A note before a call · Higher-ed / OPM · Prepared for iDesign
I would rather start with a short call to hear what iDesign actually needs. But you are probably swamped, so I did some homework first: two rough prototypes on public higher-ed data and synthetic numbers, sketching a problem the OPM world is wrestling with now. Not your data, not a finished product, just a faster way to show what working together could look like than a blank-page call.
iDesign pioneered the unbundled, fee-for-service OPM model: paid for the work, not a cut of tuition. That honest model wins renewals on one thing: showing a partner, in numbers they trust, that the program enrolled the right students at a defensible cost. The catch is structural. The cheapest conversion a partner program can measure is the inquiry, and a scorecard built on cost-per-inquiry flatters everyone and proves nothing. The partner owns the enrollment P&L; iDesign owns the renewal. So the question I would want to ask on a call: across the portfolio, can iDesign demonstrate cost-per-enrolled-start with a method it owns, or is the reporting mostly inquiries?
Both are worked examples of a method, not a deliverable.
Your own team could build a media optimizer; this is not magic, it is a linear solve. What an internal team cannot easily do is audit its own inquiry-optimized buy and hand a partner a cited, independent exhibit. The value here is that independence, the speed of a fixed-scope sprint, and a track record in higher-ed enrollment marketing, not raw capability.
The honest bound: the prototypes show the shape of the inquiry-versus-enrolled-start gap on public data. They do not size the real gap for any partner; only the partner's SIS join does, and the readiness console is exactly about whether that join exists. Wiring it to real numbers is the follow-on, not a claim made here.
Fixed-scope, one-time; the IP transfers and your team owns the cadence. No platform, no subscription. A retainer exists only if a real queue of distinct work across partners justifies it; never the default. iDesign is private, so I cannot size a dollar prize off a disclosed line: the band is a hypothesis to test per partner, not a promise. Indicative ranges; final scope set on the call.
One 30 to 45 minute call. Bring nothing; the demo runs on public IPEDS and College Scorecard data for one of your partner programs. I will load the optimizer and, in one click, show the spread between an inquiry-optimal buy and an enrolled-start-optimal one, then switch partners to show the method is portable. If your reporting already demonstrates enrolled starts, you will have spent 45 minutes confirming it. If there is nothing there, I will say so.
Book it: jeffpinto.com/engage · Method: the gcu-media-planning note
Jeff Pinto runs a small, independent data and AI advisory practice (jeffpinto.com). Thirty years across AI data and privacy, health tech, marketing analytics, renewables, logistics, and broadcasting; the last seven in ML and AI. Hands-on at Meta, Uber, and IBM, plus six startups (one turnaround, three acquisitions). Two MScs: computer science (Toronto) and engineering (Loughborough). Engagements are fixed-scope, four to twelve weeks, no platform and no subscription; whatever gets built, the IP transfers to you.
The relevant slice for iDesign: from 2009 to 2015 I ran enrollment-marketing analytics for higher-ed clients at Sparkroom and Blue Camel, the same inquiry-to-enrolled-start funnel, in an industry shaped top to bottom by gainful-employment and incentive-compensation rules; the boast-and-bound media method here comes straight out of a 2012 GCU paid-media pilot from that era.
Sources: Inside Higher Ed ("3 Questions for Whitney Kilgore"; "OPMs: Fee for service is growing," 2017) · Higher Ed Dive, "Education Department to review guidance allowing revenue-share agreements with OPMs" (2023 bundled-services review) · iDesign company materials · public higher-ed data (IPEDS, College Scorecard, NCES College Navigator, BLS OOH). iDesign is private; no revenue or cost-per-anything is disclosed, so any dollar figure is bounded, not sized. CEO and co-founder titles to confirm before external use.