Welcome to the 2026 North American Prospect Development Benchmark Study, a groundbreaking effort to establish the first comprehensive, data-driven benchmarks for prospect development operations across the continent.

With data from 500 participating organizations, this study offers a valuable glimpse into the practices, investments, and outcomes shaping the field of prospect development. From staffing and productivity benchmarks to the relationship between operational maturity and revenue influence, these findings are designed to inform and empower both practitioners and leadership.

Findings overview

This section highlights the key findings from the 2026 North American Prospect Development Benchmark Study, crafted to provide immediate value for senior leadership and board-level discussions. Among the 500 participating organizations, the study reveals a field that is meaningfully invested in research operations but still developing the pipeline discipline needed to connect research effort to fundraising outcomes.

Research investment at a glance

To give you the clearest picture of the current landscape, all figures are presented below as medians with interquartile ranges (IQR). Think of these numbers as a powerful compass rather than a strict map. Use these as benchmarks—not prescriptions—for your own shop.

Among participating organizations, the median research staffing is 0.75 FTE, with a median annual research spend of $50,000. Tool and technology expenditures sit at a median of $17,500. These figures reflect substantial variation across the sample, with smaller organizations often operating with fractional research staff and limited budgets while larger institutions maintain multi-person teams with six-figure technology stacks.

Note: a glossary of terms can be found here.

The pipeline conversion funnel

The conversion funnel reveals where prospect research connects to fundraising action. Among respondents who track these metrics, the median rates are: Qualified-to-Assigned at 12.5% (n=500), Assigned-to-Solicited at 17.0% (n=386), and Solicited-to-Closed at 14.5% (n=352). The declining sample sizes at each stage are themselves a finding: 23% of respondents do not track solicitation rates, and 30% do not track close rates, suggesting that many organizations lack visibility into the downstream impact of their research.

Maturity at a glance

The Prospect Development Maturity Index (PDMI) provides a composite measure of operational maturity based on five self-assessed practices: CRM logging, gift officer action on research, pipeline tracking, portfolio hygiene, and ability to demonstrate ROI. Its internal reliability (Cronbach’s α = 0.761) exceeds the conventional 0.75 threshold, confirming that the five items form a coherent measure of a single underlying construct. Among all 500 respondents, the median PDMI score is 65 (IQR: 50–75) on a 0–100 scale, with a mean of 59.7.

The largest single group (38%) falls in the Advanced tier (scores 60–79), while 20% reach Leading (80–100). A combined 42% remain in the Emerging (14%) or Developing (28%) tiers, indicating that more than two in five organizations have significant room for maturity improvement.

The strongest signal: Maturity and revenue

The most robust finding in this study is the relationship between organizational maturity and revenue influenced by research. Among all 500 respondents, PDMI score correlates with self-reported revenue influenced at ρ = 0.453 (p < 0.001)—the strongest correlation observed in the study. This association holds after controlling for organizational revenue (ρ = 0.294, p < 0.001) and strengthens among respondents with higher data confidence (ρ = 0.499, p < 0.001, n=286). These results suggest that organizations reporting higher maturity practices also report greater revenue attributed to research, though the cross-sectional design means we cannot establish causation.

Operational performance benchmarks

This section provides the operational benchmarks that practitioners can use to evaluate their team’s capacity, productivity, and cost efficiency against the broader field.

Research staffing

Research FTE allocation varies dramatically across the sample. The median of 0.75 FTE masks a distribution that ranges from organizations with less than half an FTE dedicated to research (often a shared function) to institutions with 20 or more full-time research professionals. The interquartile range of 0.25–3.0 FTE captures the core of the field, while the long right tail reflects large university advancement offices and healthcare systems that have invested in dedicated research teams.

Among our respondents, 254 (51%) do not have dedicated prospect research staff, while 246 (49%) do. The most common research model is fully in-house (332 organizations, 66%), followed by mostly in-house with external support (110, 22%), distributed/no central function (36, 7%), and mostly outsourced (22, 4%).

Staffing the machine: Research FTE, Gift Officer FTE, and the ratio between them

One of the most practical questions a prospect development leader can bring to a budget conversation is: how many staff should we have for our number of gift officers? The data from 500 organizations provides the clearest answer the field has had to date, and it is not the answer most people expect.

The 270 organizations with only 1–2 MGOs represent 54% of the sample. Their median research allocation of 0.25 FTE—a quarter of someone’s time—is the single most common staffing configuration in the dataset.

Research FTE and MGO count are strongly correlated at ρ = 0.778 (p < 0.001, n = 500). This is one of the strongest associations in the entire dataset, exceeded only by total advancement staff and MGO count (ρ = 0.900). Research teams and frontline fundraising teams scale together in near-lockstep.

The median ratio across all 500 organizations is one research FTE for every six major gift officers (0.17). The interquartile range runs from 0.17 to 0.38, meaning the middle half of organizations staff somewhere between one researcher for every six MGOs and roughly one for every three.

