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About the Episode:

Mallory sits down with Josh Dodson, VP of Performance Marketing and Analytics at BVK, to unpack one of the biggest questions in enrollment marketing today: what is actually driving enrollment? Their conversation challenges the false confidence many leaders place in dashboards, last-click reporting, and traditional attribution tools that were never designed to capture the full complexity of student decision-making. For higher ed marketers navigating long recruitment cycles, shifting privacy rules, and growing pressure to prove ROI, this episode offers a sharper framework for data analytics in higher education. It is a candid, timely listen for anyone trying to build a smarter marketing strategy for student recruitment without mistaking visibility for truth.

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Key Takeaways

• Attribution is not the same as impact. Traditional tracking often rewards the last measurable touchpoint, not the channels that actually build demand and influence enrollment decisions.
• Marketing mix modeling gives leaders a bigger-picture view. Instead of focusing on clicks alone, MMM helps institutions understand how channels work together over time to support enrollment outcomes.
• Higher education needs measurement models built for long decision cycles. Students, families, counselors, aid packages, and brand perception all shape enrollment, which makes simplistic attribution especially risky.
• Better data only matters if people use it. Institutional buy-in, internal trust, and actionable reporting are just as important as statistical rigor when applying data analytics in higher education.
• AI can support analysis, but it cannot replace judgment. It can speed up data cleaning and simplify interpretation, yet human expertise still matters most when making strategic budget decisions.
• MMM is powerful, but it is not a miracle cure. It can help optimize budget allocation and strengthen enrollment marketing decisions, but it cannot solve structural issues like the demographic cliff.

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Episode Summary

Why is traditional attribution falling short in higher education marketing?

Josh Dodson’s central argument is blunt: attribution has never worked quite the way marketers were promised it would. Even before privacy changes, cookie loss, and iOS tracking restrictions made things messier, attribution models were already telling an incomplete story. They were good at highlighting what could be tracked, but not necessarily what was truly driving demand.

That distinction matters a lot in higher ed. Enrollment decisions are rarely triggered by one ad, one email, or one click. They are the result of repeated exposure, trust-building, family conversations, institutional reputation, financial aid considerations, and time.

In the episode, Josh makes the case that many dashboards create the illusion of certainty. A channel can look like the “winner” simply because it happened to be the last measurable touch before a conversion. For enrollment leaders, that means a real risk of overfunding the visible and underinvesting in the influential.

What is marketing mix modeling, and why is it having a resurgence?

Marketing mix modeling, or MMM, is a statistical method that evaluates how different channels contribute to outcomes over time. Rather than assigning all the credit to one final action, it looks at the broader pattern across tactics like digital ads, events, out-of-home, and brand-building activity. That makes it especially relevant for institutions trying to improve data analytics in higher education without overrelying on flawed attribution systems.

Josh explains that MMM is not new. It has been around for decades, but for a long time it was mostly reserved for large enterprises with deep budgets and highly specialized teams. Now, thanks to open-source tools, better computing access, and lower implementation costs, the model is becoming much more accessible.

That accessibility is why the conversation matters right now. As measurement gets more difficult at the user level, institutions are being pushed to find approaches that still support confident decision-making. MMM helps answer a higher-order question that matters deeply in enrollment marketing: not just what got the click, but what moved the system.

What can marketing mix modeling solve, and what can’t it solve?

One of the strongest moments in the episode comes when Josh says, “You cannot market your way out of a demographic cliff.” That line cuts through a lot of noise. It is also a helpful reminder that even the best measurement model cannot fix structural realities.

MMM can help institutions determine where to spend the next marketing dollar. It can reveal which combinations of channels are generating momentum, where efficiency is improving, and where budget may be wasted. For teams working on marketing strategy for student recruitment, that kind of directional clarity can be incredibly valuable.

But it cannot manufacture audience demand that does not exist. If an institution is facing major demographic shifts, shrinking population pools, or market misalignment, measurement alone will not solve the problem. What MMM can do is help leaders respond more intelligently by reallocating investment, testing new markets, and seeing the bigger picture with more honesty.

How can higher ed teams make marketing mix modeling actionable?

Josh is clear that strong modeling is only half the battle. A statistically sound analysis that sits untouched on a desktop is not a strategy. For MMM to work inside an institution, the data has to be translated into decisions people can actually understand and act on.

That starts with buy-in. Leaders across enrollment, marketing, and administration need to understand why the model matters and how it can improve outcomes. Josh describes this as inviting people “into the tent,” which is a smart way to frame the organizational side of data analytics in higher education.

It also requires a realistic assessment of readiness. Some institutions have the in-house talent to use open-source tools and build models internally. Others need outside support, cleaner data, stronger infrastructure, or simply more time before they are ready to make MMM part of their enrollment marketing workflow.

Where does AI fit into marketing mix modeling?

Because no higher ed conversation is complete without talking about AI, Mallory asks the obvious question: does AI make this easier, better, or just more confusing? Josh’s answer is measured. AI can absolutely help with data cleaning, data organization, and simplifying outputs for broader audiences.

That said, he does not suggest that AI replaces the strategic core of MMM. Running robust models still requires significant computational power, disciplined methodology, and sound interpretation. AI can identify patterns and recommend tactical tweaks, but Josh warns that it often reinforces the same narrow optimization mindset that made attribution problematic in the first place.

His best framing may be the simplest: AI is getting very good at telling marketers how to run harder, while marketing mix modeling helps determine whether they are running in the right direction. For teams exploring ai in higher education, that distinction is worth sitting with. Faster execution is only useful when strategy is pointed where it should be.

What questions should enrollment leaders be asking now?

The episode closes with a smart challenge for leaders walking into their next board meeting. Instead of asking which channel won, Josh suggests asking which investments moved the whole system. That reframing shifts the conversation from isolated performance metrics to real organizational impact.

It is a powerful mindset shift for anyone leading enrollment marketing strategy. Board members and executive teams often want clean answers, but student recruitment does not happen in neat, linear paths. The better question is not who gets credit, but what mix of effort is actually producing momentum.

Josh also points leaders toward one of the most practical questions MMM can answer: where should I spend my next marketing dollar? In a resource-constrained environment, that may be the most useful question in all of data analytics in higher education. It gets past vanity metrics and into the kind of decision-making that can reshape outcomes.

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Connect With Our Host:

Mallory Willsea
https://www.linkedin.com/in/mallorywillsea/
https://twitter.com/mallorywillsea

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Enrollify is produced by Element451 —  the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.