Archive of posts with tag 'Markets'

On Markets, TAMs, and Agency

September 16, 2022 • #

If you’ve been involved in investing or fundraising activities in the past, you’ve likely heard about “TAMs” (total addressable market), as in “So what’s your TAM look like?” The general idea is to determine a metric that communicates in few words the nature of a given market for a product or service. Investors want to know how a company thinks about its market opportunity (investors generally want large ones), and startup founders need to have a sense for what they can realistically target, build for, sell to, and capture to build a business. You may also have heard of TAM’s cousins, like SAM and SOM (serviceable addressable market and serviceable obtainable market), but let’s stick to the big one here.

TAM is part of the lingua franca of the fundraising process, a term thrown around casually and understood in a variety of ways by investors and operators alike. For those of us on the operator side of the table, calculating your TAM is a necessary part of the company-building and fundraising process. Most founders don’t start with the question of “What’s the biggest TAM I could target?” before starting on their idea. Picking apart the market specifics is typically downstream of prototyping an idea for a job to be done. As a founder you have to think about how to effectively communicate just enough here to be useful to the audience. But how is TAM useful? What is an investor doing with those numbers beyond saying “Yep, this is big enough”?

What TAM analysis is good for

Firstly, a TAM serves to communicate how a company thinks about its target customer. Let’s say you build a team collaboration tool for cross-company projects. Who are the targets? What size companies? Is there a narrower industry focus? Generally the wider your target, the harder it will be to convincingly portray a useful TAM. Simply saying “anyone on Earth could be a customer, our TAM is $500 trillion” won’t persuade anyone, not to mention you’ve got quite the uphill battle to build and sell to such a broad audience. Being narrower isn’t a flaw, it’s a feature.

Which brings me to the second use for TAM: signaling the quality of your thinking about your business. A thoughtful, concise TAM analysis functions not only as a means to convey who your target is, but also as a sign of how thoroughly you’ve thought about the prospects of the business itself, meaning how to tackle a go-to-market. If you can tell a persuasive story about your target market, it lends credibility to why an investor should trust you with their money. A sloppy, or overconfident, or poorly-articulated market description (even if you have a baller product) doesn’t reassure anyone that you’ll be a good steward of the dollars. It’s like some people say about college degrees: they’re useful as signals of wherewithal and commitment as much as they are on their own merits of intelligence. They signal that you see something through to the end, a quality in higher demand by some employers than whether you have a BS in physics or a BA in philosophy.

But enough about the reasons why TAM matters. Let’s cover some reasons it’s not very useful, at least beyond a “qualifying variable” stage.

Then what’s wrong with TAMs?

Well, nothing is “wrong” with them, but they are quite often overvalued. For investors, how you read a TAM analysis and what emphasis to place on it is something to be circumspect about.

For one, markets aren’t static fixtures; they move, expand, and contract all the time. They’re moving targets. Sure, even with an understanding of this factor, a TAM can be useful as a snapshot in time as it stands the moment a deal is getting done. Even well-defined categories move all over the place. It’s more informative to look at it directionally — whether the market is expanding, or if it’s shrinking or being subsumed by an adjacent one.

Some of the best startups are great precisely because they’re targeting a strange combination of submarkets, or they’re in the early stages of creating a category, where there isn’t even an agreed-upon way of describing it yet. Conjuring TAMs for startups like this involves a mixture of alchemy and storytelling that can sometimes be unconvincing in the early days¹.

For example, I’ve been building a low-code platform for field service organizations since 2011, but I’d never heard the term “low-code/no-code” (and we didn’t use it) until the mid-2010s sometime. Had we oriented on the “TAM” of no-code back then, what would we have determined? If you could even define a known boundary around the category at that time, it was probably in the 10s of millions or maybe $100 million total. Now the market is expanding at a CAGR of 30%, headed north of $180 billion by 2030, from around $10 billion in 2019. This market became a known one gradually over time. Initially it was just an undifferentiated, messy mass of people and companies doing things that looked similar to one another with app-builder tools, WYSIWYG editors, and pluggable integrations. Over time the ecosystem became legible and better understood, and now it’s a named category tracked by the likes of G2 and Gartner. But, importantly, some of the best investments in the space happened long before analyst acknowledgement. These are lagging indicators if you’re looking to be the first in, funding the most exciting new ideas.

