This document discusses Lehman College's development of a data-centric ecosystem to improve recruitment, retention, and other initiatives. It has integrated various tools and systems to expose important information, align messaging and branding, and deliver personalized content. This ecosystem allows events to be syndicated across different platforms, newsletters to automate publishing to websites, and media assets to be shared. Analytics are used to track engagement. The college is also using predictive modeling with data on enrollment, attrition rates, and other factors to develop strategies to improve student outcomes.

1. 1
Beyond Recruitment & Retention:
Success Via a Data-Centric Ecosystem
 David Stevens, Manager of Web Services,
Lehman College, City University of New York
 Joe Medved, Manager of Database &
Applications, Lehman College, City University
of New York
 Aarti Deshmukh, Senior Applications System
Developer, Lehman College, City University of
New York

2. 2
Ecosystem Defined
 What?: The suite of tools & applications that comprise
Lehman’s enterprise publishing & communication systems.
 Why?:
 To align the College’s messaging, brand identity, and delivery of
personalized content.
 To expose critical information and create calls-to-action re
recruitment, outreach, fund-raising, & retention efforts.
 How?: By integrating silos and shadow systems, streamlining
internal processes & enhancing user experience through a
federated strategic technology architecture.

3. 3
Ecosystem At a Glance

4. 4
Ecosystem in Practice
 Event Management
Event category syndication
maximizes exposure to key
events contextually by
publishing to multiple
locations (e.g. college
homepage, affiliate websites,
& CUNY calendar).
Events may be promoted to
Social Media channels &
calls-to-action facilitate user
engagement. Google
analytics tracks traffic spikes
and conversion points.

5. 5
Ecosystem in Practice
 Event Management
Event category syndication
maximizes exposure to key
events contextually by
publishing to multiple
locations (e.g. college
homepage, affiliate websites,
& CUNY calendar).
Events may be promoted to
Social Media channels &
calls-to-action facilitate user
engagement. Google
analytics tracks traffic spikes
and conversion points.

6. 6
Ecosystem in Practice
 WordPress Newsletter:
Intuitive tagging automates
the publishing of college
news, blogs, and
announcements to
department and affiliate
websites.
Calls-to-action facilitate
user engagement, and
Google analytics track
traffic spikes and conversion
points.

7. 7
Ecosystem in Practice
 Digital Connect: Media
Asset Repository
College videos may be
published to YouTube,
Vimeo, or iTunes U and
published to Digital
Connect, Lehman’s one-stop-
shop for rich media
content.
Through an intuitive
categorization system,
videos are published to
multiple web properties
from a single content
source.

8. 8
Ecosystem in Practice
 Personalized Delivery
of Content
Via Active Directory login,
student course schedules,
grades, alerts, and
notifications can be
delivered to our secure
intranet site, Lehman
Connect.
Personalized content will
soon be sent to students via
college’s mobile app.
Critical alerts will appear as
notifications.

9. From Data to Knowledge
 Big Data: Data from traditional & digital sources for
discovery and analysis. Characterized by 3 V’s.
 Business Intelligence: Tools and practices used to
analyze & optimize decisions and performance.
 Analytics: Statistical discovery of meaningful
patterns for predictive scenarios
9

10. Different Questions & Tools
10
Reporting/BI > Analytics > Prescriptive
Davenport,
12/13 HBR

11. Lehman Examples
What is happening: LCD/BI reporting on
enrollment and retention
What is likely to happen: Rapid Insight
Analytics/predictive modeling
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12. Lehman College: Predictive Analytics
 Lehman is pursuing the power of regression and
predictive analytics.
 Regression analysis: study of statistical relationships
among dependent and independent variables.
 Based on the variables, we may be able to impact
student attrition, enrollment, graduation rates, etc.
 End result of the analysis: a predictive model,
suggesting intervention strategies and possible
outcomes in future semesters.
12

13. Student Attrition Model
 We studied attrition in a cohort of 454 FT/FT freshman students
that started in Fall 2011& followed their attrition rates through
Spring 2014.
 Relationships among attrition and 100+ parameters were
examined, including probation status, SAT scores, credits
attempted/earned, cumulative GPA, etc., for each semester.
 The model showed a 26% attrition rate though Spring 2014:
 336 students were retained and 118 attrited.
 The model predicted that 109 students would attrit at the end of
the Spring 2014 semester.
 Actual data shows 118 students attrited.
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14. Attrition Prediction Model

15. Visualization: Relationship between Attrition &
First Semester Earned Credits
15

16. Attrition and First Term Probation
16

17. Predicted attrition probability for
each student in the sample.