How I made peace with Total Addressable Market

Posted on Posted in Data Driven Culture, Design Thinking, Machine Intelligence, Marketing Science, SaaS, Technology

I was 28 and sleepless when I encountered a marketing version of the logistic function. It was beautiful. It’s one of those things you’re taught about in one context, and when you’re shown it from another angle, it expands your mind. It was like discovering Pi for the first time. I could use it to check the assumptions of a market penetration forecast, and substitute my own estimates for others. I felt empowered and delirious from being able to produce a solid forecast. It became a tool as useful as btau or the crosstab.

There’s a part of that math, a variable called saturation, that worried me from the outset. Saturation is the maximum percentage of adoption that a market will accept. While there are subtle differences between Saturation and Total Addressable Market, they essentially mean the same thing.

Who is to say what TAM is? For years I estimated it. This is where years of comparative analysis training came in handy. If I observed saturation in a set of countries, I could apply that level to other countries that were similar but a bit behind. If those countries were clearly different from others, most often broadband penetration and disposable income, I could make adjustments to the variable. The S could be squat. The S could be giant.

I’d later extend it to demographics, markets, and later still, social graphs. It was just obvious that this is how nature worked.

And as I transitioned into entrepreneurial life, it became much more of a concern.

You can tell I’ve been ever more worried by the amount I wrote about it.

As part on a very long series called Data Driven Culture, I wrote at length about the importance of Total Addressable Market (TAM) estimation. Later I broke TAM down into three variables, and over this last summer, I exposed myself to as many new findings on the subject as I could just this last summer. I tried to reconcile the uncertainty about TAM with heretical optimism.

I concluded that piece with a general stance on experimentation.

If I you don’t know what TAM is, go find out.

Go Find Out

There are several phases in a product or a startup lifecycle. Different books describe different phases in different ways. There’s an era before product-market-solution fit has been identified, and an era that comes afterward.

Most of the real risk comes before the fit. Everything after the fit is scaling. Many entrepreneurs I know really hate all the work that comes after fit has been discovered. The good stuff, to them, is discovering the fit.

(Scaling a startup is no laughing matter either, in general, if you’ve done it once, you’ve done it a million times.)

People fear what they don’t know. I don’t know which products fit with which solution with which market all the time. I don’t fear the dark. I’m hardwired to fear the hungry lion that’s lurking out there in the dark.

I believe in a surface. Along the X-axis are all the problems that exist. Along the Y-axis are all the solutions that could exist. And the Z-axis is the available margin. Most of that surface is dark.

I imagine that there is a lot of margin in replacing lawyers with the blockchain. Standardizing terms, standardizing the paper, in such a way to reduce commercial friction would be very valuable to a lot of people. I don’t really know for sure. It’s dark.

I imagine there’s a lot of margin in training a machine to reply to cold call emails. I don’t really know for sure. It’s dark.

I don’t imagine that there’s a lot of margin in nail saloning in the King West district of Toronto. I don’t really know for sure. It’s really dark.

In many economies, firms really compete for margin. It’s hard to imagine these days with all of our mercantilism and closed trade and FCC regulatory capture. But it’s true! There are some markets that are really, truly, competitive! Sometimes there really is a lion feasting on margin on some of those bumps.

The ultimate way to figure out TAM is to collect data on it.

And there are a few tools for that. There’s fire. Flashlights can be good. Glowsticks. You get it. Anything to illuminate a patch of the fabric is good.

I made peace with the random nature of TAM because can it can be discovered. And you can get a clearer picture of TAM as your explore it. It’s knowable.