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Running the Numbers: Improving YourPosition for Enrollment Planning and            Forecasting”       TxGAP – Summer Confe...
The Plan for the Session:•  Overview of predictive modeling & optimization for enrollment   management•  Case 1: Do cycles...
A little bit about me & where I work...
Where is the industry today with the idea of business analytics &          intelligence?
Degree of intelligence   Standard	             Ad	  hoc	  Report	         Query	                       Alert	      Reports...
Degree of intelligenceStatistical	             Randomized	                        Predictive	           Optimization	  	  ...
Informatics/Analytics industry is movingfrom small data to big data from data      analytics to data scientist
So, what do we know so far aboutpredictive modeling for Enrollment             Management?
Ad	  hoc	  Report	                                      Standard	  Reports	                                               ...
Predictive Analytics                                                                                Predict	  &	       Sta...
Examples of Enrollment Predictive Modeling•  Case 1: North Dakota University   –  Type of Model: inquiry model using geo-d...
Examples of Enrollment Predictive Modeling•  Case 2: University of Minnesota   –  Type of Model: application generation mo...
Examples of Enrollment Predictive Modeling:•  Case 3: State University of New York   –  Type of Model: lead modeling using...
Better predictive power with students whodo not matriculate than with model that forecast actual                  students...
The “technique” is used in other consolidated markets...     if it works for them, it should work for us!
It requires quantitative analysis of paststudent characteristics to predict probabilities of                  future results
Your predictive modeling team should have    people who are confortable doing:                     The modeling guy:      ...
Access to historical data is required!
Modeling 101: Defining Model Attributes               Student Behavior               (influences, emotions,               ...
...and off course, there is a problem with that!
SUSPECTS> PROSPECTIVE> APPLICANTS> ADMITS> DEPOSITS> NEW                                     Stealth Applications         ...
...an approach for predictive modeling in         enrollment management
Applicants   Prospective              Students
New students   Applicants
CASE#1:Do cycles have memory? The case of the 3-             yr adjusted yield   Predictions through the admissions funnel...
Recommendations 1.  Rapid “back-of-the-envelop” modeling 2.  You can go “up” or “down” the funnel 3.  Need for historical ...
CASE#2:     Am I making my class?         New Student forecasterDownload worksheets at: www.EnrollmentAnalytics.com
CASE#3:                 The magic ballranking and an enrollment predictor (2 examples)Download worksheets at: www.Enrollme...
5-Stage Admissions Funnel     Prospects           Applications                     Admits                              Dep...
Example: say that you have 20k leads in yourcycle and only 300 matriculate, then you have a              2% conversion rate
Now, you “observe” that 200 out of your 300  new students presents a subset of 5% of your           prospective students p...
300 new students                                                                         33%	                             ...
FT-­‐Dom,	                    FT-­‐Dom,	                                                             5%	                  ...
..and if you get really good atunderstanding your students...
Student	  Name	   	  	            Status	                     	     Predictor	                                            ...
So, how can I build a model like that      predicts enrollments?
Student Uncertainty & Variance                       Behavior &                      Personal Life     Academic           ...
Recommendations for Advanced Models 1.  It gets complicated 2.  Its “easy” to model for student characteristics, but     c...
CASE#4: Opps, I ran out of time, but this is a very               cool modelDownload worksheets at: www.EnrollmentAnalytic...
Final Recommendations 1.    Plan for good, bad, and what you think is going to realistic 2.    Avoid predictions but give ...
ThanksJeanCarlo (J.C.) Bonilla     jbonilla@poly.eduwww.EnrollmentAnalytics.com       718-260-3201
Running the Numbers: Improving Your Position for Enrollment Planning and Forecasting - Jeancarlo Bonilla
Running the Numbers: Improving Your Position for Enrollment Planning and Forecasting - Jeancarlo Bonilla
Running the Numbers: Improving Your Position for Enrollment Planning and Forecasting - Jeancarlo Bonilla
Running the Numbers: Improving Your Position for Enrollment Planning and Forecasting - Jeancarlo Bonilla
Running the Numbers: Improving Your Position for Enrollment Planning and Forecasting - Jeancarlo Bonilla
Running the Numbers: Improving Your Position for Enrollment Planning and Forecasting - Jeancarlo Bonilla
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Running the Numbers: Improving Your Position for Enrollment Planning and Forecasting - Jeancarlo Bonilla

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Conference presentation from the Texas Association of Graduate Admissions Professionals (TxGAP) 2012 Professional Development Conference.

