Leveraging Data-Analytics as a Service Learning Project to Support Nonprofit Organization Volunteer Recruitment Strategies

OVERVIEW Many of the Jesuit business schools and colleges have recently ramped up efforts to develop data analytic programs and classes. As we help our students gain the skill necessary for analyzing large sets of data, it is important to help students see ways in which data analysis can contribute to the common good. Our study is an example of such analysis, which found that not only are post-millennials and millennials less likely than older gen-Xers or babyboomers to volunteer as mentors in the Big Brother Big Sister (BBBS) programs, but that when marketing efforts were focused on post-millennials and millennials, their numbers suffered, correlatively. As a result of this study, the BBBS organizations involved are likely to make changes to their marketing strategies. We provide our conclusions from this study, then go on to suggest a number of possible service learning opportunities for students to help them use their data analysis skills. BACKGROUND BBBS creates and supports volunteer mentoring relationships with children, designed to ignite the potential within children. “Matches” involve assignments of adult volunteer mentors to mentees, who may be young adults but can also be children as young as 5 years of age. Facing a decline in the percentage of inquiries that yield a commitment to volunteer, the current study started as an exploratory analysis of factors influencing the propensity to volunteer in one midwestern city in the United States. A second study at another midwestern city in the United States was conducted to enhance the robustness of the findings. Findings include an analysis of the propensity to volunteer across different generations of volunteers, as well as the efficacy of marketing channels in reaching them. METHODS Data were collected on existing Matches as well as prospective volunteer inquires across two BBBS sites. The combined data consist of 1,000 current Matches and 12,826 volunteer inquiries from 2009 through 2019. Descriptive analysis of existing Matches provided comparisons between each site’s Match profiles by age, length of match, generation, gender, race, proximity, and employer size. A logistic regression model was developed to identify factors that predict the propensity to volunteer from inquiries. RESULTS We find that compared to Millennials, Boomers (1.64 times) and Gen-Xr’s (1.37 times) are significantly more likely to volunteer to be Matches. Further, inquiries from individuals associated with community partners and personal referrals are twice as likely to volunteer to become matches as compared to inquires received through the BBBS website. Regression models revealed the significant influence of marketing channels by generation. Findings also suggest an increased focus on marketing to Millennials results in overall lower Match conversion rates. DISCUSSION It is important for non-profit organizations to optimally allocate their limited resources in service of their marketing needs. Today, society is in the midst of a generational shift with Millennials representing the largest demographic, taking over for the Boomers who are increasingly moving into retirement. Using data from two cities for one non-profit organization, this study presents an example of how BBBS could recruit volunteers from different generations using appropriate marketing channels of communication. The procedure used in this study also provides a template for how students from Jesuit schools could leverage their data analytics skills in service to non-profit organizations.