ROI claims show up early in nearly every higher edtech conversation.
They’re on the vendor’s landing page. In the deck your cabinet reviewed. Sometimes even in the first five minutes of the sales call.
That’s not surprising. Institutions need to justify budget decisions. Vendors want to demonstrate impact. ROI offers a clean way to frame value.
But somewhere along the way, it lost its edge.
CTOs, provosts, and CFOs are familiar with the fine print by now:
- Four-week pilots with handpicked users
- Self-reported gains with no clear baseline
- Productivity boosts based on survey sentiment
- Case studies where five variables changed at once
This is the current standard of practice. The numbers aren’t necessarily fabricated — but they’re fragile. Outcomes reported under ideal conditions rarely translate cleanly to real-world environments.
We’ve seen this repeatedly with student success platforms, engagement tools, and instructional tech. Early metrics often look strong, but they rarely account for semester transitions, uneven faculty buy-in, or changing student populations.
Even randomized evaluations, like RCTs or quasi-experiments in education research, can suffer from short durations, novelty effects, or implementation challenges that limit their relevance. A statistically significant result under controlled conditions rarely predicts sustainable impact at institutional scale.
Institutional buyers know this, and the result is quiet cynicism. While ROI claims still show up, few senior buyers take them seriously. They're scanned, but not fully believed. They make the deck look complete but rarely inform a real decision.
And when some buyers do believe them — when they use those numbers to justify a purchase — the disillusionment that follows hurts everyone: the buyer, the vendor, and the credibility of the entire category.
That’s where we are now.
What Fails, What Lasts
ROI numbers are often generated from narrow inputs under ideal conditions. A single enthusiastic department chair. A small cohort of early adopters. A tightly controlled pilot during a low-enrollment summer term. The figures may be technically true in that context — but they don’t survive outside it.
Beyond the issue of small sample sizes, three patterns show up across disappointing implementations.
Fragile Attribution
Academic environments don’t operate in isolation. When a new advising or analytics tool rolls out alongside curriculum changes, leadership turnover, or enrollment shifts, it becomes difficult to isolate what’s driving outcomes. Yet vendors often attribute all improvements to their product. And institutions sometimes accept that framing, because a clean success story is easier to present to trustees or justify in budget hearings. The result: false clarity.
In one case we studied, a vendor pointed to improved student retention linked to their engagement tool. But the same term, the institution had launched a new first-year experience program, added advising capacity, and shifted housing policy. Nobody challenged the ROI slide — but nobody really trusted it either.
Conditional Results
Even the most well-regarded EdTech tools rely on surrounding factors being in place:
- Clean and complete student data
- Faculty and staff engagement across departments
- Alignment with the LMS, SIS, or CRM infrastructure
- Integration into advising and instructional workflows
If a 20% improvement in credit completion only showed up in programs with intrusive advising, full-time staff, and tech onboarding support, it’s unlikely to replicate across a commuter-heavy campus with limited capacity. But those pilot results often become the numbers used in strategic plans or vendor justifications — with little mention of what conditions made them possible.
We’ve seen this across digital tools designed for advising, student success, and academic planning. A product that worked smoothly during a spring-term pilot fell flat in fall. Staff turnover left gaps. Students weren’t onboarded. IT flagged new integration issues. The original ROI case was irrelevant by the time implementation hit scale.
Missing Counterfactuals
Many ROI stories fail to ask what would’ve happened without the product. Would persistence rates have risen anyway due to concurrent efforts? Could the same gains have been achieved by reallocating advisor workload? Was another vendor’s simpler, lower-cost tool never even tested?
Without serious attention to the counterfactual, ROI becomes inflated by default. And when the expected gains don’t materialize, trust erodes — not just in the vendor, but in the entire category.
These patterns show up clearly in renewal cycles.
Usage quietly declines. Champions rotate out. The renewal conversation becomes a budget scrutiny exercise. No one says the tool failed. They just say: “We didn’t get what we expected.”
ROI fails when it’s treated as a promise rather than a hypothesis. When the story is too tight to reflect how higher education actually works.
The vendors that earn long-term trust don’t oversimplify. They offer evidence with context. They say what works, for whom, and under what conditions. They let the results speak — and they stay in the conversation when the numbers need explanation.
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What Smart Institutions Are Doing Instead
For most campuses, ROI isn’t just a data point. It’s a political argument.
Cabinet leaders need to justify spend to finance. Finance needs to justify it to the board. And at public institutions at least, the board needs to show the public that tuition and taxpayer dollars are being spent wisely.
That’s why the most effective institutions no longer accept vendor-supplied ROI as-is. They apply pressure early — and keep it applied.
Because when a software decision goes sideways, it’s not the vendor’s name in the press release or board minutes.
We recommend applying a 4-question test to every ROI claim before it’s used in any business case or procurement justification:
- Baseline: What’s the starting point, and who collected it?
- Variation: Did every customer see the same gains — or just a few?
- Disaggregation: Who benefited? Were certain departments or student groups left out?
- Timeline: How long did it take, and is that realistic for us?
When institutions start from these questions, they flip the dynamic. They’re no longer reacting to someone else's evidence. They’re defining the terms of what counts.
This doesn’t mean building a full research center inside procurement. But it does mean treating software claims like you’d treat any claim from a vendor who wants to touch student data, impact learning outcomes, or plug into core systems.
Some institutions are going further. They're designing implementation pilots that track impact across multiple variables — not just platform logins or sentiment. They’re asking IR and assessment teams to co-design evaluations with IT and faculty. And they’re negotiating contracts that tie future pricing to actual performance metrics.
This doesn’t just protect the budget. It protects credibility. Because when a high-visibility initiative fails to deliver, the institution carries the reputational risk — not the vendor.
Here’s the deeper shift: institutions are realizing they don’t have to settle for a polished deck over durable evidence. If the ROI claim can’t survive scrutiny, the product likely won’t survive implementation.
And when the data does hold up — perhaps, it should be yours. Not just for the vendor’s marketing story, but for your own internal case-making, strategy alignment, and budget defense.
Institutions that approach ROI this way don’t just buy better software. They build stronger decision muscles.
Some institutions are layering in third-party ROI due diligence customized to their conditions. We’ve designed a process that’s beginning to reshape how leadership teams evaluate EdTech investment cases.
If you’re building this discipline internally or want to see how a third-party can help, ping me. Let's Discuss ROI
