Higher education institutions are expected to deliver a high-quality education while competing for enrollment. Each institution has unique features that attract students to apply, but most share the same challenge of retaining students after enrollment. The challenge does not seem to have an ideal solution in most higher education institutions. This study integrates Lean Six Sigma and data analytics to establish a standardized process for improving student retention rates. Data sets pertaining to admission, enrollment, transfer and dropout were analyzed to identify opportunities for improving the retention rate. Descriptive and predictive analytics models were developed to find trends in the data and predict future retention outcomes. Finally, recommendations are made to help higher education institutions to better understand and improve student retention.