Illustrative case study · Healthcare & Life Sciences
Procurement would pay 6% more for data it can take into an audit.
Rival labs hand over raw data for free, so a contract testing lab weighing paid data add-ons had to know whether buyers would pay for the data behind a result, and who would. We put the question to 150 R&D and procurement leaders who had recently chosen a lab, with the report delivered in 14 days.
Illustrative example. The company, scenario, and all figures are fictional. They show UserCue’s method and deliverable format. They do not come from any client study.
The engagement
Illustrative scenariodays, with the report, the data file and a live readout.
R&D and procurement leaders who choose outside testing labs.
90 R&D leaders and 60 procurement leaders.
Three data add-ons priced from 2% to 8% more, compared by buyer role.
A contract testing lab
Whether to charge for data add-ons, and which one to lead with
Buyer decision research, before pricing
AI-moderated interviews with ranked criteria and a premium ladder
Selection criteria, reasons for dropping a lab, add-on demand by role
R&D and procurement leaders at biotech and pharma companies
Headline figures
index score for raw data with a full audit trail, third of eleven criteria and ahead of price
of buyers who have dropped a lab name missing or late raw data as a reason
of procurement leaders would pay a 6% premium for audit-ready data, against 48% of R&D
The question
Will buyers pay extra for the data behind their test results?
Rival labs hand over raw data for free. The lab wanted to know whether an audit-ready package could carry a price, and whether the R&D leaders who use the results value it the way the procurement leaders who sign the contracts do.
What decides which contract lab wins the work?
Where do buyers’ current labs fall short?
Which data add-ons would buyers pay for, and how much more?
What we did
How the study ran, start to finish.
- 1Kickoff
Free raw data, or a paid package.
Can we charge extra for the data that comes with our test results? The kickoff turned it into three questions: what wins a lab the work, where current labs fall short, and which add-ons buyers would pay for.
- 2Design
Two roles, eleven ranked criteria.
Every respondent had chosen or signed for an outside testing lab in the last two years: 90 in R&D and 60 in procurement. Each ranked eleven criteria for that recent selection, then priced three data add-ons on a five-step premium ladder.
- 3Fielding
150 lab buyers, and what went wrong.
Buyers spent a median 31 minutes by voice or text, quant and qual in one conversation, and anyone who dropped a lab was asked what broke. The researcher read all 167 and removed 17 for wrong titles, no outsourcing history or rushed responses, leaving 150.
- 4Analysis
Audit trails sell to the people who sign.
The researcher sorted the criteria by index, which put raw data with an audit trail third, ahead of price, then ran the premium ladder against buyer role. Procurement was readiest to pay for audit-ready data: 67% at a 6% premium, 19 points ahead of R&D.
- 5Readout
67% of procurement would pay 6% more.
The researcher led the lab’s commercial team through the criteria index, add-on demand by role and every verbatim, in a live readout built on the 29-page report and full data file. The audit-ready package became a 6% add-on, offered to procurement first.
What we found
Four findings from the report.
Buyers put on-time results first and raw data with a full audit trail above price. Missing raw data is the most common reason a lab gets dropped. Procurement is the readiest to pay for an audit-ready data package: 67% would pay a 6% premium, 19 points ahead of R&D.
On-time results lead, and raw data with an audit trail outranks price.
Buyers ranked eleven criteria for their most recent lab selection. On-time results score highest, ahead of method validation evidence (86). Raw data with a full audit trail places third at 78, above price per test at 64.
Criteria index, top criterion = 100 · top 4 of eleven · n = 150Missing raw data is the top reason a lab gets dropped.
96 of the 150 buyers have dropped a lab at least once, and 52% of them name missing or late raw data as a reason, ahead of missed delivery dates at 40%. 61% of all buyers have chased a current lab for raw files in the past year.
Reasons for dropping a lab · coded, more than one allowed · n = 96Procurement would pay for audit-ready data.
67% of procurement leaders would pay a 6% premium for an audit-ready data package, 19 points ahead of R&D at 48%. Procurement ties the package to vendor audits, where complete raw files cut the work of checking a lab.
Audit-ready data outsells a results portal and interpretation.
At a 6% premium, the audit-ready data package draws 55% of buyers, a live results portal 31% and scientist interpretation 19%. R&D teams read their own results and see little reason to pay a lab to do it.
Would pay a 6% premium, by add-on · n = 150“If the audit file is complete when the results arrive, I skip all the chasing. I would pay for that before I paid for nicer charts.”Procurement director · Illustrative quote
Illustrative quotes
Three themes behind the numbers.
“The results came on time. The raw files took three more requests, and our vendor audit was the following month.”
“Price gets a lab onto the list. It never decides who wins once two labs can both deliver on time.”
“My scientists read the data themselves. Paying the lab to tell us what it means would be paying twice.”
What the testing lab did
Three moves made with the report.
Turned audit-ready data into a 6% add-on.
The audit-ready data package became a paid add-on at a 6% premium, written into every proposal bound for procurement, where it saves vendor-audit work.
Promised raw files with every result.
Raw files now ship free with each result, aimed at the top reason buyers drop a lab (52%); the audit trail and audit file stay in the paid package.
Kept interpretation free.
Scientist interpretation draws 19% at a 6% premium, so it stays a free conversation with the study scientist.
Illustrative example. The company, scenario, and all figures are fictional. They show UserCue’s method and deliverable format. They do not come from any client study.
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