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AMMI WORKSHOPSMARTER. FASTER. STRONGER. | BI AND

ANALYTICS IN ENROLLMENT

Matthew Ellis

Rockhurst University, MO

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WHO AM I?

212 Years | 82,000+ Students Enrolled

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Education is the most powerful

weapon which you can use to

change the world. – Nelson Mandela

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ENROLLMENT MANAGEMENT IS HOW OLD?

• Started in 1976

• Jack Maguire at Boston College is credited with the term and concept in

an article he wrote in Bridge Magazine

• Even then, institutions were seeking to learn more about how their own

operations worked amidst change

• BC was struggling. Birthrates were dropping and there was a concern

about the rising cost of college (sound familiar ☺).

• First official enrollment data: admit questionnaire focused on marketing

channels

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A BRIEF HISTORY LESSONComplicated Systems: Linear, predictable systems that prize efficiency and

effectiveness

Pioneered by mechanical engineer Frederick Taylor in the

1890’s.

He was considered one of the very first management

consultants

Works well when you have predictable input and controlled

systems (assembly line)

Admissions/EM was based on this up until the 20th century

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A BRIEF HISTORY LESSON

Emerging field of study in both mathematical modeling and philosophy

Used in management, chemistry, economics, computer science, etc.

Seeks to better understand how the relationships between parts of a system give

rise to its collective behavior, which in turn forms a relationship with its ecosystem

Complex Systems: Non-linear, multi-variable systems that prize agility and

outcome over process and mechanics

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WHAT’S YOUR BIGGEST CHALLENGE?

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Great ideas executed poorly

often look just like bad ideas executed well

So how do we ensure that we’re executing a good idea well?

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WHAT IS BI AND WHO THE HECK CARES?

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Data Information Decisions Actions

Let’s be honest: most organizations stop

here and call it a day…

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EVOLUTION OF BI AND ANALYTICS

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DESCRIPTIVE

• Funnel reports and conversions

• Event/visit show and no show rates

• Class profile metrics

• Outbound activity and tactics

• Marketing engagement rates

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DIAGNOSTIC

• Financial aid matrix analysis

• Comparative model performance

• Competitive analysis

• Surveys

• National Student Clearinghouse

• Market research on tuition, brand, etc.

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PREDICTIVE

• Application of past funnel conversions

• Model scoring

• Financial aid matrix leveraging

• Admit/enrollment qualifiers

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PRESCRIPTIVE

• Model score augmentation

• Model score overrides

• Rapid response contact models

• Mindset/persona identification

• Proactive aid leveraging

• Amazon the universe

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CASE STUDY | VIP APPLICATION

• Descriptive: Drop in completion rate from previous year

• Diagnostic: Shift in days to completion and decision

• Predictive: History showed that apps completed pre-Nov 1 and with less than 30

days to decision yielded 5-7% higher than post Nov 1 and 31+ days

– ACTION POINT: Create VIP app incentive to drive up pre-Nov 1 completions and re-

design processing to manage influx and reduce time to decision

• Prescriptive: Monitor days to completion and intensify drip comms based on time

away from thresholds

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CASE STUDY | MICRO MATRIX

• Descriptive: Fastest growing populations are first-gen, lower income,

underrepresented

• Diagnostic: Previous history of enrolling EFC segments below 10,000 low and

dropping. Past tuition increase strategy out-pricing future growth segment (prior

base segment stagnant). Still too much lost yield on fringes of matrix cells.

• Predictive: Yield of students below 10,000 EFC trending down due to economics.

Yield of students above 10,000 EFC trending down due to rising competition.

– ACTION POINT: Create new need based aid program that adapts to the growth

population. Create a micro matrix.

• Prescriptive: Instead of falling into one of 130 matrix cells optimized for yield. An

optimization point is calculated for every individual student

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SO HOW DO I BUILD A BI CULTURE?

• Be obnoxiously curious

• Be an example (if you ask for it…use it)

• Recruit obnoxiously curious allies to also be examples

• Become bff’s with the data gurus

• MacGyver until you can Ironman

• Go raw

• Build room for pipelines and exploration

• BYOBI

• Over time, build a data fabric

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STRATEGY OF BI

• Have a strategy

– Chicken and egg on this one. To know your direction, you need to know your

data. To know what data to look at, you need to know your direction. Just

start somewhere.

• What get’s measured gets managed

• Set KPI’s (Drivers and Outcomes)

– Tip: Besting YTD does NOT mean you’re on track…you might just be racing

towards the cliff at a higher speed

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STRUCTURE OF BI

• Data warehouse

– SIS

– CRM

– Service Platform

– EMS

• Dashboards (eliminate the table – GO VISUAL)

– Democratize the data

• Predictive and Prescriptive require action oriented integrations (think

CRM codes/scores, etc.)

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TOOLS WE USE

• NRCCUA Data Lab: Funnel Analysis

• Tableau: Visualize things for leadership

• Slate Reports/Analytics: Live understanding of activity

• MOZ: Strength of SEO and SEM

• R and Python (the heavy hitters use this)

• 3rd Party prospect to enroll, inquiry to enroll models: Targeting and

modeling

• In-house aid leveraging model: Targeting and revenue composition

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COMMON BI PITFALLS

• Survivorship bias – WWI Planes and Enrollee Surveys

• Correlation vs. Causation – Drownings and Nick Cage films : Reg events

and yield

• Anchoring bias – Is this scholarship too low?

• Availability bias – One angry parent = zombie apocalypse

• Illusion of validity – More data does not always equal better data

• KISS

• Feed the elephant and the rider. Stats + Stories = Hearts + Minds

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BE PREPARED FOR REACTIONS

• Disprove with data (actually a healthy BI culture response)

• Disagree with anecdotes (most common)

• Discredit the source (hitting a nerve)

• Ignore the intelligence (hang in there buddy)

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COURSES YOU SHOULD CONSIDER

• BI 101

• Data visualization

• Marketing/social media analytics

• Organizational change

• Presenting/verbal communication

• Grad programs in business intelligence, analytics, data science

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ENROLLMENT MANAGEMENT MATURITY MODEL

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Source: NRCCUA

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WHERE IS YOUR ORGANIZATION?

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THANK YOU!

Matt Ellis

Associate Provost for Enrollment Management

matt.ellis@rockhurst.edu

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