That ratio is not evenly distributed. It shifts meaningfully with organizational maturity—Leading-tier organizations invest nearly twice as much research capacity per gift officer as the field median—and it varies sharply by the size of the frontline team, as detailed in the tables below.

How the ratio changes with maturity

Leading-tier organizations invest nearly twice as much research capacity per gift officer (1:3) as the overall field median (1:6). The jump from Developing to Advanced is notable: organizations move from 0.25 FTE to 1.50 FTE, and the ratio tightens from 1:6 to 1:5.

Developing-tier organizations (PDMI 26–50, n = 41) show the same 0.17 ratio but with slightly more headcount on both sides: a median 0.75 research FTE supporting a median 1.5 MGOs. The research function exists but is typically a fraction of someone’s job.

Advanced-tier organizations (PDMI 51–75, n = 101) edge up to 0.19 — a median of 1.5 research FTE supporting 4.0 MGOs. This is the tier where most organizations have at least one dedicated researcher, and the largest segment of the dataset sits here.

Leading-tier organizations (PDMI 76–100, n = 54) are where the ratio shifts noticeably: 0.38, or roughly one researcher for every 2.6 gift officers. The median research team is 3.0 FTE supporting 8.0 MGOs. These organizations invest nearly twice the research-to-MGO ratio of the rest of the field.

The ratio itself is positively correlated with PDMI (ρ = 0.290, p < 0.001) — organizations that devote proportionally more research capacity relative to their frontline are measurably more mature in their operations. It is also associated with more qualified prospects entering the pipeline (ρ = 0.286, p < 0.001) and modestly with higher total revenue (ρ = 0.216, p = 0.001).

The understaffed outlier

15 organizations in the sample reported 8 or more MGOs but less than 1.0 research FTE. Their median PDMI score is 65—indicating that understaffing research relative to gift officer capacity limits maturity outcomes.

The small-shop reality

193 organizations—39% of the sample—have less than half a research FTE and only one to two gift officers. Their median PDMI is 50. Two-thirds are independent nonprofits. Nearly all have total revenue under $5 million. This is the modal configuration of the field.

The finding that matters for these organizations is not the 1:5 ratio—it’s the behavioral correlations. Lookup frequency is associated with PDMI at ρ = 0.417, nearly as strong as the correlation between research FTE and PDMI (0.425). In other words, how often you look matters almost as much as how many people you’re looking at. A half-time researcher who checks every donor before every meeting may contribute more to operational maturity than a full-time researcher who produces quarterly profiles that sit in a shared drive.

The ratio does not predict efficiency

There is no meaningful relationship between the research-to-MGO ratio and cost per qualified prospect (ρ = +0.140, p = 0.002, n = 500). Investing proportionally more in research does not reliably lower per-prospect costs. The ratio predicts maturity and output volume, but not unit economics.

Productivity: Prospects per FTE

The median prospect output is 100 qualified prospects per FTE (IQR: 50–100). This metric captures raw throughput and is useful for staffing models, but should be interpreted cautiously: a high number may reflect efficient processes or lower qualification standards, while a low number may indicate deeper research or smaller pipelines.

Cost efficiency: cost per qualified prospect

The median cost per qualified prospect is $500 (IQR: $500–$1,333). This figure is derived by dividing the midpoint of each respondent’s research spend band by the midpoint of their qualified prospects band. The relatively tight clustering around $500 suggests a floor effect from the band structure, while the upper IQR of $1,333 reflects organizations that either spend more on research or qualify fewer prospects.

A null result: Spend does not predict productivity

One of the study’s most important findings is what it did not find. Research spend shows a weak negative correlation with prospects per FTE (ρ = −0.156, p < 0.001). Spending more does not automatically make a team more productive per capita. This likely reflects scale effects: larger organizations spend more but also have more complex, time-intensive research processes. The takeaway is that investment and efficiency are separate levers.

Portfolio health benchmarks

Portfolio health indicators reveal how effectively organizations manage their prospect pipelines after research has been completed. These metrics speak directly to the handoff between research and frontline fundraising.

Portfolio size

The median portfolio size among respondents is 75 prospects (IQR: 25–125). This refers to the number of prospects actively assigned to gift officers at a given time. The distribution skews toward smaller portfolios, with a substantial proportion of organizations maintaining portfolios under 50 prospects.

Inactive prospects and maturity

The proportion of inactive or stalled prospects in the pipeline provides a signal of portfolio hygiene. Across all PDMI tiers, the median inactive rate holds steady at around 17%. While Leading-tier organizations show a tighter upper range (IQR capped at 17% vs. 32–50% for other tiers), the overall pattern suggests that portfolio hygiene is a universal challenge rather than a problem solved by maturity alone.

Assignment rates and maturity

Assignment rates show a clearer maturity gradient. Emerging organizations cluster at a median of 12.5% assignment rate, while Advanced and Leading organizations achieve median rates of 37.0%—nearly three times higher. This suggests that mature organizations are substantially better at converting qualified research into active gift officer portfolios.

