But connecting the dots between free trial sign-ups and closed-won deals is sometimes easier said than done.
Here are three of the top takeaways from that conversation, including why:
At its core, every product-led play is a data play.
Free trials, workspace invites, surges in feature usage—these data points act as intent signals for go-to-market teams.
But they’re worthless if those teams can’t access them quickly and easily.
The modern tech stack is a messy mixture of technologies: cloud data warehouses, customer data platforms, identity resolution tools, scoring platforms, and much more. It’s easy to get hung up on the tech in your toolkit.
And while it’s important to figure out the best way for your organization to extract and analyze product usage data, making sure those insights get into the right hands is essential.
“How you leverage the tech stack is a lot less important than getting the data to the right place,” Drew said.
Keep in mind that not all product data is created equal. It’s tempting to want to get eyes on every user activity in your product, but this can make it harder to separate the signal from the noise.
“One thing that we would do at MadKudu is we would really look at the statistical significance of the signal,” Drew said. “If signals were statistically significant in their correlation to conversion, then you do want to surface that, but you don't necessarily want to overwhelm somebody with something that's too voluminous.”
Turn product users into product buyers.
The further upmarket you go, the less likely it is that you’re selling to just one person.
When you break into enterprise deals, odds are you’ll need to convert an entire buying committee.
But that doesn’t mean that you should focus on product-qualified accounts (PQA) at the expense of product-qualified leads (PQL).
“You wouldn't necessarily just go with a PQA or a PQL,” Drew said. “Using those two in tandem and looking at the way in which individual users are escalating their engagement or intent, but also looking at the profiling of individual personas and then tracking that and rolling that up to the account level, is super critical. So you want to really be able to look at it at both levels.”
At the same time, be mindful that while end users are an excellent way to uncover potential opportunities, they’re rarely the decision-makers for larger deals—especially when belts are being tightened.
Go-to-market teams must be prepared to multi-thread their way into deals and build a business case for accounts as a whole, up to and including the C-suite.
“With [...] decentralized purchasing not really being permitted to a large degree right now in terms of the macroeconomic conditions, you definitely see downward pressure to a certain degree on decentralized approaches around product-led growth,” Drew said.
See the people behind the product usage.
Product usage is an excellent intent signal, but it doesn’t prove that an opportunity is worth your time.
Go-to-market teams don’t just need to know who’s active in the product—they need to be able to quickly qualify them.
“If you have individuals or accounts that aren't really a high fit but they're super engaged, you either have one of two scenarios,” Drew said. “You have a false positive […] or you may have potential use cases that you haven't thought of.”
If you keep being led down blind alleys by product data, it may be time to rethink your approach.
Maybe you need to tighten your PQL and PQA criteria. Or maybe you need to expand your ideal customer profile (ICP).
“If you see low fit in terms of ICP combined with high qualification in terms of intent, then that's something that you probably want to surface to your product team and really dig deeper on that data,” Drew said. “You may have a completely different or undiscovered ICP there.”
Product usage data is a powerful signal—but it’s only one piece of the puzzle.
Make sure you have the tools and frameworks you need to see the big picture.
Ready to see how Common Room helps you power growth with visibility into every buyer signal?