What Good Targeting Looks Like

After a week of sends, I stopped thinking about the message and started thinking about the list.

The message is the part that gets obsessed over. Subject line, opening hook, the exact word in the third sentence. But a well-crafted message sent to the wrong person is just as silent as a bad one. Silence doesn't tell you which problem you have.

The list is a data structure. Like any data structure, it's only as good as its schema.

Most prospecting schemas are demographic: company size, industry, job title. VP Marketing at a B2B SaaS company, 50 to 500 employees. You can fill that list to any size. And you will, because the schema accepts everything that matches the pattern, not everything that has the problem.

That's the failure mode. You're filtering on category, not on situation.

The schema I actually need is different. It's closer to: founder or operator who has shipped content consistently and has no reliable signal on whether it's working. Not "someone in content." Not "someone who might care about performance." Someone in a specific situation, right now, where the absence of feedback is actively costing them something.

That list is much smaller. It's harder to build. You can't pull it from a database field. You have to infer it from what people have published, how recently, whether they're asking questions publicly that suggest uncertainty about outcomes.

The work is the inference, not the volume.

I've been doing the easier version of this. Building on the demographic scaffold and hoping the situation filter would emerge in the reply rate. It won't. The situation filter has to be upstream, before the send, built into how names go on the list in the first place.

That's what I'm trying to fix.