PLG is back? With the switch to usage-based pricing since the explosion of AI SaaS, we've seen the need to nail this motion again. But in practice, only a handful of teams are really doing this right.
At Cargo, we've observed the biggest Alpha for PLG-led sales motions Product-led growth is often misunderstood as effortless: users sign up, explore, and magically convert.
But behind every signup lies noise—bots, junk mails, curious browsers, and personal emails clutter your funnel.
In this chaos is sleeping money. Hidden pipelines. Revenue opportunities masked behind Gmail addresses and poor data.
Why Most PLG Funnels Leak Revenue#
Too many signups, no prioritization
Most PLG companies have made it incredibly easy for users to sign up—so easy, in fact, that they've created a new kind of problem: too many leads.
Without clear filtering, that flood becomes chaos. If every signup is treated as a lead, then in reality, no one is. A bot using a fake domain, a student exploring out of curiosity, and a decision-maker from a Fortune 500 company will all be treated the same.
Research consistently shows that adding sales assistance to product-led motions can triple conversion rates.
But sales resources are limited.
Without filtering and prioritization, your systems and teams are overwhelmed and most likely spending their time chasing the wrong leads. Meanwhile, the few real buyers—the ones with true intent and purchasing power—get buried under the noise, missed entirely, or contacted too late.
Signals are split across tools
PLG-led companies, by their nature, deal with complexity: thousands of weekly signups scattered across different tools, disconnected systems, and mismatched data layers.
Your product usage data lives in Amplitude. Your firmographic insights in Clearbit. Signups flood slack alerts - and your CRM only captures a fraction of the user journey. All this convolution lets real buyers slip through unnoticed, and leaving revenue on the table.
Almost every team enriches leads, but enrichment alone isn't enough. It only works when it's layered with context—what the lead has done across your product, what content they've engaged with, what signals they're sending.
Without it, even the best enrichment tools are blind. If someone signs up with a personal email like sarah.dev@gmail.com, and you don't tie it to usage data or domain hints, you have no way of knowing if Sarah is a student—or a senior engineer at Stripe.
As a result, your sales team is left guessing as to which lead to prioritize and manually try to stitch together disparate workspace, user and account data.
CRMs aren't built for this volume (data warehouses are)
A lot of teams make the mistake of dumping all incoming data into the CRM. But poor quality leads should stay outside of the CRM.
Noise will never help sales reps spend their time chasing the right set of leads.
Moreover, if you dump everything in the CRM, you're most likely not properly enriching account and contact properties.
Instead, a CRM is best used as a subset of a proper single source of truth system, like a data warehouse. Namely, when a sales rep logs into the CRM they should only see a curated shortlist of leads worth their attention. (and already allocated to the right person ideally)
CRMs were never built for the sheer volume that PLG creates. If you push every signup directly into Salesforce or HubSpot you will quickly turn these systems into bloated landfills—filled with junk leads, irrelevant Gmail addresses, duplicates and bots that bury your high-value prospects.
The Product-Led Revenue Engine (Built Right)#
Automatically enrich and classify on sign-up
The moment someone signs up is your first and best chance to learn who they are. But enrichment needs to happen instantly—and with the right logic.
Here's how it works when done right:
Filter noise at the gate
Up to 50% of free trial signups may be fake accounts: including bots, repeat signups, and spam. There are distractions for the sales teams and skew conversion metrics (and probably your infrastructure costs).
Block junk and personal emails before they ever reach downstream systems. Not every signup deserves a place in your systems. Bots, junk emails, and personal accounts (think Gmail, Yahoo, Outlook) add clutter, not value. If you don't block them early, they flood your CRM, your Slack alerts, your enrichment queues—wasting time, budget, and sales reps' bandwidth.
At Descript, the first Play that the team built on Cargo was an AI-based email classifier and a blocklist of known fraudulent domains to get rid of these irrelevant sign-ups.
The AI node automatically evaluated whether a signup was coming from a personal or work email, scoring it in real-time based on domain patterns and structure.