Author:
Jeancarlo Bonilla
Director of Graduate Enrollment Management
Polytechnic Institute of New York University

Description:
Learn how to use predictive modeling techniques and apply them to the area of graduate enrollment management.

For more information, visit www.txgap.com.

Published in: Technology, Business
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Running the Numbers: Improving Your Position for Enrollment Planning and Forecasting - Jeancarlo Bonilla

  1. 1. Running the Numbers: Improving YourPosition for Enrollment Planning and Forecasting” TxGAP – Summer Conference July 20th, 2012 JeanCarlo (J.C) Bonilla Director of Graduate Enrollment Management New York University, Polytechnic Institute
  2. 2. The Plan for the Session:•  Overview of predictive modeling & optimization for enrollment management•  Case 1: Do cycles have memory? The case of the 3-yr adjusted yield (4 examples)•  Case 2: Am I making my class? Modeling for scenarios forecasting (3 examples)•  Case 3: The magic ball, ranking and an enrollment predictor (2 examples)•  Case 4: Opps, I ran out of time, but this is a very cool model Worksheets download at EnrollmentAnalytics.com
  3. 3. A little bit about me & where I work...
  4. 4. Where is the industry today with the idea of business analytics & intelligence?
  5. 5. Degree of intelligence Standard   Ad  hoc  Report   Query   Alert   Reports   “how  many,  how   “what  is  exactly   “what  actions  are  “what  happened”   often,  where”   the  problem”   required”   Descriptive Analytics
  6. 6. Degree of intelligenceStatistical   Randomized   Predictive   Optimization     Model   testing   Model/Forecast     “what  is  the  best  “why  is  this    “what  happens  if   “what  will   that  can  happening”   we  try  this”   happened  next”   happened”   Predictive Analytics
  7. 7. Informatics/Analytics industry is movingfrom small data to big data from data analytics to data scientist
  8. 8. So, what do we know so far aboutpredictive modeling for Enrollment Management?
  9. 9. Ad  hoc  Report   Standard  Reports   “how  many,  how   “what  happened”   often,  where”  SUSPECTS> PROSPECTIVE> APPLICANTS> ADMITS> DEPOSITS> NEW Query   Alert   TACTIC “what  is  exactly  the   “what  actions  are   problem”   required”  
  10. 10. Predictive Analytics Predict  &   Statistical  Model   Random  Testing   Optimization     Forecast     “why  is  this    “what  happens  if  we   “what  is  the  best  that   happening”   try  this”   “what  will  happened   can  happened”   next”  SUSPECTS> PROSPECTIVE> APPLICANTS> ADMITS> DEPOSITS> NEW TACTIC
  11. 11. Examples of Enrollment Predictive Modeling•  Case 1: North Dakota University –  Type of Model: inquiry model using geo-demographic –  Predictive Power: 36% of students who will enroll & 97% of student who will not enrolled
  12. 12. Examples of Enrollment Predictive Modeling•  Case 2: University of Minnesota –  Type of Model: application generation model using, ACT and geo- demographic information –  Predictive Power: 85% of applicants to a “large research university” are from within the same state or form a neighboring state
  13. 13. Examples of Enrollment Predictive Modeling:•  Case 3: State University of New York –  Type of Model: lead modeling using geo-demographic, academic data, and financial aid data –  Predictive Power: 45.67% of applicants predicted to enroll did in fact matriculate and 82.16% who where predicted not to enroll did not matriculate
  14. 14. Better predictive power with students whodo not matriculate than with model that forecast actual students enrollments
  15. 15. The “technique” is used in other consolidated markets... if it works for them, it should work for us!
  16. 16. It requires quantitative analysis of paststudent characteristics to predict probabilities of future results
  17. 17. Your predictive modeling team should have people who are confortable doing: The modeling guy: 1.  Regression Analysis (logistic regression) 2.  Business analytics The computer guy: 1.  Database architecture & design 2.  Database querying 3.  Data aggregation & integration 4.  Data reporting
  18. 18. Access to historical data is required!
