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Smarter. Faster. Stronger. Business Intelligence & Analytics in Enrollment

Matt Ellis, MBA

University of Nebraska - Lincoln

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MY PATH

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13 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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MY PURPOSE

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MY ENERGY

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TODAY

• A brief history of time Enrollment Management & Analytics

• What is Business Intelligence (BI) and how do we know when we see it?

• Tips for starting a BI culture in your organization

• Avoiding the pitfalls of data

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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 – WHAT HAPPENED?

• 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 – WHY DID IT HAPPEN

• Financial aid matrix analysis

• Static models and win/loss reports

• Competitive analysis

• Surveys

• National Student Clearinghouse

• Market research on tuition, brand, etc.

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PREDICTIVE – WILL IT HAPPEN AGAIN & TO WHOM

• Application of past funnel conversions

• Model scoring

• Financial aid matrix leveraging

• Admit/enrollment qualifiers

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PRESCRIPTIVE – WHAT CAN WE DO TO CAUSE/PREVENT IT FROM HAPPENING?

• Model score augmentation

• Model score overrides

• Rapid response contact models

• Mindset/persona identification

• Proactive aid leveraging

• Amazon the universe

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YOUR CASE STUDIES

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CASE STUDY | MATRIX PERSONAS

• Descriptive: Strained yield at nonprofit private after rapid gross tuition increases. Rapid rise in discounting.

• Diagnostic: Need/yield analysis shows impacts across financial aid spectrum –but show zones of like yield behaviors when looking at academic/economic matrix. Interesting patters when zones are overlapped with Clearinghouse lost admit data

• Predictive: Use academic and EFC matrix not only for aid leveraging strategy, but create 4 messaging personas based on similar choice behavior

– Affordability, High Academic/High Need, Stretch Value, Competitive Value

• Prescriptive: Create different yield messaging and engagement strategies for each one of the four personas who behave differently within different aid and competition choice sets

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SO HOW DO I START 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

• Order your data rare and medium rare

• Build room for pipelines and exploration

• BYOBI

• Over time, build a data fabric

• Manage complex change

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CASE STUDY | WATCH & WAIT

• Admission team lacked full funnel goals for each market/territory (final enrolled counts only)

• Dashboards only measured high level YTD trends

• Weekly snapshots showed detail but primarily consumed only by directors

• CORE ISSUE: Staff did not see their piece of the goals, or where they were at in the pursuit

• Resolutions

– Weekly distribution of official snapshot with rapid analysis and call for feedback

– Construction of monthly funnel pace goals for markets

– Development of new dashboards and filters focused on “small data”

– Formation of “scrum” teams for each major goal to look at data bi-weekly and adjust plans (recruitment, marketing, events, technology, operations, orientation)

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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

• 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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WHERE IS YOUR ORGANIZATION?

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THANK YOU!

Matt Ellis

Executive Director, Academic Services & Enrollment Management

University of Nebraska - Lincoln

matt.ellis@unl.edu

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