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Institutional Success Via a Data-Centric Technology Ecosystem

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The content and data landscape is vast and decentralized, and aggregating assets via a unified technology strategy poses significant challenges. This presentation describes how to design a federated technology ecosystem to inform recruitment, marketing, and outreach efforts, and increase student retention through the innovative use of data modeling and predictive analytics. Collecting data for actionable insight, leveraging CRM, web, and mobile channels, and tracking retention and graduation rates will be highlighted.

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Institutional Success Via a Data-Centric Technology Ecosystem

  1. 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. 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. 3 Ecosystem At a Glance
  4. 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. 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. 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. 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. 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. 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. 10. Different Questions & Tools 10 Reporting/BI > Analytics > Prescriptive Davenport, 12/13 HBR
  11. 11. Lehman Examples What is happening: LCD/BI reporting on enrollment and retention What is likely to happen: Rapid Insight Analytics/predictive modeling 11
  12. 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. 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. 13
  14. 14. Attrition Prediction Model
  15. 15. Visualization: Relationship between Attrition & First Semester Earned Credits 15
  16. 16. Attrition and First Term Probation 16
  17. 17. Predicted attrition probability for each student in the sample.

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