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BRIDGEi2i helps premier US University predict and identify applicants with higher risk of dropouts. This helped the customers build an effective and timely retention strategy. More about BRIDGEi2i at http://www.bridgei2i.com
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For more details contact us: [email protected]
© BRIDGEi2i Analytics Solutions
Customer Case Study
Information Insight Impact
BRIDGEi2i helps a premier US university to be predictive in identifying applicants and
students with higher risk of drop out and build effective & timely intervention strategies.
Business Challenge The client organization is a leading US university with 10,000+ students in variety of programs across Science, Arts,
Management, Nursing, etc. The university spends significant resources & effort on student reach out, awareness
programs and admissions process to attract the best and suitable talent to the university. It is also financially critical
that students stay through the period, since it impacts state funding to the university and also results in unutilized
capacity within a program. The University was very keen to identify the drivers of early attrition and to identify
profiles of students with high dropout risk so that appropriate interventions can be planned in a timely manner to
maximize retention.
BRIDGEi2i Solution Predictive models were built leveraging advanced statistical methods that help not only select applicants with a
higher retention propensity but also identify students with a significantly higher risk of dropping out.
BRIDGEi2i also identified key variables and triggers from both application and course performance data that will
enable the university to design and implement effective intervention strategies aimed at maximizing retention.
Key Drivers of Drop out
Performed in-depth analysis of all
applicant and student related data and
information to identify the most significant
parameters that affects retention.
Predictive Retention Models
BRIDGEi2i developed couple of statistically
robust predictive models that predicts high
risk applicants and identifies “at-risk”
students at various points of lifecycle. One
of the models was useful to identify such
applicants who might leave within 3-6
months’ time and the other one helped to
identify drop outs at the end of first year
based on their performance and
engagement during the first semester.
For more details contact us: [email protected]
© BRIDGEi2i Analytics Solutions
Customer Case Study
Information Insight Impact
Intervention Strategy Development
We supported the client to identify specific
triggers that help development of targeted
intervention strategies aimed at improving
retention
Business Impact An illustrative simulation exercise by the university estimated an annual saving in the tune of USD 180K due to
incremental retention.
About BRIDGEi2i BRIDGEi2i is an analytics solutions company partnering with businesses globally, helping them achieve accelerated
outcome harnessing the power of data. BRIDGEi2i helps companies to BRIDGE the gap between INFORMATION,
INSIGHT and IMPACT in their journey to institutionalize data driven decisions across the enterprise.
