Last week was about producing a delectable meal out of our ingredients. In our case: great outbound copy out of our building blocks. We obtained some techniques, frameworks and instruction. In sticking with our theme – recipes. But now, we turn our attention to what success looks like. Because without that North Star, it’s hard to know up from down. What we should be aiming for. And even how to improve. Success – for us – will come in the form of metrics. Because at the end of the day, it’s hard to improve what you don’t measure.

Over the past decade, hundreds of founders and startup operators have come to me for coaching on their sales and go-to-market challenges. And I’ve done my best to help. In the series unfolding in the coming weeks, you will not get only the required frameworks to get you thinking properly about prospecting as a founder. But you’ll get the tools necessary to generate consistent pipeline for your company. So sit tight. Tune in. And, as always, be loud with your questions. Here’s your fifth installment.

So what metrics should be measuring, tracking and analyzing? If I could be greedy (as Gordon Gekko once allowed us to be), I’d yell out “all of them”. And while that would be nice and certainly a worthwhile quest, we should start with the essentials. The must-haves. The fundamentals. If we’re going to start with automated email outbound – partly because it’s especially data rich, but also because it’s very scalable – the metrics we should capture and examine are laid out in the slide below.

Now, it’s one thing to have them laid out in front of you. Yet another to fully understand them. What impacts them and how you can have some measure of control over them. Let’s dig in.

Bounce rates are about lead data quality and staying out of spam filters. Years ago, when using Apollo, you had to take your leads out of their system, clean them in a service like Neverbounce before bringing them back into your Apollo account to send out your campaigns. That’s how dirty their lead data was back then. They have made significant strides in cleaning things up in recent years, and their data is now decent. But those were the measures we had to take to prevent our bounce rates from skyrocketing. We now use Amplemarket, which has very good data quality (among other things) and has us worry much less about this issue. A bounce rate of 5% or less is what you should be aiming at for each 4-step campaign that is launched. Anything around 10% should have you ringing the alarm, and have you pulling up your sleeves to fix the crisis immediately.

Open rates are governed by the quality of your subject lines and how good you are about staying out of spam and getting delivered in the main inbox. And you’re going to want to be between 40 to 60% for any 4-step campaign deployed over two weeks or less. If you’re above that range, you’re doing even better. Can you avoid words like “free” in your email body copy – which tends to trigger spam filters? Can you make your subject lines so irresistible prospects can’t help but click on them? Those are just two amongst a sea of questions you should be asking yourself.

Reply rates are all about the engagement garnered by your email body copy. If your emails are getting opened, what are you putting in the email body that gets folks to reply to you. And when I say “reply”, I mean the whole array of replies. From “Please unsubscribe me”, to “here’s my colleague Andrew, who you should chat with”, to “let’s jump on a call next week please”. Any reply is encapsulated in this metric. And we’re aiming to hit 6%.

Interested rate is short for “interested reply rate”. Any replies that indicate interest in your product or service should be tabulated in this metric. (If you want examples of the types of replies that fall under the “interested” category, drop me a line at paul@gassee.com, and I’ll send you a slide containing them). A lot of what is going to dictate an interested reply is your email body copy, but also the call-to-action/offer you put in front of your prospect. We’ve seen this metric jump by getting creative and dangling a lot of value upfront. I’d challenge you to think this way. For this one, you’re going to want to hit 1% as your target. The reaction I get more often: “That’s it, Paul?” Hell yes. More on the impact of that number in a bit.

And last, but not least: we get to the calls booked rate. We’re going to want two thirds of our interested replies to be booked calls. Thus our 0.66% rate. If we get any lower than 50% of our interested replies being booked calls, we’re in deep trouble. And need to take severe corrective measures.

Now if I were to ask you, among these five metrics, if you had to hang your hat on a single one as your measure for whether or not a campaign was successful: which one would you choose?

The answer I get the most when I ask this directly to my clients in session is, “Calls booked rate of course. At the end of the day, we want to start sales conversations. And the first step in that is getting that intro call scheduled.” And while I applaud this results-oriented inclination, it’s not the correct answer. Calls booked rate can have a lot to do with how quickly you follow up with interested leads, or how you might handle objections and questions over email. The purest indicator of whether or not your message is garnering interest from the market is your interested rate.

So now that we have the metrics we need to be tracking, how does one go about building out an automated email initiative? And how do we leverage our metrics in doing so?

Glad you asked. One principle you want to apply when building out go-to-market initiatives is to not waste good leads on bad copy. With that in mind, the smallest yet statistically significant amount of leads we should start with is 100 leads. With that number, a 1% interested rate would mean we’d get one interested reply.

To those clients that do want to take automated outbound email seriously, we recommend they send out 4-to-6 100-lead test campaigns out on a weekly basis. All 4-steps, deployed over two weeks or less. The idea being that you can get data back quickly. Look at it. Figure out what worked and what didn’t work. And improve your go-to-market motion quickly.

Now, out of those 4 or 6 100-lead test campaigns, you might not get any campaign that hits the 1% interested rate for weeks. But when you do get one that hits that marker, you’re going to want to double down on it. Meaning, you’ll clone it, put 200 leads in there, and then launch it the following week. We’re testing for whether or not it can scale. At times, the campaign won’t. It won’t hit that 1% interested rate marker – in this case garnering at least 2 interested replies. But when it does, you should double down on it again. You’ve guessed it: clone it, load 400 leads in it and then send it out the following week. If you get that 400-lead campaign to hit 1% interested rate, you’ve got yourself a golden campaign; a fully scaled up winning campaign that can be sent out week-after-week to harvest interested replies and introductory calls booked.

Here’s the beauty in all of this: imagine a scenario where you have 5 of these golden campaigns. That means that you’re sending out to 2,000 new leads per week – 5 times 400 leads. At a 1% interested rate, that’s 20 interested replies generated on a weekly basis. At the two thirds calls booked, the bottom line is: you’re scheduling 13 intro calls on a weekly basis. Now you’re cooking with gas! That’s the kind of scalability automated email affords you when executed properly.

Hopefully what we’ve just gone through not only showed the practical way to build out an automated email initiative but also gave you a flavor as to how much rigor, discipline and data literacy is needed to develop a full fledged go-to-market measure. You could apply the same principles we reviewed earlier to different channels and techniques. LinkedIn personalized outbound, Google Search Ads for inbound leads, Content Marketing on X, cold calling to book meetings…all of these and more should work in the same way. Know your metrics and what impacts them. Track them closely. Run experiments. And scale the successful ones.

It’s all great having metrics and messaging. What do we do with all of this when we’re first starting? What experiments should we even consider? How should we conjure them up? If you’re thinking about experiment design, you’re on the right track. We’ll cover what goes into good experiment creation in our next installment. See you soon.