasz Tunguz wrote a nice piece about the 2-part tariff: http://tomtunguz.com/three-part-tariffs/

Actually, he mentions that academics argue that 3-part tariffs may be the most optimal.

From his article:

"Linear Pricing (LP) - Each analytics event costs $0.10."

"2 Part Tariff (2PT) - The analytics software has a base platform fee of $10,000 and each analytics event processed by the system costs $0.10 more."

"3 Part Tariff (3PT) - Again, the software has a base platform fee but the fee is $25,000 because it includes the first 150k events are free. Each marginal event costs $0.15."

It can be a pain to implement "tariffs" on the billing system side, but it can be well worth it in the end. Props to you all at Canny for pushing through.

BTW, if anyone is interested in building tariff/metered billing into their product, you might want to check out Cheddar's Billing API:
https://gtchdr.com/2CjFrpv