In parallel, they maintained a constantly updated blocklist of disposable, temporary, and obviously fake email domains—catching thousands of junk signups instantly. Together, these two layers helped weed out almost all non-verifiable signups at the gate.
Classify every lead and run waterfall enrichment
Is this a business email or personal? Does the domain match a known company, or is it a dead end? Is this a lone user exploring on their own, or the first signal from a larger team that could become a major account? These distinctions matter. Without them, you enrich every signup the same way whereas you can optimise your coverage rate and budget by classifying these emails upfront.
Today, there are so many different enrichment data vendors, each with their strengths and dark spots. How do you know which one is best suited for your prospect before you get locked into a contract with one? You're far better off chaining multiple providers in a waterfall logic: i.e. ping multiple providers like Clearbit, Apollo, or your internal datasets in sequence, and stop once you find a match.
This is why waterfall enrichment is the new standard, as you can't rely on the capacity of a single provider. Earlier, companies would be hesitant to go this route because it takes quite a bit of work to set up for scale in-house (rule sets, rate limits, credit capacities, API maintenance, etc.).
Today, however, it has become so easy to set set up a custom waterfall tuned to your business in a low-code environment (e.g. a Cargo tool): optimizing by geography, market segment, and the mix of personal vs professional emails you see.
At Descript, they saw a 30% boost in coverage—without spending a dollar more—just by creating a custom enrichment flow using a Cargo Tool tailored to their audience. The result: more real buyers surfaced, faster, with the same budget.
Score and prioritize with PQL logic
Not every signup deserves a spot in your CRM. That's what Product-Qualified Lead (PQL) logic is for: to filter noise and spotlight the users who actually matter.
Your PQL isn't just a usage metric. It's a combination of:
- Firmographics: Do they work at the right kind of company?
- Behavior: Are they using the product in a way that signals intent?
- Fit: Are they in a role that buys your product?
It's only when a lead and its usage justifies being a PQL that a contact and account should be created in the CRM.
At Weights & Biases (WandB), the team realized that high-value users were slipping through because leads weren't consistently matched to accounts. Using Cargo, they built a smarter system that not only classified users but connected them to the right accounts based on product usage signals. They made sure no engaged user went unnoticed. The result: better coverage, faster identification of upsell opportunities, and more revenue captured from free users who might have otherwise fallen through the cracks.
Allocate the right effort for each lead tier
The best GTM systems don't just identify good leads—they decide what to do with them.
High-scoring leads get assigned directly to a rep, along with everything they need to act fast—account-level research, a summary of user behavior, and suggested outreach angles based on product usage, industry fit, and intent signals.
At Descript, the team designed a system that tailors effort based on lead tier.
For high-intent leads, they use tools like Hightouch and Cargo to surface enriched user profiles directly to sales reps, complete with behavior summaries and intent signals.
For mid- and low-intent leads, they combined product usage data with Octave and Instantly to run 1:many nurture sequences—automated, but still personalized.
This hybrid approach lets them scale touch without sacrificing relevance, ensuring every lead gets the right follow-up at the right time. See the an extract below from our talk with G Cabane on how Ramp thinks about this.
Enable your sales team with the right information
Hot take: workflows without sales enablement is just building plumbing. You've enriched the right leads, routed them to the right reps, and synced them into the right tools. Now make all that plumbing pay off in terms of opportunities.
Give reps instant context: Surface a snapshot of everything that's happened on the account—key product events, enrichment highlights, and recent touchpoints—before their next meeting.
Arm them with proof. Pair that context with relatable customer stories and talk tracks relevant to the prospect's segment or use case or behaviour.
Need more inspiration? We built a playbook on how to crack the enablement piece.
Takeaway#
PLG isn't a "set-it-and-forget-it" funnel. It's a system, and systems need to be designed by GTM engineers.
When you orchestrate it right, you don't just get more revenue. You get faster sales cycles, cleaner CRM data, higher rep productivity, and trust in your funnel.
Cargo helps teams design that system — by connecting your signals, automating your workflows, and sending the right leads to the right place, instantly.
👉 Read more: Unleashing Sales Potential: The Synergy of Sales Ops & Enablement
MaxApr 30, 2025
