Podcast Summary: EdTech Connect

Episode: "How Data Can Fix Higher-Ed’s Pricing Problem"

Host: Jeff Dillon
Guest: Dr. Emily Chase Coleman, CEO & Co-founder of HAI Analytics
Date: August 29, 2025

Main Theme & Purpose

In this episode, host Jeff Dillon sits down with Emily Chase Coleman, a higher ed data strategist and co-founder of HAI Analytics, to dive into how advanced analytics, artificial intelligence, and transparent predictive modeling are reshaping pricing and admissions in higher education. The conversation explores dismantling the high price/high discount tuition structure, widening equitable access, fostering institutional data literacy, and empowering leadership to make nimble, data-informed decisions in a time of demographic shifts.

Key Discussion Points & Insights

1. Emily's Journey: From Data Analyst to EdTech Founder

Emily's unexpected path to entrepreneurship emerged from a need to fill significant gaps in higher ed analytics tools ([02:00]).
Starting HAI Analytics was daunting but fulfilling:

“…there was sort of a solution missing in the marketplace…we could author something a bit different and we just made the leap. And that was seven years ago.”
— Emily, 02:13

2. The Power (and Limits) of Data in Higher Ed

Emily’s early attraction to analytics came from the excitement of uncovering insights, not just numbers:

"...it was more just learning how to do the statistical tests…learning…how to remove bias. That really, really kind of hooked me on analytics…"
— Emily, 03:16
Blending social psychology with statistics now informs her advisory work:

"…both [survey data and statistical models] are very valuable. I don't think you can get the complete picture from just one."
— Emily, 04:12

3. From Consultancy to Scalable Solutions

HAI Analytics shifted to a software with service model to democratize access for all institutions, not just those with robust budgets:

"We wanted to have an offering that was a less expensive, less time intensive for Us, but that could be used by…a wide range of schools…”
— Emily, 05:23
Main focus: Predicting student application, enrollment, and retention (with some work in donor behavior) ([06:07]).

4. Dashboards vs. Actionable Insight

Red flags: Dashboards that offer static or superficial metrics or conflate correlation and causation.
"…making these broad, sweeping generalizations without any research behind it is a red flag…we want things to be real time…they just really have to be interactive…"
— Emily, 06:50

5. Surprising Data Discoveries & Case Studies

Dorm and course selection as predictors of retention provided unexpected but actionable insights for partner schools ([08:07]).
Sometimes, students taking "easier" courses correlated negatively with retention, suggesting they may be struggling academically.

6. Helping Institutions Ask the Right Questions

A crucial service: Helping schools move past “data overload” to identify meaningful questions, necessary data, and building systems for extraction and analysis ([10:01]).

7. Reducing the Black Box Problem

Commitment to transparency: HAI Analytics always shares modeling techniques and explanations with clients.

"…we're very open with our partners about the models that we build. We don't use kind of proprietary methodology."
— Emily, 10:49

8. High Price, High Discount Model: A Ticking Clock

Emily declares the model is “nearing the end of its shelf life”—private schools squeeze out middle-class families who can’t afford tuition but also can’t qualify for aid ([11:42]).
“...the discount rates have just gotten so high that they’re not sustainable. ...the end is near. That’s my thought on it.”
— Emily, 11:42

9. Pandemic Lessons for Predictive Analytics

The pandemic invalidated models based on historical behavior.
Institutions learned to question which data points will truly predict future behavior in a changed landscape ([14:06]).

10. Test-Optional Policies: Limited Equity Wins

Results of test-optional admissions did not fully close access gaps—other aspects like extracurriculars still privilege wealthier applicants.

“…eliminating the tests wasn’t enough to get where we want to get with that. But at least, you know, it sort of exposed that.”
— Emily, 15:15

11. Optimizing Financial Aid with Data

Real-world case: Optimization of aid at the individual student level increased enrollment and revenue for clients ([16:43]).
“...adjusting to that type of optimization ... often will increase yields...we see with that increased revenue and really all of the schools that we're working with now, we've seen that kind of success.”
— Emily, 16:43

12. Changing Campus Culture Around Data

The “I’ve always done it this way” mindset among veteran managers can stall innovation.

“...there’s no way that numbers are going to tell me anything more about that. And I think that is what they need to drop...”
— Emily, 17:57
HAI’s philosophy: Human expertise and quantitative evidence—never one without the other.

“...the name of our company, HAI, stands for human and artificial intelligence.”
— Emily, 17:57

13. Avoiding Dependency and Building Capacity

HAI will train institutional staff to run their models if desired; success requires resources and commitment ([18:53]).

14. AI’s Role in Higher Ed Analytics

AI/ML process massive data, refine relevant factors, but must have human context and oversight.

“There has to be a person who is thinking about the data going in and interpreting the findings coming out. It can’t just be dump the data in and the model will kind of tell you the answers.”
— Emily, 20:30
AI is neither magic nor menace—it just enhances efficiency if used wisely, not naively.

15. Supporting Women Founders

Systemic issue: Women founders in edtech receive disproportionately less funding.

“...female founders do not get funded at the same rate as male founders...it does put an extra barrier...”
— Emily, 22:16

16. Advice for Presidents Facing Demographic Declines

Move all key metrics from quarterly to real-time monitoring for maximum nimbleness.

“I think my answer to that would be every metric…if we don’t have that real time tracking...then it takes too long to make adjustments…”
— Emily, 23:15

Notable Quotes & Memorable Moments

On the future of tuition pricing:
“The end is near. That’s my thought on it.”
— Emily, 11:42
On the limits of machine learning in education:
“It can’t just be dump the data in and the model will kind of tell you the answers.”
— Emily, 00:00 / 20:30
On changing leadership culture:
“There’s no way that numbers are going to tell me anything more about that. And I think that is what they need to drop...”
— Emily, 17:57
On data transparency:
“We're very open with our partners about the models that we build. We don't use kind of proprietary methodology. We will show them the models, we will explain the factors."
— Emily, 10:49
On test-optional admissions and equity:
"Eliminating the tests wasn’t enough to get where we want to get with that. But at least ... it sort of exposed that."
— Emily, 15:15

Timestamps for Key Segments

[02:13] Emily’s journey to launching HAI Analytics
[04:12] The value of integrating social psychology with statistics
[05:23] Shifting to productized software to serve more institutions
[06:50] Dashboard red flags & importance of insight over display
[08:07] Unexpected data-driven discoveries (e.g., dorms and course selection)
[10:01] Overcoming data overload: how to ask the right questions
[10:49] Making predictive modeling transparent for leadership
[11:42] Why the high price/high discount tuition model is unsustainable
[14:06] Lessons from the pandemic on predictive analytics
[15:15] Test-optional policies: gaps remain
[16:43] Financial aid optimization and its positive impact
[17:57] Leadership’s need to embrace data over gut
[18:53] Training schools for self-sufficiency in analytics
[20:30] Role and risks of AI/machine learning in higher ed
[22:16] Systemic funding barriers for women founders
[23:15] Real-time metrics for institutional agility

Final Takeaway

Emily Chase Coleman outlines a vision for higher ed pricing and data culture that is transparent, equitable, and adaptive. The sustainability of current tuition models is in serious doubt—data-driven, AI-assisted, but fundamentally human-centered decision-making is now essential for institutional survival and student success.

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