At Galadriel we ran roughly thirty go-to-market experiments over four months and ended up at a hundred and eighty thousand dollars in monthly recurring revenue from a standing start.
A handful of those experiments produced almost all of the result. The useful part of this story is not the handful. It is the rest.
What counts as an experiment
Most teams do not run experiments. They try things. The difference is that an experiment has a hypothesis written down before it starts, the number of accounts you will test it on, and a decision rule for what you do with the answer.
Without those three, you get a month of activity and an argument about what it meant. Somebody remembers it working. Somebody else remembers it not working. Nothing is learned and the loudest person decides.
It does not need to be elaborate. One line of hypothesis, the number of accounts you will run it against, and what you will do at each outcome. Fifteen minutes of writing before a two-week test.
The pattern in what worked
Looking back at the ones that moved the number, nearly all of them were positioning changes rather than channel changes.
Narrowing the segment did more than any new channel. Going from a broad technical audience to one specific type of team with one specific problem changed reply rates more than any amount of sequence optimisation.
Changing the reference point mattered enormously. Not what the product did, but what we compared it to. Buyers understand new things by analogy to old things, and we were letting them pick the analogy themselves. Picking it for them changed how the conversation opened.
Pricing turned out to be a messaging problem as much as a revenue one. The number signals what kind of product you are. Ours was telling people the wrong thing.
Founder-led outbound against a specific signal beat volume by a wide margin. Fewer accounts, properly researched, with a reason for the message to exist.
The pattern in what did not
Broad outbound. Predictably, but we ran it anyway because it is the thing everyone tells early teams to do.
General brand content. Nobody was looking for us, so there was nothing for the content to catch.
Adjacent-tool partnerships. These look excellent on a slide and almost always underdeliver at this stage, because the partner has no real incentive to sell for you and their team has never heard of you.
Discounting to close. It worked exactly once, on a deal that then churned, which is the usual shape of it.
Carrying the old pipeline through a pivot. When the product changed underneath us, the instinct was to keep the accounts and re-pitch them. They had been qualified against an old hypothesis, and they polluted everything we learned next. Starting the list again is painful and faster.
The thing nobody says about this
Twenty-four of thirty failing is not a bad result. It is the normal result, and it is why the discipline matters. If you are running three experiments a quarter you cannot afford a hit rate like that. If you are running eight a month you can.
The constraint on early-stage go-to-market is almost never ideas. It is cycle time: how fast you can get from hypothesis to a clean answer. Everything we did to compress that, mostly by using models for the research and drafting, raised the number of swings we got.
Number stalled and nobody agrees why? A GTM diagnostic is the quickest way to find out.