Here are our answers, including the two that don't flatter us.
Where the signal comes from
One of our callers gets a member of the HRMorning or ResourcefulFinancePro community on the phone and asks them what they're running, how it's going, when it ends, who else weighs in, and what would have to change. Roughly 50 fields come off that call, about 38 of them from the conversation rather than the contact record.
99.9% of those conversations have a recording and a transcript behind them. Ask any signal vendor what share of their data traces back to something a person can sit and listen to.
Nobody in the dataset was recruited, screened or paid. There's no panel to join and no honorarium, which matters because people with a grievance select into surveys and nobody selects into a phone call they weren't expecting.
So the comparison to your existing tools isn't competitive. 6sense, Clay and ZoomInfo tell you who might be in market; this tells you what the people in market said when somebody asked them. Both are useful, and they answer different questions.
The number that shows why inference has a ceiling
We score the same people two ways. Every contact in our community accumulates a digital engagement score from what they read, attend and click. Separately, a caller has a real conversation with some of those same people, which produces its own score.
Across 9,046 people carrying both scores, the correlation between them is 0.048. A 1.0 would mean the two move together perfectly. A 0 would mean one tells you nothing about the other.
Knowing how much somebody clicked tells you close to nothing about what they'll say when you ask. If your scoring model runs on digital behavior alone, that's the ceiling it's working against.
Whether the scoring is a black box
TruSQL™ is a 0 to 100 composite with two halves. Call lead quality contributes up to 60 points and digital engagement contributes up to 40. Bands are 0 to 49, 50 to 69, and 70 and above.
Inside the call half sit the stated buying timeline, the person's role in the decision, company-size fit against your profile, and the substance of the conversation itself.
Worked: a lead at 76 is 46 from call lead quality plus 30 from digital engagement. Both halves are visible per record, so your team can trace any individual score rather than taking it on faith.
Free guide for growth and RevOps
50+ data points a call. None of it counts until it lands.
How the signal actually reaches your reps, why rich data is a burden until it does, and where 6sense, Clay and ZoomInfo fit alongside it rather than against it.
What “calibrated” means for your routing rules
The score is calibrated to your target profile rather than run off a generic model, and it recalculates per vertical. So the same buyer can score differently for two of your product lines, because fit is being measured against two different profiles.
That is worth knowing before you build a routing rule. Any individual score traces to its two halves; what you should not expect is one universal threshold that behaves identically across every line you sell.
How it lands in the tools you already run
Scored, prioritized records land in Salesforce or HubSpot, annotated, at a median of 13 hours against a published 48-hour commitment we've met on 100% of 43,208 delivered records.
A Context Qualified Lead (CQL) is somebody you know well enough to work today, and it doesn't stop at a human. The same context feeds the agents and systems your team already runs, over MCP, the standard AI agents use to query data, on one consent.
How your reps get the three that matter out of the thirty
This is the question most vendors skip, and it's where rich data turns into a liability. A buyer in our community might engage ten of your assets, fifteen articles, two webinars and a recorded call. Most CRMs choke on that, and no rep holds it in their head.
So the scored, ranked version pushes into your CRM and alerts surface what crossed your threshold. The full context stays queryable over MCP for when a rep or an agent needs it. Your team gets the three, not the thirty raw.
One field that should change how you score renewals
63.6% of HR buyers do not know their own contract term. That's 6,049 of the 9,509 who answered. Not “I'd have to look it up.” Did not know.
Worth asking any vendor selling you renewal timing where their date came from, and who exactly told them.
The answer that cost us a headline
Now the part that doesn't flatter us, because a transparency claim you can't check isn't worth much.
Building our learning-management research, we found LMS buyers looking roughly ten times more in-market than HRMS buyers. That's a spectacular number and we could have led with it.
It wasn't real. On an HRMS call the rating question gets asked and answered 91.1% of the time and a buying timeline is captured 7.1% of the time. On a learning-management call those invert: rating 67.0%, timeline 44.4%.
A script that opens on evaluation stage produces a population already evaluating, then reports back that the population is evaluating.
Watch it move as the script changed: 53.9% in market in Q4 2024, 5.8% in Q3 2026, same category and same team. So we published the diagnosis instead of the finding, superseded our own earlier numbers by name, and declined two further findings on the same grounds.
A ten-times finding is almost never a ten-times market. It's usually a difference in how you asked.
If you're evaluating a data vendor, that's the behavior to test for. Ask what they found and decided not to publish.
What this doesn't do
It doesn't replace your CRM, your enrichment layer or your routing. Native integrations are Salesforce and HubSpot only. It isn't another dashboard for somebody to babysit, and it doesn't replace 6sense, Clay or ZoomInfo.
Coverage is concentrated: HRMS 78.7% of conversations, payroll 11.3%, learning 5.9%. Outside those three it's thin, and we'd rather say so now than after you've signed.
Figures from The HRMS Buyer Signal Report and the LMS Signal Note, Rover Insights, September 2026, and from Rover first-party scoring and delivery data. Nobody applied, nobody was paid, and nobody was screened into a panel. Denominators stated per figure.