A Measurement for Product-Market-Sales-Fit (PMSF)
Back of the napkin formulas for tracking the efficiency of sales teams
🙋🏻♂️Hey, it’s Liam from Kleiner Perkins. Commercializing innovation is how I sum up my job in two words. This newsletter is a place to share my experience working across 100+ early stage companies, helping founders and GTM leaders improve the odds of making history.
Measuring someone’s physical strength can be very subjective.
What strength looks like for a grandmother will be vastly different from a professional cage fighter.
With that said, there is a loose framework to determine where someone would fall on that spectrum.
Strength-to-Weight Ratio = Weight / Bodyweight
For example, if the grandmother and cage fighter both perform a deadlift, regardless of their physical makeup, being able to deadlift 1x their bodyweight is considered having “decent” strength. “Good” would be considered 1.3x bodyweight, “athletic” would be 2.25x bodyweight. There are similar ratios for squats, benchpress, etc.
Is this a perfect measurement? No. That’s not the point. It’s useful because of the simplicity, and because the formula generally falls within a standard distribution of what it’s measuring, in this case strength.
Many of the most useful measurements for startups tend to have a similar ethos.
How do you measure product-market-fit? How do you know when you’ve crossed the chasm? When is the right time to launch your second product? Aspects of these can and should be quantified where possible, but there is no perfect measurement for any of them1 .
With that in mind, after working across two venture firms, with over 100+ companies early stage companies, I’ve noticed a rough measurement for product-market-sales-fit (PMSF) for teams where the majority of revenue is created and closed by sales reps (not PLG):
In it’s simplest form, if a sales rep(s) can consistently close a $40K deal in 40 days, a $24K deal in 24 days, a $215K deal in 215 days, etc., this has served as a strong indicator of a repeatable sales motion, or, product-market-sales-fit. In the PMSF formula, this would result in a positive yield equal to or greater than 1.
Now, I can already hear furious typing with 100 counter examples, so let’s unpack the equation a bit. And, remember, the goal here isn’t military-grade precision. It’s to have a GTM formula that’s easy to remember, not overly complex, and fairly accurate.
Since companies have different average contract values (ACVs), and sales teams within those companies have their own unique ACVs (mid-market vs enterprise vs strategic), the value of each day and opportunity is open for isn’t created equal.
Therefore, each sales team would have their own dollar amount that equates to a Day Value, representing what what a rep should get back for their time working a deal. The longer a deal is open in the forecast, the more it should be worth when it closes.
I find using a Day Value of $1,000 to be the most universal application of this framework for early stage B2B companies. The table below has Day Values adjusted for different sales teams.
So, let’s run through some simple examples, assigning the Day Values above to the teams typically associated with each ACV.
It’s worth calling out, getting to a yield equal to or great than 1 will take time, and for some companies there may always be a small delta. Early on, a team’s PMSF yield might be deep in the red, but the key is to start measuring this as early as possible for each sales rep, team, and the company as a whole.
For example, on the way to $1M ARR, the yield will likely be red, but should improve over time. Consistent, or worsening negative yields beyond certain revenue milestones ($5M, $10M, $25M) could signal underlying issues:
Product gaps?
Wrong ICP?
Weak qualification?
Low TOFU?
Market conditions?
Pricing & Packaging?
Conversely, if a team’s yield is equal to or great than 1, it could be a sign to increase sales capacity and hire more reps.
It’s important to note, brining in a new cohort of sellers will naturally have a negative impact on this metric as they ramp. This will be felt more acutely the faster you scale sales headcount, and will take that much longer to get back, so hiring thoughtfully and strategically is critical.
Ultimately, this is just another way for founders, sales leaders, and sales reps to measure the impact of how and where they spend their time. I’m a big believer that you can’t manage what you don’t measure, so hopefully this is another helpful formula in the company building toolkit.
Bonus Reading (including a PLG formula)
I can’t help myself.
Another informal measurement I’ve used with teams is how much each meeting with a prospect should be worth in terms of ACV.
So, if a team has an ACV of $50K, and on average it takes 8 meetings to get to close/won, each meeting should be worth roughly $6,250.
Therefore, if a sales rep is 12 meetings in for a $38K deal (which can easily be tracked in any CRM) they’re either running an inefficient process, or potentially wasting their time altogether.
If an entire sales team is making a similar mistake, the following QBR will be a tough one.
Would it be a GTM post without mentioning PLG?
For B2B companies with a PLG motion, a better measurement would be the Sales Velocity Formula.
This formula provides an estimate of what a day/month/quarter should produce in terms of ARR. So, for example:
Sales Velocity = (50 opportunities x $2,500 x 0.35) / 28 days
Sales Velocity = $43,750 / 28 days
Sales Velocity = $1,562.50 per day
This would mean the team should bring in $1,562.50 per day, $43,750 per month, or $131,250 for the quarter.
This could more appropriate for PLG companies, since the number of opportunities can have broader attribution (inbound, outbound, etc.) and the win rate can also vary between self-serve, sales-assist, or sales-led.
Lastly, you could theoretically use the Sales Velocity formula to determine a Day Value to plug into the PMSF formula. In the spirit of trying to keep this piece short and to the point, I didn’t do that, but if you go that far let me know!
There are times when precision measurement is paramount, like measuring LTV:CAC, NDR, FTE:ARR or my partner Everett Randle’s concept of Operating Yield.