Shane Wilson's Blog

Since the froth of the 2022 VC Market, the dearth of IPOs means startup founders haven’t had too much inspiration to rip apart an S1 in hopes of finding some directional guidance.  Fortunately for every VC, Banker, LP, and yes, even you startup founder, Figma broke through last week.  Figma’s meteoric first day of trading can be partially attributed to under-pricing and pent up demand for IPOs but the fact remains- Figma has built a very strong business.  Here’s my retrospective on Figma Day interwoven with my recent experiences consulting with startup founders.  

I stepped onto the FIG casino floor this week knowing I was a short-term player.  Having watched Circle earlier this Summer I predicted an early surge in price based largely on a behavioral perspective of the market (pent-up demand and a recent uptick in meme stock activity were sufficient reasons to question the rationality of the Summer 2025 market).  This hunch was quickly rewarded (+20% in ~5min).  Now, sitting on a nice day’s gain the question was: when do you cash in your chips?  

Latter in the day I grabbed coffee with an old startup friend and we started hashing the question out.  As a designer and marketer, this friend is a long-time Figma user and has deep context on Figma’s competitive landscape.  His hunch: 

“Figma could be up to about 40% of the value of Adobe.  Anything higher than that feels wrong.”

Adobe’s Market Cap (Shares Outstanding * Share Price): ~$150B

Figma’s ceiling, 40% of Adobe: $60B

Figma’s shares implied  outstanding: 487M

Figma’s Stock Price Ceiling ($60B/487M): $123.17

This turned out to be a really great benchmark for guessing the top of the market for Figma.  I arrived at a similar conclusion via a different route.  

One of the more valuable concepts I learned in my MBA was how to abstract business performance into easy-to-compare ratios and multiples.  Though I had previously bought and sold stocks, I was worse than a vibe investor.  “Troglodyte” may be more fitting.  Even a rudimentary understanding of multiples gives an unsophisticated investor like me an easy proxy for “cheap” or “expensive.”  

P/E is the tried-and-true ratio to watch, and if let’s say, you are vibe investing on Robinhood, scroll down to the stat sections of your favorite stock and you’ll find it.  It’s a great stat to start watching to build a modicum of rationality into your trading strategy:

Since Figma is brand new to its public journey, we have this nonsensical, negative P/E multiple.  We’ll have to take a page from the startup investor’s playbook: valuation off revenue multiples, or Price-to-Sales (P/S).  Here are some of the numbers Figma highlights on page 5 of its S1 we can work with:

• $821M LTM Revenue
• 46% YoY Revenue Growth
• 132% Net Dollar Retention
• 91% Gross Margin

With $821M in LTM revenue and the stock jumping between $110 and $125 at the time, here’s what revenue multiples we were working with:

Stock Price (FIG)	Implied Valuation	P/S
$110	$53.7B	65.3x
$120	$58.4B	71.2x
$125	$60.8B	74.2x

65x to 70x felt very rich.  So I popped into Yahoo finance and grabbed some P/S figures from the blue chip tech stocks:

Stock	P/S
Amazon	3.45
Apple	7.48
NVIDIA	28.93
Meta	10.88
Alphabet	6.3
Netflix	12.14
Microsoft	13.89
Salesforce	6.3

My hunch that Figma was over-heating felt pretty well-founded at this point.  At $110 per share the market was favoring Figma over NVIDIA by more than 2x.  

Is this rational?  Probably not, but this is a good time to remind ourselves that just because we have an easy-to-compare figure, choosing comparable companies is an art that takes some practice.  NVIDIA builds physical chips that require assembly and distribution.  Figma is a pure-play software company.  The marginal cost for adding an additional Figma customer is virtually 0.  Great margins are a reason investors love SaaS companies.  We could be much more disciplined with our set of comps. That said, these Magnificent stocks gave me enough confidence to say: “At $120 Figma is really expensive.”

