The Pricing Shift Behind the Qualification Shift
A GetMonetizely benchmark study of more than 100 SaaS companies’ public pricing pages, plus 47 pricing-leader survey responses, found 43% of surveyed SaaS companies now incorporate usage-based pricing elements, up 8 percentage points from 2024. Pure per-user pricing as the primary model fell from 64% of companies in 2024 to 57% in 2025, and 61% of companies now run some form of hybrid pricing, up from 49% the prior year, with a platform fee plus per-user structure the most common hybrid sub-pattern, at 41% of hybrid setups.
That is a real, dated, measurable shift in how SaaS companies charge, and it is not incidental to how outbound qualification works, it is the direct cause of why qualification has to change alongside it.
What Usage-Based Pricing Actually Buys a Company
GetMonetizely’s same study found companies using usage-based pricing report 18% to 23% higher net revenue retention than peers on flat or seat-only pricing, and a land-and-expand motion that moves 34% faster. Those are not small margins, they are evidence that letting usage data drive pricing, and by extension qualification, is not just a billing preference, it correlates with meaningfully better retention and expansion outcomes.
That context matters for a founder deciding whether usage-based pricing is worth the qualification rework this guide describes. The retention and expansion numbers above are the reason it is worth the effort, not just a philosophical preference for a more elegant pricing model.
How This Is Different From Asking Whether a PQL Score Is Reliable
VA Horizon’s existing guidance on product-qualified leads argues a usage-based score still needs human verification before a demo gets booked on it, a data-quality argument about the reliability of the signal itself. This guide is scoped to a different, earlier question: not whether a given usage score can be trusted, but how the underlying pricing model changes what qualified is even supposed to mean in the first place.
A company can have a perfectly reliable usage-scoring methodology and still be qualifying against the wrong bar if that bar was copied from a seat-based, demo-request-first motion instead of built for a usage-based one.
What “Qualified” Actually Measures in a Usage-Based Motion
In a traditional, demo-request-first motion, qualification leans on firmographic fit, job title, company size, industry, and a form-filled intent signal. In a usage-based motion, the product itself is already generating a far more direct signal: how deep a team has gone into a feature, how fast usage is climbing toward a plan’s ceiling, and how many distinct users inside an account are actually active.
Those are structurally different inputs. A firmographic filter answers whether an account looks like it should want the product. A usage signal answers whether it actually does, which is a materially stronger basis for a qualification decision.
Where a Human Conversation Still Adds Something Usage Data Cannot
Gong Labs’ analysis of more than 28 million cold emails found that pitching, leading with a product description instead of a genuine question, reduces reply rates by as much as 57%. That finding matters here specifically: an account already showing strong usage signals does not need to be sold on the product, it needs to be asked a few direct questions, who else is using this, is there budget behind an eventual upgrade, what would need to be true for this to become a real purchase.
A usage signal tells a rep an account is worth a conversation. It does not answer those questions on its own, which is exactly the gap a short, well-timed human conversation is built to close.
Building the Qualification Bar Around the Signal That Is Actually Available
The practical fix is not discarding firmographic data entirely, it is weighting usage signal correctly against it in a pricing model where usage is the more honest predictor of actual buying readiness. A qualification framework built for a seat-based motion and simply relabeled for a usage-based one is still measuring the wrong thing most of the time.
Human + AI SDRs can build outreach around a real usage signal, engagement crossing a threshold, a seat approaching its cap, rather than a generic, firmographic-only qualification bar that a usage-based pricing model has already made partly obsolete.
What this means for you
- 43% of SaaS companies now build usage-based elements into their pricing, up 8 percentage points from 2024, a real, dated shift that directly changes what a qualified account looks like.
- Usage-based pricing correlates with 18% to 23% higher net revenue retention and a 34% faster land-and-expand motion, evidence the shift is worth the qualification rework it requires.
- This guide is scoped apart from whether a PQL score is reliable. Its argument is that the pricing model itself resets what qualified is supposed to measure in the first place.
Sources
The external data in this guide draws on the sources below. Figures described in the text as estimates or industry triangulations are directional and are not attributed to a single dataset.
- GetMonetizely, SaaS Pricing Benchmark Study 2025: Key Insights From 100+ Companies Analyzed
- Gong, Does Cold Email Even Work Any More? Here’s What the Data Says