Startups are experiment machines. Anyone that doesn’t recognize this puts their venture at risk. What you want to do is to learn about the world around you as quickly as possible. Once you’ve received funding, you’re sitting on a ticking time bomb. The faster you learn about reality, the better shot you have of building a product folks want. And moving it off your shelves and into customers’ hands. All before your runway runs out.

How does one do that?

You start by asking the right questions. The meta inquiry I like to use to elicit all the questions I might have about reality: ‘What would I like to learn about the market?’

When they first started, Netflix’s founders might have asked themselves, ‘Are consumers willing to wait for DVDs in the mail, versus getting them immediately at a retail location?’ Or Brian Chesky, founder of Airbnb, might have pondered, ‘Will hosts post photos online to advertise their spaces to travelers?’ Or perhaps Marc Benioff wondered, ‘Are customer-facing teams willing to entrust their data to the cloud?’ I wasn’t in the room for any of these inquiries, but I’m sure some version of those questions were posed. And these founders had to either find ways to answer them quickly – through cost-effective, feasible experiments – or break down these inquiries into smaller, more bitesized questions they could then get answered through experiments.

I, myself, had a question I was itching to answer when I was running sales at Whitetruffle. I knew we had two valid ways into the companies we sold to. I was dying to know however, ‘What’s the better entry point into the organization: the technical leader or the recruiter?’

I had to design an experiment that was going to help me get my answer.

Here’s what I came up with:

2 X 100-Lead 4-Step Automated Email Campaigns, 1 targeting Heads of Recruiting & 1 targeting VPEs/CTOs

The results came in 2 weeks later:

Campaign A to Heads of Recruiting: Open Rate: 45%, Reply Rate: 3%, Interested Rate: 0%

Campaign B to VPEs/CTOs: Open Rate: 51%, Reply Rate: 6%, Interested Rate: 1%

This first experiment started to show us that perhaps there’s a better way into the organization through the technical leader. We’re not ready to make a final determination yet, as it’s only one experiment. But it’s solid early signal.

So, what ended up happening at Whitetruffle, as it pertains to our entry point question? Well, after running dozens of experiments like this one, we came to the conclusion that both entry points did work well. But if I had my druthers, I’d pick the technical leader over the recruiter any day. Reply rates were higher, sales cycles were shorter, and deal velocity picked up quickly after the first call. Our explanation for those results?

Well, first and foremost: VPs of Engineering or CTO’s were more likely to feel the pain we were eradicating more directly than recruiters. Whitetruffle helped employers source and meet with technical talent – software engineers, UI/UX folks, designers and product people – based on their open job reqs. If a technical leader was to miss a product deadline, their derrières were on the line. And more often than not, tech team constraints were to blame. An issue we’d help with by enabling them to hire technical employees more quickly. Second, technical leaders possess a lot of political capital within their companies. If they’re not the most important department head, they’re tied for first with the revenue leader. Meaning, when they had chatted with me on an introductory call, and then suggested to Recruiting that they should consider us, things moved fast. Recruiting considered the technical leader their internal client, and they’d scurry to serve them. They’d enthusiastically take the next call with me, and we were off to the races. Lastly, I bet we were able to seduce technical leaders intellectually. As a reminder: we described Whitetruffle as a dating site for tech talent. We matched these technical candidates with employers looking to hire them based on a matching algorithm that looked at 50+ core signals. My educated intuition – based on hundreds of conversations with VPE’s and CTO’s – told me that these technical leaders loved that we solved a human problem with a sophisticated algorithm.

Now that we’ve got an idea about how to design impactful experiments based on the questions we want answered from the market, what else can we do to enhance our chances of success?

Well, for any given question you’re looking to get answered, you’re going to want to rank these experiments based on their likelihood of success.

Imagine having 17 experiments listed out to answer the question: ‘what outbound initiative will get us to message-market fit?’ They’re all conjured up and ready to go. The difference between hitting success in one experiment and getting to message-market fit in Experiment 1 versus Experiment 17 could mean months of runway. The survival of your startup could hinge on the way you’ve ranked your experiments. Most founders unfortunately don’t see it this way. And it ends up killing far too many startups.

The earlier you land on a successful experiment, the better off you’ll be. Because you’ll be able to pull hard on a growth lever. If you had the following two experiments to rank amongst the list of 17 we came up with, how would you go about it?

Target: Recruiters

A. LinkedIn Automated Campaign: 3 Steps, starting with a Connect Request and an accompanying note, and two subsequent messages.

B. Automated Email Campaign: 4 Steps, two full pitches in your E1 and E3, with a breakup email as your E4.

Not having read the copy — but assuming they’re both well-crafted, I’d rank Experiment A ahead of Experiment B.

Why is that? Well, my rationale here is based on experience. And go-to-market expertise. (Both things I help my clients with). I’ve seen this movie hundreds of times. I won’t be able to tell you with certainty what experiment is best. Nobody will. Because each startup is unique. With its own set of circumstances: market, product, team, capabilities, etc. But an educated bet will improve your chances dramatically.

I know for a fact that we can reach recruiters via email. I built an automated email initiative that was responsible for 50%+ of our MRR growth at Whitetruffle. Which targeted recruiters among other personas. But I also know that recruiters spend almost every waking hour on LinkedIn. If I’m going to make a bet on an experiment working, the channel is going to matter a ton. And I’d rather wager on the channel my target persona lives on.

We’ve gotten a sense for how experiments should play a role in our go-to-market. Now, how do we go about making the most of these experiments? And why is listening to the market a crucial skill for us to learn as startup operators? We’ll get into that and more in the final episode of our Founder-Led Prospecting series next week. Till then, take care.