You’ve Seen One, You’ve Seen One

Like all CEOs and VCs, I’m a big believer in the power of pattern matching. I just wrote a whole blog post about the limits of pattern matching after hearing the quote above at a board meeting recently. But then a little alarm rang in the back of my head, and realized that I wrote about the value and limitations of pattern matching here years ago with an even better quote from my father-in-law:

When you hear hoof beats, it’s probably horses. But you never know when it might be a zebra.

So rather that rewrite that entire post, I thought I’d just add onto it a bit here with a current example in my head about executive recruiting and hiring executives. But then I realized I wrote that as well – the myth of the playbook. Think I’ve been blogging too long now, or what?

So let’s focus on these two angles instead: first, how do you know when you’re in a situation where You’ve Seen One, You’ve Seen Them All, or if you’re in a situation where You’ve Seen One, You’ve Seen One? And second, how can you protect yourself from a “seen one, seen one” situation when you are approaching the situation as “seen one, seen them all”?

Here are three ways to think about the decode – is this a pattern or is it a one off?

1. The list is long. It’s not actually Seen One… so much as it’s Seen X. The longer the list, the more likely you’re seeing a link in a chain instead of a one-off
2. The item is more everyday/less bespoke. Back to my example in the playbook post I referenced above, hiring a late-stage CFO is bespoke. Hiring an entry level collections person for your AR team is a lot more everyday
3. The item is more specialized. No two companies are exactly alike. No two SaaS companies are alike, but they’re more alike than two random companies. No two B2B SaaS companies are alike, but they’re even more alike than two SaaS companies. B2B SaaS companies with email marketing platforms. B2B SaaS companies with email marketing platforms serving SMBs. You get the idea

And here are a couple tactics to mitigate against calling a pattern where a pattern doesn’t exist:

1. Do a premortem (I wrote about this concept in this post) and ask yourself “If this turns out to be wrong, what are the possible reasons it was wrong?”
2. Build a very small bullet-point level mitigation plan against the top three reasons you come up with

There’s no guarantee that the sound you hear is horses and not zebras. But these indicators may help raise the odds that your pattern match is on point or protect against an unexpected herd of zebras.