Diagnostic engagement · Cross-brand enrollment-marketing efficiency · Prepared for Covista

Your CEO named the problem on the Q1 call. Your $247M advertising line still reports it as one number.

An independent, compliance-aware read of the cross-brand efficiency gap: built on your own public filings before it asks for a single internal number, then scoping the real per-brand split your stack already holds. A 4-6 week diagnostic ships the working models; the IP transfers. The per-brand cost-per-start it shows is a synthetic estimate calibrated to public anchors : not a number it claims to know.

$247.4M

FY25 advertising: 13.8% of revenue, bundled in one expense line, no public cost-per-start

13.6% / 2.2%

Q1 FY26 enrollment growth, Walden vs. Chamberlain: the gap management blamed on "local marketing effectiveness"

Data asked for: SEC 10-K, the earnings 8-K, IPEDS, Scorecard. All public, all free

The problem: your need, first

Advertising is your largest controllable growth lever: $247.4M in FY25, up three years running (219.4 to 227.9 to 247.4), reported as one lump inside "student services and administrative expense," with no per-brand split and no cost-per-start. So the blended advertising-to-revenue ratio hides which brand subsidizes which. Walden's online-grad keywords are auction-priced against WGU and SNHU; Chamberlain's nursing demand is pulled by a workforce shortage. Those aren't the same cost-per-start , and on the Q1 FY26 call the CEO said as much, conceding Chamberlain "underperformed in local marketing effectiveness" with funnel conversion "below benchmarks" while Walden grew 13.6%. With operating margin up to 19.1%, the board will ask how much of three years of spend growth bought new starts versus rode the tuition increase, and what a point of efficiency on that $247.4M is worth in EBITDA and enterprise value.

What it is: three working reads

Cross-brand cost-per-start radar: splits the disclosed $247.4M across the three brands and shows an implied cost-per-start for each as a bounded range, the worked example of the method. Not a number it claims to know; the real split is the knob.
Constrained efficiency frontier: where the next dollar buys the most qualified starts once 90/10, Gainful Employment, and FTC rule out the moves a pure-ROI optimizer would make. The read your in-house team is structurally bad at producing.
EBITDA to enterprise-value bridge: converts a point of efficiency on the $247.4M line into dollars to EBITDA and, at your multiple, enterprise value: the currency the board and a sponsor actually price in.

The wedge vs. the agency bid optimizer

Agencies are genuinely good at placing media and optimizing bids once the budget's set. What they structurally can't sell you is a model that might say spend less: their incentive ends in more managed spend. A black-box optimizer is a non-starter in a 90/10 + Gainful-Employment + FTC environment, where which-students-you-recruit is a compliance question, not just an ROI one. This is the opposite: every input is a citation, the IP transfers, your team audits it.

Honest bound: this won't hand you the true per-brand cost-per-start: only your internal data can. It hands you the shape of the cross-brand efficiency gap on data nobody disputes, and scopes getting the real numbers.

Why now

Margins jumped to 19.1%: the CFO has proof discipline pays, so the ad line gets read under a microscope.
Marketing was reorganized in 2026; cross-brand efficiency is now one owner's question, not three.
The nursing-shortage tailwind means the win is efficiency on qualified demand, not manufacturing volume.

The 4-6 week diagnostic

WK 1-2Build the cross-brand model from the 10-K, the earnings 8-K, IPEDS and Scorecard → retrospective per-brand cost-per-start, bounded

WK 2-3Calibrate the spend-allocation frontier; map the regulated-marketing constraints (90/10, GE) onto the brand mix → efficiency frontier with compliance flags

WK 3-4Build the EBITDA → enterprise-value bridge on the disclosed numbers → efficiency sized in the currency the board reads, not just a ratio

WK 5-6Data-readiness read on the internal extension (real per-brand split, channel cost-per-start) + the quarterly cadence that makes the lever durable → phase-2 scoping memo, honest about the gap

Engagement shape

Fixed scope, 4-6 weeks, one diagnostic. Ships the working cross-brand efficiency model on public data, the retrospective cost-per-start read, and a data-readiness read on extending it inside your stack. All IP transfers to Covista. No platform, no subscription, no data leaves the building; none is asked for.

Commercials in a one-page SOW after a 30-minute call. Receipts: built higher-ed marketing-budget SaaS at Sparkroom (the same funnel, the same LeadsCon trade floor, 2009-2015); measurement at Meta scale; the prototype fleet at github.com/bigbrownjeff.

The ask

One 30-45 minute working session. Bring nothing: the data's public. I'll walk the cross-brand cost-per-start spread live on your own FY25 filings and show where revenue-share spending hides the gap. If the three brands really do share a cost-per-start, you'll see it in the first session and you've spent 45 minutes confirming the spend is already where it should be.

All three tools, live: covista-enrollment.pages.dev · Book it: jeffpinto.com/engage · The fleet: github.com/bigbrownjeff

Sources: SEC EDGAR (Covista/Adtalem CIK 0000730464, FY25 10-K + earnings 8-K; advertising $247.4M, segment revenue/margin, enrollment) · IPEDS College Navigator (Chamberlain 454227, Walden 125231) · College Scorecard · BLS OOH (RN demand) · AACN (nursing-school capacity) · FederalRegister.gov (90/10, Gainful Employment) · FTC

Built by Jeff Pinto: Meta / Uber / Sparkroom + startups · Higher-ed marketing analytics · Two MScs. jeffpinto.com