Are you overwhelmed by noisy leads while missing out on great ones?

Momo Ong4 min read

It’s a common story in SaaS sales teams today. “We get hundreds of Product Qualified Leads each week, but most of them don’t convert. Meanwhile it feels like we are missing great leads that are right under our noses.”

We did a straw poll of a few dozen GTM teams we spoke to recently. 

The variance in how sales teams are spending their time is huge. 

For example, we recently met the Growth team at a $2b+ SaaS company that’s a household name. They told us they were only converting 1.5% of the PQLs they were selling to. That’s hours and hours of reps reaching out to users that just aren’t ready to buy. Hundreds of sales calls circling the drain.

Worse still, GTM leaders are seeing great users slip through the cracks. “We had many users from a potentially massive account that were on our self-serve product. Usage was really heavy for a period but we somehow missed it completely. Then a competitor reached out with a discount and closed a deal with them!”

The result of all this is that sales teams have low efficiency and conversion. In the midst of the tech downturn, this is a more dire problem than ever before. Sales leaders actually need to convert more leads with fewer resources. Instead, sales calories are wasted on duds while golden opportunities fly by. 

The culprit? Low-quality PQL signals

SaaS companies are eager to roll out PQLs today. In their haste, many have cobbled together a very ‘MVP’ PQL. These PQLs are overly-simplistic, and can be over- and under-inclusive at the same time! The result is that GTM teams stop trusting the PQLs, and the initiative is dead in the water. 

From the companies we spoke to, PQL maturity was the top predictor of the conversion rates and revenue generated from a PQL. 

Typically, a company logs thousands of different types of in-app interactions from users. Examples can include time spent in the app, adding team-mates, or using a specific feature. Knowing which of these signals are actually indicative of buying intent is already hard. Trying to figure out where the thresholds should be is downright mind-boggling. (For example, is it adding 2 team-mates, or 3, that should trigger a PQL?)

PQL systems that are too basic often rely on intuition. They don’t allow for rapid iteration loops, are unlikely to land on the right answer. Instead, it’s the mature GTM teams that can rely both on sophisticated analytics and can experiment with many different PQL definitions that surface the right leads.

How do you know this is worth investing in?

Here are a few ways to think about the potential upside of improving your PQL system.

Missed revenue

  • A devtools company migrated from a very rudimentary set of PQLs derived from intuition, to HeadsUp’s analytics-driven platform.
  • Over 6 months their sales team closed converted 5 more enterprise customers than forecasted, using the PQLs surfaced.
  • With an average ACV of $20k per customer, this represented an additional $100k of revenue booked.

Sales productivity

  • A $5b e-commerce software company looked at its sales team and concluded that up to 50% of time was spent selling to users that don’t have a strong need.
  • Furthermore, reps were wasting hours each week looking at usage data in a messy spreadsheet.
  • After onboarding onto HeadsUp and refining their PQL system, they estimated each sales rep spent at least 1 more day each week on truly qualified leads. This allowed them to hit higher quotas the next quarter without growing headcount at all.

“Before we used HeadsUp, we found that almost 50% of our rep’s time was spent on engaging low-quality leads. Sifting out these false positives was the number one factor that led to our sales productivity increasing.”

VP of Sales, $2b SaaS company

Save months of build time with an out-of-the-box solution 

Most companies are stuck with a basic, scrappy PQL system. It’s understandable – launching a sophisticated PQL initiative is hard.

Here’s what you’d need to build a fully-fledged PQL engine and an internal tool for iterating it:

  • A team of 2-3 engineers and data scientists
  • 3-4 months of fully dedicated engineering time
  • At least 1 engineer to maintain the system long-term and to tweak it based on feedback from GTM teams

Most SaaS companies would rather dedicate such engineering resources to building their core product.

At HeadsUp, we are building a platform that replaces all this. No more lobbying for engineering resources. GTM teams can set up the platform themselves, and get it up and running within a few days. 

Interested in upping your sales efficiency and the quality of your leads? Learn more about what we are building today!

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