But an even more important reason why TAMs are dangerous is that even if a market itself is relatively stable over time, your company doesn’t need to be stable. A company has agency. A great product team doesn’t stand still and let their market “happen to” them. Dimitri Dadiomov nails it here — couldn’t have said it better myself:

Part of the calculus with investing in an early product is measuring the future potential of the founders and the company. How a TAM is computed given the current status of a market, or the fitness of a product to said market, is irrelevant to capturing the future potential. A great product team will not stand by and let a better market, or a more focused market pass them by. In the best instances of this, in fact, startups can be singularly responsible for (or close to it) the creation of a market from whole cloth. Or at minimum they can greatly accelerate the realization of latent, obscured demand. Think iPad for tablet devices, Dropbox for personal cloud storage, Salesforce for CRM.

All of this is largely academic if you’re researching your market size to communicate with stakeholders. Take these tips for sizing and refining your definition and run just enough with them to communicate clearly. Just be wary not to internalize too directly the left and right bounds of your market. That’s not to say that defining ideal customer profiles is a bad thing… Far from it! But there’s a difference between “Alex here is the type of persona we’re building for” and “Our market is X big, and defined in this specific way.” Taking to heart what your market looks like without appreciating your own ability to change it can drive fatalistic behavior on the team. If your success in a market is flagging, or you’re making discoveries that the go-to-market angles into a specific customer set aren’t working, you have the steering wheel. You can change course, experiment your way into new markets, and make your TAM simply a snapshot of where you are, not a strict destiny.

But the great angel and seed investors pride themselves on the ability to conjecture about a founder or an idea’s prospects. ↩

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On Legibility — In Society, Tech, Organizations, and Cities

April 6, 2021 • #

This is a repost from my newsletter, Res Extensa, which you can subscribe to over on Substack. This issue was originally published in November, 2020.

In our last issue, we’d weathered TS Zeta in the hills of Georgia, and the dissonance of being a lifelong Floridian sitting through gale-force winds in a mountain cabin. Last week a different category of storm hit us nationwide in the form of election week (which it seems we’ve mostly recovered from). Now as I write this one, Eta is barreling toward us after several days of expert projections that it’d miss by a wide margin. We’re dealing with last-minute school closures and hopefully dodging major power outages. 2020 continues to deliver the goods.

There’s a lot in store for this week, so let’s get into it:

Seeing Like a State

I’ve been deep in James C. Scott’s Seeing Like a State lately, so I wanted to riff this week on Scott’s notion of “legibility,” the book’s central idea. His thesis in SLAS is that central authorities impose top-down, mandated designs on societies in order to make them easier to understand through simplification and optimization techniques. Examples in the book range from scientific forestry and naming traditions to urban planning and collectivist agriculture.

Scott calls this ideology “authoritarian high modernism,” wherein governments, driven by a zealous belief in knowledge and scientific expertise, determine they can restructure the social order for particular gain: higher crop yields, more compliant citizenry, more efficient cities, or crime-free neighborhoods (a dangerous proposition when a “crime” is redefined at will by authorities). He’s ruthlessly critical of these ideas, as evidenced by the book’s subtitle, and presents dozens of cases of top-down-design-gone-wrong, the most extreme case being the Soviet Union’s collectivization program that led to widespread famine.

An interesting factor to think about is how and when to apply intentional design in service of legibility and control. It’s not an all good–all bad proposition, to be sure. Even though Scott levels a pretty harsh review of high modernist ideology, even he acknowledges its value in small, targeted doses for specific problems.

Legibility: A Big Little Idea

I linked to a piece a while back by Venkatesh Rao, the source where I first learned of Scott’s work.

The post is largely an introduction to the book’s themes, but adds a few interesting notes on the psychology behind legibility. Given all the history we have that demonstrates the failure rate of high modernist thinking, why do we keep doing it?

