Thread by @JaredSleeper

Yesterday, I posted an outline of my public SaaS framework. As promised, I'm going to go through each of the five factors in detail with a mini-case study. First up: what is TAM saturation and what are the warning signs for a SaaS company at scale? 🧵👇

1/ First, some words on TAM. These three letters are essential to SaaS investing and yet are often glossed over. Shockingly (to me at least), many public companies pitch themselves to investors using poorly supported estimates from third-party research reports. 🤯

2/ This is remarkably useless given the importance of the exercise. I remember when folks thought ServiceNow was "running out of TAM" because a big research firm showed it had >50% share of ITSM. As it kept growing, suddenly the whole category ~reaccelerated~ to higher growth😂🪄

3/ I wrote a piece on how to calculate TAM and communicate it to investors a while ago. I wish more companies would use this approach rather than citing a third party in their IPO roadshows/investor presentations with no logic to back it up.

forentrepreneurs.com/calculating-ta…

4/ At any rate, I think of TAM as: "total market" X "addressability." I define total market as"if everyone who would be better off using the software did, what would ARR be?" and "addressability" as some estimate of the share of those customers the company can ultimately win.

5/ Addressability is a measure of sales & marketing efficacy/scalability and competitive dynamics. Competition is straightforward: if there are high-quality competitors, whatever share of the market they end up with isn't addressable.

6/ Sales and marketing efficacy/scalability are trickier. Software is a "way of being" as much as a product. Evangelizing ways of being is hard- even if you can reach every potential customer, some of them won't "see the light" and invest the energy to move to a better state.

7/ More on competition in another thread. Happily, I rarely worry about sudden changes in S&M efficacy/scalability. I could imagine a situation with two customer segments where one is dramatically harder to sell to, creating a sort of cliff, but I haven't seen that in the wild.

8/ "Total market" is very hard to estimate in some cases (hi, $TWLO!) and easy in others. Helpfully, the easier it is for a company to name/find all of its potential customers, the more likely it is that "total market" becomes a constraint. If the analysis is simple, that's a🚩

9/ Let's look at a case study of a SaaS company hitting a TAM snag: Instructure ($INST). Its flagship product, Canvas, is a cloud-based learning management system (LMS) for colleges. The product is universally acclaimed and dominates the market.

10/ The higher ed market is quirky in that most of the ~4,000 institutions are open about their LMS. Some industry observers keep a running database of which LMS each institution uses, which allows granular visibility into market share. As an investor, this is really neat. 😊

11/ Sadly, the fact that this existed was a yellow flag. When I first looked at INST in 2015, LMS implementation data looked like this (note: this is NOT market share). There isn't a public version of this chart past 2014, but iirc Canvas was eventually ~70% of implementations.

12/ This was so impressive that it was actually a problem. My model had net new ARR from INST's core business (domestic, higher-ed Canvas) more than doubling over time. With the "capture rate" already over 50% in 2015, that would be tough.

13/ For my model to be right, industry-wide implementations would have to grow over time. But the market wasn't growing and the data showed the rate of implementations wasn't either (actually, they ended up falling over 50% from peak!).

14/ INST kept adding customers and is still a great business, but peak adoption of core Canvas is in the past. In retrospect, the steepest part of the S-curve was right around 25% market share. I'd still bet on the company getting well north of 50% share, but it will take time.

15/ How can we apply this to other companies? First, realize that, whether observable or not, many cos have a similar dynamic and a thorough investor should have a sense of what these charts would look like for any SaaS company they own. Here's my framework:

16) A company's new sales in a given period are theoretical "opportunities" for new customer wins multiplied by "capture rate". We can decompose capture rate into "see-through" (share of opportunities the product was considered for) X "win rate" (share it won when considered).

17) Some companies control the flow of opportunities because they have outbound sales motions and "push" their product. For others (like INST), opportunities are driven by forces internal to their target customers leading to "pull". Pull is cheaper, but harder to control.

18/ It's crucial to have a sense of the flow of opportunities⇒ is the pace of customer decision-making increasing? Flat? Decreasing? How much influence (if any) does the company have on that? What share of the remaining "total market" is up for grabs in a given year?

19/ Then, we need to understand the "capture rate" of those opportunities. The best-case scenario (all else equal) is that opportunities are growing and both "see-through" and "win rate" are low, leading to a low capture rate.

20/ That may seem counter-intuitive, but remember we already have metrics like growth rate and sales efficiency that tell us how well a company is doing. A company growing 100% with a low capture rate has a lower risk of hitting a TAM wall than one with a high capture rate.

21/ In the case study above, by 2017 INST was probably seeing almost 100% of higher-ed LMS deals and winning ~70% of them for a 70% capture rate. We knew the flow of opportunities wasn't growing much, and also that there was a TAM ceiling ahead.

22/ I focus on opportunities/capture rate here because they are usually easier to observe than total market size (INST is an exception). Investors can survey customers, speak to consultants, etc. and get a sense of the quantity/velocity/win-rate of deals. Market sizing is art.

23/ With this framework, we can gut-check a model and evaluate the risk of a TAM constraint. Look at forecasted net new ARR- if it is doubling or more (it probably is if you're bullish!!), what does that imply for the level of opportunities and the capture rate?

24/ If they don't seem to add up, you may have just discovered a looming TAM issue. Happily, most SaaS companies have avoided this. There's just so much room for software to eat more of the world. 😀

That's all for now- stay tuned for the competition 🧵sometime soon.