Promoting Saving for College Through Data Science

TL;DR

This paper demonstrates how data science techniques, including person matching and lookalike modeling, can effectively identify and target families likely to save for college, thereby increasing enrollment in college savings plans.

Contribution

It introduces a novel approach combining person matching and racially balanced lookalike modeling to target potential college savers and promote savings plan enrollment.

Findings

Increased signups for college savings plans through targeted digital advertising.

Effective use of racially and economically balanced lookalike models.

Enhanced outreach without reinforcing demographic disparities.

Abstract

The cost of attending college has been steadily rising and in 10 years is estimated to reach $140,000 for a 4-year public university. Recent surveys estimate just over half of US families are saving for college. State-operated 529 college savings plans are an effective way for families to plan and save for future college costs, but only 3% of families currently use them. The Office of the Illinois State Treasurer (Treasurer) administers two 529 plans to help its residents save for college. In order to increase the number of families saving for college, the Treasurer and Civis Analytics used data science techniques to identify the people most likely to sign up for a college savings plan. In this paper, we will discuss the use of person matching to join accountholder data from the Treasurer to the Civis National File, as well as the use of lookalike modeling to identify new potential signups. In order to avoid reinforcing existing demographic imbalances in who saves for college, the lookalike models used were ensured to be racially and economically balanced. We will also discuss how these new signup targets were then individually served digital ads to encourage opening college savings accounts.