I suspect that what tempts us into this failure is that legibility quells the anxieties evoked by apparent chaos. There is more than mere stupidity at work.

In Mind Wide Open, Steven Johnson’s entertaining story of his experiences subjecting himself to all sorts of medical scanning technologies, he describes his experience with getting an fMRI scan. Johnson tells the researcher that perhaps they should start by examining his brain’s baseline reaction to meaningless stimuli. He naively suggests a white-noise pattern as the right starter image. The researcher patiently informs him that subjects’ brains tend to go crazy when a white noise (high Shannon entropy) pattern is presented. The brain goes nuts trying to find order in the chaos. Instead, the researcher says, they usually start with something like a black-and-white checkerboard pattern.

If my conjecture is correct, then the High Modernist failure-through-legibility-seeking formula is a large scale effect of the rationalization of the fear of (apparent) chaos.

Scott also points out in the book how much of the high modernist mission is driven by “from above” aesthetics, not on-the-ground results. He analyzes the work of Le Corbusier and his visionary model for futuristic urban planning, most evident in his designs for Chandigurh in India and the manufactured Brazilian capital of Brasília (designed by his student, Lúcio Costa).

While Brasília projects a degree of majesty from above, life on the street is a hollowed-out, sterile existence. Its design ignored the realities of how humans interact. Life is more complex than the few variables a planner can optimize for. It’s telling that both Chandigurh and Brasília have lively slums on their outskirts, unplanned neighborhoods that filled demands unmet by the architected city centers.

He juxtaposes the work of Le Corbusier with that of Jane Jacobs, grassroots city activist and author of The Death and Life of Great American Cities. Jacobs was a lifelong advocate of “street life,” placing high emphasis on the organic, local, and human-scale factors that truly make spaces livable and enjoyable. She famously countered the ultimate high modernist visions of New York City planner Robert Moses.

This contrast between failure in legible, designed systems and resilience in emergent, organic ones triggers all of my free-market priors. The truth is there is no “best” mental model here. Certain types of problems lend themselves well to top-down control (require it, in fact), and others produce the best results when markets and individuals are permitted to drive their own solutions. Standard timekeeping, transportation networks, space exploration, flood control — these are all challenges that are hard to address for a variety of reasons without centralized coordination.

Imposing legibility demands an appreciation of trade-offs. Yes, dictated addressing schemes and fixed property ownership documentation do enable state control in the form of taxation, conscription, or surveillance. But in exchange for the right degree of imposed structure, we get the benefit of property rights and land tenure.

Big Tech’s Legible Vision

Byrne Hobart touched on this idea in an issue of his newsletter. (The Diff is some of the best tech/business/investing writing out there, I highly recommend subscribing). He makes the point that wherever scale is required, abstraction and legibility are highly valuable. He calls up Scott’s usage of the term mētis, translated from the classical Greek to mean roughly “knowledge that can only come from practical experience.” The value of this local knowledge is Scott’s counter to legibility-seeking schemes: that practical knowledge beats the theoretical every time.

I like this point that Hobart makes, though, on seeking larger scale global maxima: that a pure reliance on the practical can leave you stuck in a local maximum:

But mētis is a hill-climbing algorithm. If it’s based on experience rather than theory, it’s limited by experience. Meanwhile, theory is not limited by direct experience. By the 1930s, many physicists were quite convinced that an atomic bomb was possible, though of course none of them had ever seen one. Because some things can’t be discovered by trial and error, but can be created by writing down some first principles and thinking very hard about their implications (followed by lots of trial and error), the pro-legibility side has an advantage in inventing new things.

