One of the largest education companies in Brazil and globally, this organization has a vast network of institutions, spanning primary, secondary, and higher education. It plays a pivotal role in shaping the educational landscape, operating through multiple brands and serving millions of students across various stages of education.
A leader in digital education, the company invests heavily in technology, offering online learning platforms and hybrid models, which have become essential to its growth strategy.
The company faced a significant challenge in improving its credit recovery strategy for inactive students, primarily due to the lack of continuous engagement and limited behavioral data for comparison. The existing approach relied solely on payment delays, without utilizing the full range of available behavioral data. Our task was to implement data-driven intelligence to consolidate this data and select precise credit recovery offers, predicting each student’s likelihood to accept while maximizing profitability.
Although the initial focus was on inactive students, the goal was to scale this intelligent approach across all credit recovery processes, optimizing outcomes at every stage.
Our primary goal was to enhance communication efficiency and optimize the credit recovery process by selecting tailored offers with a high probability of acceptance. Success would be measured by maximizing offer acceptance rates while ensuring the highest possible profitability, ultimately reducing the average discount currently offered.
The ultimate goal was clear: to drive more effective and profitable recoveries through data-driven and personalized strategies, generating valuable insights for the creation of future credit offers.
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