Optimized marketing spend
Identification of optimum students
Increased ROI
An American higher education provider wanted to optimize its marketing spend on students for their campus-based and online programs. Therefore, they wanted to transition from using a third-party owned lead scoring model to a custom, in-house model that would enable them to identify, evaluate, and take action on the students on the basis of the lead quality or likelihood to enroll in a course.
Google's BigQuery
Google Cloud Storage
Google Compute Engine
JupyterLab
Quantiphi developed a Machine Learning-based, custom Lead Scoring Model that generates the probability scores of respective leads that enroll in either the campus-based or online programs. These predictive conversion probability scores are then used to categorize the leads into different Bins A-G, in decreasing order of their likelihood to convert. This provides the customer with insights from the underlying factors per lead that affect the likelihood of enrollment. Google’s BigQuery helps to analyze, clean, transform, and process the data. Feature engineering was also performed on the final dataset in BigQuery. Model training was performed using different classification models to identify the best performing model using instance running on Google Compute Engine.
