There are two series – the total number of cumulative customers (top curve) and the number of new customers added each month (bottom curve). The top curve is shaped like an ‘s’ and the bottom one is shaped like a bell. Each month that goes by, the rate of new customer acquisitions increases up to a point, and then declines. You can see the impact that the bottom curve has on the top, because adding up all the incremental customers yields a cumulative penetration curve.

Pop-literature (Moore, Crossing The Chasm) focused on the bell shape of the new customers added curve. Strictly speaking, it’s not a distribution, but the shape causes a degree of comfort with the audience. This core relationship, between a bell and an s, happens repeatedly in nature. If familiarity causes comfort, this is comfortable.

This is about as close to a natural law as you can get in the marketing sciences. And all the explanatory models that underpin it are wild and varied and rich.

To generate a forecast for your SaaS startup though, the model requires a bunch of assumptions, simplified below:

1. When is product-market-solution fit achieved? (Hypergrowth begins)

2. How long does it take the firm to achieve market share saturation between product-market-solution fit achieved, and approaching the saturation point? (takeover time)

Starting with the first assumption – entrepreneurs are horrendous at estimating when hypergrowth begins. Finding 11 from the Genome Startup Report bears that out – “startups need 2 to 3 times longer to validate their market than most founders expect.” (p. 5). That fact alone isn’t too devastating. The Venture Capital industry manages entrepreneurs in such a way that it doesn’t matter nearly as much.

The second assumption, takeover time, isn’t nearly as dangerous. It’s important. It’s esoteric. It’s high marketing science. And if anybody asks, a post will be written.

The third assumption, TAM and saturation, is important. The concepts are interrelated.

The startup ceases behaving like a startup at 51%. It becomes a business. And with that comes buying out the second place competitor and kicking off harvesting that market and all the other typical anti-competitive behavior that goes with full capture.

Setting saturation at 51%, for SaaS startups, is a terrific target assumption because it’s a goal that is reasonable, and it simulates competitive pressure.

In this example 100,000 customers because it made the chart easier to read.

Note that there isn’t any sort of additional detail about who the customers are, if they’re self-referential when making purchasing decisions, or even what the product is. All of that reality lies outside the model.

Let’s assume that the founding team is rational and they truly want an estimate of TAM. One way would be to consider the social network, count the number of known noses from their non-random sample, and project the total number of customers from there. So, let’s say the founder has a rolodex of 1000, and they can count 100 total people, they might project outward that there are 100 * 1000 total potential customers. That’s fraught with bias.

But it’s also where some solid psycho-demographic targeting can start to come in.

Get it right, spend the right way, and the curve responds.