Problem Statement

Many nonprofits celebrate a new donor or member but lose most of them soon after the first gift. Industry benchmarks from the Fundraising Effectiveness Project (FEP) show that only about one in five newly acquired donors gives again the next year (NonProfit PRO 2025).

The Glendora Foundation faces the same risk with its October $25 members: it does not yet know whether this low cost entry point leads to lasting donor engagement.

Analytics Objective (AO3)

Importance

Theory: Why Do Some Donors Stay?

Relationship fundraising research finds that donor loyalty depends on satisfaction, trust, and commitment, and that donors who feel uninformed or disconnected are more likely to lapse (Sargeant 2001; Sargeant and Woodliffe 2007).

Why Do People Give? Eight Mechanisms

A review of more than 500 studies identified eight mechanisms behind charitable giving, including awareness of need, solicitation, and efficacy (Bekkers and Wiepking 2011).

• Solicitation: the November campaign and GivingTuesday asks
• Awareness of need: cultivation messages about the Foundation’s work
• Efficacy: showing members what their gift achieved

What Does the Evidence Show About Retention?

Is the Existing Information Adequate?

Most published work studies donor-to-donor retention at large organizations or online platforms. It says less about how a low-cost membership leads into repeat giving and event participation at a small foundation.

Data and Sampling

The cohort contains 120 October $25 members (illustrative placeholder; replace with the real count from the CRM export). Each member is followed from joining through the November campaign, GivingTuesday (December 1, 2026), and the March 2027 Sip and Support event.

Data Wrangling

Records from separate CRM tables are combined with left_join() so every member stays in the cohort, even if they did nothing after joining.

# PLACEHOLDER: simulated data. Replace with your CRM export, e.g. read_csv("crm.csv")
set.seed(2026)
n_members <- 120

members <- tibble(member_id = 1:n_members)
nov     <- tibble(member_id = sample(members$member_id, 30), gave_nov = TRUE)
gt      <- tibble(member_id = sample(members$member_id, 22), gave_gt = TRUE)
rsvp    <- tibble(member_id = sample(members$member_id, 18), rsvp = TRUE,
                  attended = rbinom(18, 1, 0.7) == 1)

cohort <- members |> 
  left_join(nov,  by = "member_id") |> 
  left_join(gt,   by = "member_id") |> 
  left_join(rsvp, by = "member_id") |> 
  mutate(across(c(gave_nov, gave_gt, rsvp, attended), ~ replace_na(.x, FALSE)),
         repeat_donor = gave_nov | gave_gt)

Sample Characteristics: Journey Funnel

funnel <- tibble(
  stage = factor(c("October $25 members", "Repeat donors\n(Nov or GivingTuesday)",
                   "RSVP'd to\nMarch event", "Attended\nMarch event"),
                 levels = c("October $25 members", "Repeat donors\n(Nov or GivingTuesday)",
                            "RSVP'd to\nMarch event", "Attended\nMarch event")),
  n = c(nrow(cohort), sum(cohort$repeat_donor), sum(cohort$rsvp), sum(cohort$attended))
) |> mutate(pct = n / first(n))

ggplot(funnel, aes(stage, n)) +
  geom_col(fill = "#1a4d7a") +
  geom_text(aes(label = paste0(n, " (", scales::percent(pct, accuracy = 1), ")")),
            vjust = -0.4, size = 5) +
  labs(x = NULL, y = "Members") +
  theme_minimal(base_size = 16)

Figure 1: PLACEHOLDER: member journey from October to March (simulated)

Summary Table

funnel |> 
  transmute(Stage = str_replace(stage, "\n", " "), Members = n,
            `% of cohort` = scales::percent(pct, accuracy = 0.1)) |> 
  knitr::kable()

In this simulated example, 46 of 120 members (38.3%) became repeat donors.

Measures

Analytics Methods

Useful Links

• Fundraising Effectiveness Project
• Sargeant (2001), Relationship fundraising
• Bekkers & Wiepking (2011), Eight mechanisms of giving
• Althoff & Leskovec (2015), DonorsChoose.org
• Quarto Revealjs guide