With my suspicions largely confirmed it was time to call in the big guns (ChatGPT) to understand how crazy this number really was.  My query “what companies have traded at or around 70x P/S and how long did it last?” returned: 

Company	Peak P/S Multiple	Period	Post‑Peak Outcome (3-Year Change)
Zoom	~178×	2020	–86 % decline by ~2023
Zscaler	~74×	2021	–45 % decline by ~2024
Shopify	~74×	2021	–48 % decline by ~2024
Palantir	~67×	Late 2024	Short-lived; prices reverted lower
Amazon	~66×	1999–2000 (dot‑com)	–88 % decline by 2002; recovery took years

This is another who’s who of great tech stocks and we’re seeing a pretty clear P/S ceiling around 70x.  Zoom, our big outlier, received its eye-popping 178x amid the black swan event that was COVID.  

So I bailed at ~$125 and enjoyed my one-day win.  Trading has since momentarily bounced up around $140, but I’m happy with 40%+ gain.  A less mature version of me would have rode this longer only to be disappointed when the value settled back towards $100 at EOW.

Takeaways

1. If you know the space, intuition can be very valuable.  My friend’s “40% of Adobe ceiling” was was really close to the 60x-70x P/S ratios that were the high water mark for top tech stocks during peak froth moments

2. My stock mathing was pretty rudimentary, but it gave me the confidence to trust my intuition that Figma was indeed overheating.  

How does this apply to startups? 

If any sophisticated public market investors are still reading, they’re probably saying something along the lines of “This guy’s a simpleton, he got lucky.” And they’re probably right.  While my recent foray into day trading had a happy ending, it did inspire some fresh thinking relative to my life investing in and advising startups.  While Figma’s outsized returns on IPO day were undoubtedly juiced by the Animal Spirits the exercise of evaluating public companies and deriving comparable value figures can give startup founders some directional guidance.  Backward induction from public companies that enjoy high-multiples can give your team some scaffolding to build your strategy. 

Let’s go back to the figures from Figma’s S1:

• $821M LTM Revenue
• 46% YoY Revenue Growth
• 132% Net Dollar Retention
• 91% Gross Margin

Metric	High-Multiple (20x-30x) Benchmark
ARR Growth	≥ 50%
Net Revenue Retention (NRR)	≥ 120%
Gross Revenue Retention (GRR)	≥ 90-95%
Gross Margin	≥ 70-80%
Magic Number	≥ .75

ARR Growth Rate

Formula: ARR Growth %=(ARR current – ARR prior) / ARR prior * 100%
Why it matters:

This one’s intuitive- if your revenues are growing your company is growing.  Perhaps one nuance that could be informative to the early stage founder is focusing on the second R: recurring.  If you’re selling a software product and all of your revenue is tied to annual contracts for software usage, you’re all set.  Where this can get a little squirelly is the degree to which your revenue can be attributed to services.  If you collect $100k from a customer but $20k is onboarding and install costs, your ARR bump is $80k.  The one-time $20k install price will likely receive a 1-2x multiple as it is by definition non-recurring.  While big contracts are great, if the revenue is not pure-play ARR your big contracts are padded with empty calories.

Now “what does good look like?” depends a lot on your strategy.  In the pre-COVD days where most startup strategies were “growth at all costs” it was not uncommon to raise crazy rounds that could be applied to gimmicky customer acquisition strategies to juice ARR growth.  (Remember when Ubers were basically free?) Since the COVID bubble burst, VCs are supposed to be more diligent and expect strong growth coupled with financial discipline (discipline will come into focus in some of the following metrics).  Anyway, I found a few charts that provide benchmarks for bootstrapped companies.  These serve our purpose of creating some scaffolding around business metrics.

