Your guide to GTM metrics 2.0

Say goodbye to MQLs, say hello to unified GTM metrics

👋 Hi, it’s Kyle Poyar and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups. Subscribe to join 52,352 readers who get Growth Unhinged delivered to their inbox every Wednesday morning.

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There might have been a time when marketing generated pipeline and sales closed it. But that’s not the world we live in now.

These days marketing, sales, product, customer success and even ops all play a role in acquisition. We’re running paid social, organic social, BDR/SDR campaigns, automated outbound, SEO, lead magnets, free trials, influencer campaigns, field marketing, ABM… heck, we might even be piloting an AI SDR. Anything that could build pipeline is fair game.

Some of these campaigns can be tracked; for others, we’re essentially flying blind unless the customer tells us about it later. A ton of calories go into figuring out precise attribution at a campaign and channel level. And most of those calories are ultimately wasted as attribution becomes a game of who gets credit rather than what’s the best way to influence target accounts to ultimately buy from us.

At a fundamental level, our foundational GTM metrics — like traffic, marketing qualified leads (MQLs) and sales accepted leads (SALs) — make it nearly impossible to understand where we stand with our target accounts and what influences those accounts to ultimately buy. What strikes me is that if we’re ever going to find out what works, we need a unified view of go-to-market (GTM) effectiveness.

Shifting to a unified view of GTM drives actual alignment across teams (no more fighting over lead quality). It uncovers hidden opportunities to accelerate pipe creation. And the best news: getting to a unified view should be more attainable than ever due to the explosion of next-gen GTM tools. Let’s dive in 👇

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What’s wrong with MQLs

The old view of GTM:

1. Marketing generates leads… hopefully in our ICP!

2. Marketing scores those leads, sending the good-ish ones (MQLs) to sales.

3. Sales cherry-picks the most interesting and ignores the rest.

Of course, if we aren’t hitting our numbers, there might be a bit of what Elena Verna calls “MQL stuffing” at quarter-end. (All’s fair in love and pipeline.)

This playbook never worked particularly well for a product-led growth (PLG) motion where users sign up for, try, and even buy products before ever talking to sales. But we adjusted, flagging high-intent PLG users as “product qualified leads” (PQLs) — a variation of MQLs — for sales to upsell into enterprise customers.

The playbook also never made much sense for account-based motions where marketing and sales concentrate their efforts on a set of target accounts. Again, we pivoted, often flagging ABM as another pipeline channel.

These fixes were band-aids. In my view, the MQL paradigm is broken:

• It’s nearly impossible to know whether our efforts are influencing the right people at the right accounts.

• It sets up attribution fights and blame games between marketing and sales.

• It’s a highly subjective and arbitrary stage — and in many cases is disconnected from the buyer’s intent.

• It creates a leaky lead bucket with significant wasted effort and spend.

• It encourages spray-and-pray demand generation tactics — with the logic that we can disqualify or filter out anyone who’s a bad fit.

Why not instead focus our efforts on reaching the accounts we care about where (a) our product works the best, (b) we have the best chance of winning, and (c) where our customers are most likely to renew and expand over time?

If we shifted to an account-centric view, we’d measure:

1. How many accounts are in our ideal customer profile (ICP)?

2. Where are those accounts in their buying journey with us?

3. Which activities are most effective at influencing accounts along that journey?

It might look something like this ⤵️

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A unified view into GTM effectiveness

Nearly every software company I talk to says they want to focus on their ICP.

Why? Put simply, your best-fit accounts are dramatically more valuable than the average prospect. I’ve found that best-fit accounts have:

• Higher win rates (2-3x better than average)

• Larger deal sizes (2x better than average)

• Faster sales cycles (shorter time-to-close)

• Better retention and expansion rates

Staying focused allows you to build better, more differentiated products and then expand from a position of strength.

While nearly everyone wants to be focused, only a select few could show me the data points below. Here’s what it means to be focused on your ICP:

• You know exactly how many accounts are in your ICP.

Many companies define their ideal accounts based on factors like company size, industry, and geography. I’d encourage you to consider other factors, too, such as their existing tech stack or which roles they’re hiring for. This should be easier than ever with tools like Keyplay or Clay, which allow you to build a precise ideal account list.