He also brings up its implications in the modern tech ecosystem. The Facebooks, Googles, and Amazons are like panoptic overseers that can force legibility even on the most impenetrable, messy datastreams through machine learning algorithms and hyper-scale pattern recognition. The trade-off here may not be conscription (not yet anyway), but there is a tax. There’s an interesting twist, though: the tech firms have pushed these specialized models so far to the edge that they themselves can’t even explain how they work, thereby reconstituting illegibility:

Fortunately for anyone who shares Scott’s skepticism of the legibility project, the end state for tech ends up creating a weird ego of the mētis-driven illegible system we started with. The outer edges of ad targeting, product recommendations, search results, People You May Know, and For You Page are driven by machine learning algorithms that consume unfathomable amounts of data and output a uniquely well-targeted result. The source code and the data exist, in human-readable formats, but the actual process can be completely opaque.

Functional versus Unit Organizations

While legibility interests me in its applicability to society as a whole, I’m even more intrigued by how this phenomenon works on a smaller scale: within companies.

Org charts attempt to balance productivity and legibility, which often pull in different directions. Organizational design is driven by a hybrid need:

To ship products and services to customers in exchange for revenue and equity value, and
To be able to control, monitor, and optimize the corporate machine

Companies spend millions each year doing “reorgs,” often attributing execution failures to #2: the illegibility of the org’s activities. Therefore you rarely see a reorg that results in drastic cutback of management oversight.

Former Microsoft product exec and now-VC Steven Sinofsky wrote this epic piece a few years back comparing the pros and cons of function- and unit-based organizational structures. Each has merits that fit better or worse within an org depending on the product line(s), corporate culture, geographic spread, go-to-market, and headcount. The number one objective of an optimum org chart is to maximize value delivery to customers through cost reduction, top-line revenue gains, lower overhead, and richer innovation in new products. But legibility can’t be left out as an influence. The insertion of management layer is an attempt to institute tighter control and visibility, a degree of which is necessary to appropriately dial-in costs and overhead investment.

Look at this statistic Sinofsky cites about the Windows team’s composition when he joined:

One statistic: when I came to Windows and the 142 product units, the team overall was over 35% managers (!). But the time we were done “going functional” we had about 20% managers.

Even corporate teams aren’t immune to the pull of legibility. When its influence is stretched too far, you end up with Dilbert cartoons and TPS reports.

Emergent Order in Cities

It was serendipitous to encounter this same theme in Devon Zuegel’s podcast, Order Without Design. The show is a conversation with urbanist Alain Bertaud and his wife Marie-Agnes, an extension of his book on urban planning and how “markets shape cities.”

In episode 3 they discuss mostly sanitation and waste management in cities around the world, but my favorite bit was toward the end in a discussion on how different cities segment property into lots and dictate various uses through zoning regulation. Some cities slice property into large lots, which leads to fewer businesses and higher risk for large, expensive developments. But others, like Manhattan, segment into smaller chunks, resulting in a more diverse cityscape that mixes dining, retail, services, and many other commercial activities.

The shopping mall was an innovation that allowed developers with limited local knowledge to have tenants respond to customer demand in smaller, less risky increments. Malls in Asia notably differ from ours in the west in the breadth of goods and services they typically offer. Here’s Marie-Agnes from the episode:

…Remember the Singapore mall. In Singapore the malls have not only retail, but you find also dental offices, notaries, kindergarten schools, dispensaries, and all sorts of activities. Up to now and in general the concept of a mall in the US is for mainly shopping. You may have food court and some restaurants, but nothing like a real city where you have all sort of business.

Devon points out that this quality might make these malls more resilient to market stress than our retail-focused American versions.

Malls are by no means a modern innovation, of course. Ad-hoc congregations of commercial activity have been around for 5,000 years, evolving from purely organic bazaars of the Near East into the pseudo-planned, air conditioned behemoths we have today. Even in our technocratic culture, planners still realize that the flexibility to market demand is crucial to sustainability. Trying to pre-design and mandate a particular distribution of stores and services is a fool’s errand.

In Byrne’s newsletter, he says “once you look for legibility, you start to see it everywhere.” This has certainly been true for me as I’ve been reading Scott’s work. The deepest insights are the ones that cut widely across many different dimensions.

There’s no silver bullet in how to apply legibility-inducing schemes to any of these areas. But until you’re aware of the negative consequences, there’s no way to balance the scales between legible/designed and illegible/organic outcomes.

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