  19. 19. Modeling 101: Defining Model Attributes Student Behavior (influences, emotions, competition) Student Characteristics (geo-demographic, academic, financial aid)
  20. 20. ...and off course, there is a problem with that!
  21. 21. SUSPECTS> PROSPECTIVE> APPLICANTS> ADMITS> DEPOSITS> NEW Stealth Applications 30%-40% of adult students Source: Aslanian Market Research
  22. 22. ...an approach for predictive modeling in enrollment management
  23. 23. Applicants Prospective Students
  24. 24. New students Applicants
  25. 25. CASE#1:Do cycles have memory? The case of the 3- yr adjusted yield Predictions through the admissions funnel Download worksheets at: www.EnrollmentAnalytics.com
  26. 26. Recommendations 1.  Rapid “back-of-the-envelop” modeling 2.  You can go “up” or “down” the funnel 3.  Need for historical data (static snapshots of cycles) 4.  Student characteristics add more resolution to the model 5.  Use of adjusted 3-year cycles are useful for historical modeling 6.  Historical validity: account for new initiatives
  27. 27. CASE#2: Am I making my class? New Student forecasterDownload worksheets at: www.EnrollmentAnalytics.com
  28. 28. CASE#3: The magic ballranking and an enrollment predictor (2 examples)Download worksheets at: www.EnrollmentAnalytics.com
  29. 29. 5-Stage Admissions Funnel Prospects Applications Admits Deposits2% New
  30. 30. Example: say that you have 20k leads in yourcycle and only 300 matriculate, then you have a 2% conversion rate
  31. 31. Now, you “observe” that 200 out of your 300 new students presents a subset of 5% of your prospective students pool.This means that 1000 prospective students (5% of 20k) converted into 200 enrollments, whichmeans that your conversion rate for this subset is 20%
  32. 32. 300 new students 33%   of 20 % on rate or p redictability 67%   5%   New conver si 95%  20,000 prospective students
  33. 33. FT-­‐Dom,   FT-­‐Dom,   5%   20%  FT-­‐Intl,   40%   PT,  40%   FT-­‐Intl,   55%   PT,  40%   300 new students20,000 prospective students
  34. 34. ..and if you get really good atunderstanding your students...
  35. 35. Student  Name       Status     Predictor     Inquiry   11/16/10       App.   N/A     Hall,  Joy     Adm.   N/A     0.4     Conf.   N/A       Enr.   N/A       Inquiry   12/22/10       App.   12/24/10     Li,  Xiao     Adm.   3/23/11     0.6     Conf.   4/2/11       Enr.   N/A       Inquiry   12/5/10    Build a model that Lopez,  Jose         App.   Adm.   Conf.   1/5/11   1/29/11   3/16/11         0.2  does the following:       Enr.   Inquiry   App.   N/A   12/20/10   2/3/11         Mitchell,  Tamara     Adm.   N/A     0.2     Conf.   N/A       Enr.   N/A       Inquiry   1/26/11       App.   1/28/11     Smith,  John     Adm.   4/16/11     0.4     Conf.   5/5/11       Enr.   N/A       Inquiry   12/13/10       App.   N/A     Troy,  Bryan     Adm.   N/A     0.9     Conf.   N/A       Enr.   N/A    
  36. 36. So, how can I build a model like that predicts enrollments?
  37. 37. Student Uncertainty & Variance Behavior & Personal Life Academic Demographi Financial cal Geographic alFT vs intl vs PT
  38. 38. Recommendations for Advanced Models 1.  It gets complicated 2.  Its “easy” to model for student characteristics, but complexity increases when accounting for student behavior 3.  Models are better at predicting student who do NOT register 4.  Every school is different, so every model is also different 5.  Trust your instincts! No one knows students better than you... Your job is then trying to articulate and generalized characteristics and behavior
  39. 39. CASE#4: Opps, I ran out of time, but this is a very cool modelDownload worksheets at: www.EnrollmentAnalytics.com
  40. 40. Final Recommendations 1.  Plan for good, bad, and what you think is going to realistic 2.  Avoid predictions but give options 3.  Its about resource allocation 4.  Work with other groups in your institution 5.  Trust your GEM instincts 6.  Its earsier to account for student characteristics, but modeling and forecasting behavior is very complex
  41. 41. ThanksJeanCarlo (J.C.) Bonilla jbonilla@poly.eduwww.EnrollmentAnalytics.com 718-260-3201

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