• You’ve identified the buyers at those accounts and can market to them.

These buyers should be set up in your CRM and marketing automation platforms, ideally along with other important characteristics (ex: industry, location) that allow for tailored campaigns. And they should get pushed to ad platforms like LinkedIn.

Again, we’re spoiled by a wealth of modern tools to help including Clay, Apollo, Lusha, or ZoomInfo (I’ve used Harmonic in the past for access to early-stage startups.)

• You know how many are aware of you.

When asked, they should know who you are. Useful proxies for this might include: they visit your website, consistently open your emails, engage with an ad, or receive direct mail.

Many folks have claimed that email is dead. I’ve found that email can still be highly effective at this stage if done correctly. This usually means: (a) emails come “from” a person, ideally a customer-facing executive, (b) they’re short, conversational, and lightly formatted, and (c) they focus on helping the buyer (ex: sharing relevant content, inviting to an event) rather than immediately pushing for a demo. In fact, a great direct email program can provide powerful insight into what buyers care about (so you can create more of these offers or content).

• You know how many are interested.

These prospects are self-educating on your value proposition and opting into ongoing communication. A subset of example signals: they watch an interactive demo, sign up for a free account, attend a webinar, or view a high-intent website page (ex: pricing page, integrations, advanced features).

Ideally, these buyers become high-intent leads for warm outbound with tools like Unify, Warmly (featured earlier), Snitcher, Pocus, etc. This is usually a place to experiment with more costly and more personalized outreach like 1:1 videos (tools like Sendspark leverage AI to offer the feeling of a 1:1 video on a 1:many basis).

• You know how many are considering a purchase.

This stage is most analogous to a sales qualified lead (SQL), but that’s only one signal of being in the consideration phase. Product qualified leads (PQLs) or self-service teams buyers would qualify, too. There might be a world where buyers are engaging with an AI bot or avatar in a way that signals consideration as well.

• You know how many are selecting a vendor.

Selecting means they are engaging in a buying process. This translates into what’s usually called an interest or proposal stage where the prospect is actively making a decision.

• You know how many have chosen your product, becoming a customer.

This one is pretty self-explanatory!

It’s worth noting that the work isn’t over upon contract close. In fact, 45% of growth eventually comes from expanding existing customers. As the new business motion gets going, the natural next step is to extend this framework post-sale — turning customers into champions and maximizing share-of-wallet within accounts.

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How to operationalize a unified GTM

If you look at GTM effectiveness this way, it doesn't matter whether you invest more in PLG, automated outbound, SEO, paid ads, BDRs/SDRs, or something else entirely.

What matters is that you ultimately reach the right people and turn them into customers. This requires a truly cross-functional operating rhythm and a mindset of objectivity — things that are easy to say and hard to pull off.

Ultimately, you need to run GTM experiments with test and control groups. Measure the results and then scale up what works so that it’s always on. I recommend looking at three things:

• Conversion rate: For every 100 folks you target, how many move to the next stage in the journey?

• Cost-per-conversion: After adding up all the expenses associated with the experiment — including both headcount (time) and program costs — how much does it cost to convert a buyer to the next stage? Plan for a low cost-per-conversion in the initial stages (Identified, Aware) and a much higher cost-per-conversion in the later stages.

• Conversion capacity: Some GTM experiments can be pushed to all of your target accounts. Others apply to a small subset of them. Conversion capacity tells you to what extent you can scale winning experiments to larger and larger audiences — helping make the case for more budget or more hires.

My advice: start with the initial buying stages, run experiments, discover what works, and then move down-funnel.

As you find success, assign an owner for each buying stage and set a target number of conversions along with a target cost per conversion. These owners should double down on what’s already working while carving out time to keep experimenting to either increase conversion or lower the cost per conversion.

Shifting toward a unified GTM approach might be painful at first. In time it can spur a surprising amount of creativity. Marketing, sales, customer success, product, and ops come together to apply their unique expertise toward engaging the accounts that matter. Everyone plays a role in growing the business, everyone can see the impact of their collective work, and everyone is focused on getting 1% better.

Welcome to a unified GTM.