Source: SaaS Capital

Net Revenue Retention (NRR)

Formula: NRR=(Starting ARR + Expansions-Contractions-Churn) / Starting ARR * 100%

Why it matters:

NRR measures how much you’re growing within your existing customer base.  This is a proxy for product stickiness and customer satisfaction.  If you’re delivering on your promises and building a truly great product your customers’ willingness to pay should increase over time.  On the flipside, a company with strong ARR growth but weak NRR could be over-indexed on quick wins, insufficiently servicing customers post-close. Or it could simply be a sign that your product is not that good.  

Thinking about NRR is a healthy way to make sure that you’re targeting the right customers and your firm is oriented towards long-term value creation.  I suspect NRR is where many of today’s massively funded AI companies will start to fall short.  Anecdotally, most AI users are toggling between multiple tools which are underpriced relative to the value they provide.  As AI companies correct this, their prices will increase and customers will start choosing which tools to churn.  High NRR companies will survive, low NRR companies, even with impressive ARR growth, are headed for the rapture. NRR could also expose some challenges to pay-for-usage models and month-to-month subscriptions (common for consumer products, unusual for most Enterprise products).  Shortening the contract basis means customers have more opportunities to churn.

Gross Revenue Retention

Formula: GRR=(Starting ARR – Churn) / Starting ARR * 100%

Why it Matters:

GRR is the hot rod to NRR’s 4-door sedan.  We get there more quickly but it’s a little more rough.  By ignoring the nuance of expansions and contractions we’re really trying to figure out how many customers you’re losing YoY.  If you have not yet built an upsell opportunity in your product, GRR may be the end-all be-all for your company.  If this is the case, start thinking about what you can do for your customers that can justify upsells and growing NRR over time.

 

Net Dollar Retention

Formula: NDR= (Starting ARR+Expansion ARR – Churn ARR – Contraction ARR)/Starting ARR * 100%

Why it matters:

I think this is really just a fancier way of communicating NRR and GRR which I’d say are sufficient for startup founders to internalize.  I’m including this calculation as it’s what we saw from Figma’s S1.

Gross Margin

Formula: Gross Margin (%) = (Revenue – Cost of Goods Sold [COGS]) / Revenue * 100%

Why it Matters:

As a reminder, COGs are the direct costs of delivering your product including hosting, support, third-party software etc).  In a non-software setting, let’s say a canned beverage startup, COGS would include raw ingredients, packaging, producing labor, and manufacturing costs.  In either case, COGS does not include sales commissions, marketing costs, corporate salaries, and R+D.  

Keeping a keen eye on Gross Margin is a great way to remember the unit economics of each of your customers.  Software is such an attractive business because Gross Margins are typically very high.  If you’re building a different type of company, one with physical products, you won’t be able to achieve numbers like the below and that’s ok (though you will get dinged on your multiple).  In the AI age, this could be a great number to track.  If you’re overly reliant on third-party APIs your value capture will always be threatened by the OpenAIs et al of the world.

Magic Number

Formula: Magic Number = (Subscription Revenue current qtr – Subscription prior qtr)*4 / Sales&Marketing Expense prior qtr

Why it’s important:

So this figure was introduced to me as part of my Figma research.  It’s a SaaS-specific metric that answers “For every $1 spent on sales and marketing last quarter, how many dollars of new ARR am I generating?”  This measures how efficient your GTM function is.  Again, if we look back to the heady days of Uber’s growth-at-all-costs days where CAC got out of control during a blitz to capture users we can see why smart investors would introduce the magic number to their evaluation. 

For startup founders, this is your reminder: buying users is a losing strategy.  It may also influence you to press pause on that flashy ad campaign.  If your sellers and marketers are trying to convince you “You need to spend money to make money” training your team to keep the Magic Number in mind could be the right move. 

Wrapping up

Thinking like an investor may make you a better startup founder.  If you dream of ringing the bell on the NYSE and popping bottles like Figma, backward induction from the metrics that drive great valuations is one way to guide your strategy when you’re in your early days.  In future pieces, I’ll explore tools that can help founders gather data that can help founders quantify strategy decisions.