Every career services office running first-destination methodology knows the annual scramble. Six months to hit a knowledge rate that actually means something, a survey that fewer graduates answer every cycle, and a leadership team that wants a number by a date that does not move. Here is what NACE's standards actually ask for, why knowledge rates stall out, what automated scanning adds and does not, and the combined approach the standards themselves point toward.
What is a first-destination survey knowledge rate Knowledge rate is the percentage of a graduating class for which an institution has reasonable, verifiable information about their postgraduation activity, whether that is employment, continuing education, service, military, or still seeking. NACE's First-Destination Standards and Protocols set a minimum recommended knowledge rate of 65%, while cautioning that the goal should be the highest rate an institution can reasonably reach.
What NACE's FDS standards actually ask for
NACE defines the graduating class as everyone who completed a degree between July 1 and June 30, matching the window IPEDS already uses, and covers associate through doctoral level. Data collection is meant to be ongoing across that window, with a final summary analysis completed by six months after the end of the class year.
Outcomes are sorted into standardized categories, and each graduate is counted once, in whichever category best reflects their primary activity. Employed full time is generally 30 or more hours a week, employed part time is fewer than 30, and both split further into sub-categories like entrepreneurship, temporary or contract work including internships, freelance work, postgraduate internships or fellowships, postdoctoral appointments, and faculty positions on or off the tenure track. For every graduate counted as employed, the standard data points are the employing organization, position location by city, state, and country, job title, base salary if full time, any guaranteed first-year bonus, and whether the position was one the graduate already held before finishing the degree.
Why knowledge rates stall
Three patterns explain most of the gap between a school's target and its actual rate.
- Survey fatigue. The same class gets asked again and again across the six-month window, and response volume drops with every additional send, especially once a graduate has already ignored the first two.
- Wrong contact information. The school email a survey was sent to gets deactivated within weeks of graduation, and personal contact details on file are often out of date by the time outreach starts.
- Timing. Ask too early and a graduate has not landed anything yet, so they skip the survey rather than report an unfinished search. Wait too long and the response rate has already dropped before the six-month deadline arrives.
There is a bias risk buried in this too. Graduates who already have a job to report are more likely to respond than graduates still searching, which means a low knowledge rate does not just mean less data, it means the data that did come in probably skews more positive than the class as a whole.
What automated scanning adds, and what it cannot do alone
An automated scan finds and verifies public employment records for a graduate whether or not that graduate ever opens an email. It does not need the graduate to respond, so it is not exposed to the same fatigue or contact-information problems that erode survey response. What it cannot do is capture the data a public record does not carry. Salary, bonus, and whether a graduate is satisfied with the role are self-reported facts. A scan can tell you where someone works and what their title is. It cannot tell you what they were paid or how they feel about the job, and it generally cannot place someone into categories like continuing education, military, or service the way a direct response can.
There is also a practical staffing benefit worth naming directly. A scan runs on a schedule, not a deadline. Instead of one intense six-month push where the whole office is drafting reminder emails and calling employers, a monthly automatic scan builds the knowledge rate gradually across the year, so the survey window that follows starts from a much smaller list of unconfirmed graduates. That changes the job from finding everyone to finding the graduates a scan genuinely could not, which is a more realistic use of a small career services team's time.
| Source | Coverage | Effort | What it proves | Gaps |
|---|---|---|---|---|
| Survey (self-report) | Capped by who responds within the window | Ongoing staff outreach across six months | Salary, satisfaction, category, the graduate's own account | Non-response bias, stale contact info, fatigue |
| Automated public-record scan | Finds records regardless of whether the graduate replies | Runs monthly, no staff outreach required | Employer, title, and a source link for staff to review | No salary or satisfaction data, weak on continuing education |
For Career Services Teams Running FDS Methodology
Find out how much of your class year 2025 knowledge rate is already sitting in public records
See what an automated scan finds for your class of 2025, free, and see the exact source behind every result before you send a single survey.
The combined approach the standards already point toward
NACE's own guiding principles say data can come from a range of legitimate sources beyond the graduate directly, including employer information and other online sources, and that the institution should make good faith efforts to verify what it collects. That is effectively an invitation to combine methods rather than lean on one. The sequence that works best in practice is to run an automated scan first, so the knowledge rate has a floor built on public records before a single survey goes out, and then target survey outreach specifically at the graduates the scan could not confirm. That cuts outreach volume down to a smaller, more relevant list, which tends to lift response quality since the people being asked have not already been asked twice.
Prentus runs an automatic monthly LinkedIn scan plus dozens of third-party sources that verify employment, and AI reviews those sources and surfaces what it believes are legitimate outcomes for your staff to confirm. Customizable email and SMS surveys then collect follow-up data from graduates automatically, which is where salary, satisfaction, and the categories a public record cannot show still get filled in. Neither piece works as a full replacement for the other. Together, they get you closer to a rate that reflects the whole class instead of the half that was easiest to reach. See how this fits with alumni outcome tracking and how it supports the reporting your institution needs for gainful employment reporting, especially with the earnings accountability framework taking effect July 1, 2027.
If your office is planning survey outreach for the class of 2025 and wants to know what a scan would already confirm before that outreach starts, see what it finds for free.