Screening & discipline benchmarks

Screening and lookup practices reflect the operational discipline of the research function. This section examines how frequently organizations conduct batch screening reviews and individual prospect lookups.

Batch screening frequency

Batch screening practices reveal a field in transition. While 77 organizations (15%) report continuous or automated screening, the most common response is ad hoc (153 organizations, 31%), and 112 (22%) never batch screen at all. Regular cadences (quarterly, semi-annually, annually) collectively account for 158 organizations (32%).

Individual lookup frequency

Individual lookups paint a more active picture: 192 organizations (38%) conduct daily lookups, and another 158 (32%) do so weekly. Only 73 (15%) rarely or never conduct individual research. This suggests that even organizations without formal batch processes maintain active ad-hoc research habits.

Maturity index findings

The PDMI score is the arithmetic mean of five Likert-scale items, each scaled 0–100. The five practices are: 

1. Research outputs are consistently logged in CRM
2. Gift officers act on research recommendations
3. We track movement from qualified to assigned to solicited
4. We regularly remove low-quality prospects from portfolios
5. We can demonstrate research improves pipeline efficiency. 

Its internal reliability (Cronbach’s α = 0.761) exceeds the conventional 0.75 threshold for acceptable reliability, confirming that the five items hang together as a coherent measure of a single underlying construct. This is an improvement from the preliminary dataset (α = 0.733), likely reflecting the greater variance contributed by the expanded and more diverse sample.

Score distribution

Among all 500 respondents, the median PDMI score is 65 (IQR: 50–75), with a mean of 59.7. The largest single group (38%) falls in the Advanced tier, while 20% reach Leading. A combined 42% remain in the Emerging or Developing tiers.

Maturity by organization type

Healthcare foundations report the highest median PDMI (70), followed by higher education (65). Independent nonprofits score lower on average (55), likely reflecting their smaller staffing and budgets rather than lower ambition. Community foundations (n=14) report a median of 58. The healthcare finding, while based on a smaller sample, aligns with the sector’s emphasis on data-driven donor management and compliance documentation.

Item-level analysis

The weakest practice area is portfolio hygiene—“We regularly remove low-quality prospects from portfolios”—with a mean score of 3.10 on a 1–5 scale. This is the only item where the mean falls below 3.3, making it the clearest opportunity for improvement across the field. Gift officer action on research (3.57) and CRM logging (3.52) score highest. This pattern makes intuitive sense: logging research in a CRM is a relatively straightforward procedural step, while maintaining active portfolio hygiene—regularly reviewing, reassigning, and removing prospects—requires ongoing organizational discipline.

Revenue influence

This section explores the direct financial outcomes reported by participating organizations, focusing on the revenue that research operations influence. The primary and most robust finding thus far is a strong, statistically significant correlation between an organization’s Prospect Development Maturity Index (PDMI) score and its self-reported philanthropic revenue influenced by research. Despite the enormous variation in self-reported influenced revenue, the link between operational maturity and financial outcomes persists even when controlling for organizational revenue. Furthermore, we examine how maturity directly relates to an organization’s ability to track key pipeline outcomes, revealing that many organizations cannot improve what they do not measure.

Revenue influenced by research

The largest group of respondents (206, 41%) report research influencing under $100k in major gift revenue. However, the distribution has a long right tail: 135 organizations (27%) report $1M or more in research-influenced revenue, with 47 (9%) reporting $10M+. The wide IQR underscores that “revenue influenced” means very different things across the sample—from small organizations attributing under $100,000 to large institutions reporting $10 million or more.

PDMI and revenue influence

The relationship between maturity and revenue influence is striking. Emerging-tier organizations report a median of $50,000 in research-influenced revenue. Leading-tier organizations report a median of $3,000,000—a 60x difference. While organizational size partly explains this gap (larger organizations tend to be both more mature and higher-revenue), the partial correlation after controlling for organizational revenue (ρ = 0.294) confirms that maturity contributes independently.

“Not Tracked” as a maturity signal

Among all 500 respondents, 109 (22%) do not track the percentage of major gift revenue involving researched prospects. This rate varies sharply by maturity: 28% of Emerging organizations don’t track this metric, compared to 14% of Leading ones. The pattern is even more pronounced for pipeline metrics: 43% of Emerging organizations don’t track solicitation rates versus only 11% of Leading organizations.

Additional correlations: Annotations and monitoring status

The full correlation analysis produced 45 pairwise Spearman correlations across the n = 500 dataset. Below, we organize these into three categories based on their strength, significance, and interpretive interest. Each correlation is annotated with a brief interpretation.

Noted—Not surprised

These correlations are statistically significant and directionally expected. They confirm that the PDMI is measuring something real and that organizational scale correlates with both investment and outcomes.

Total Staff FTE <-> MGO FTE (ρ = +0.900, n = 500): Larger advancement shops employ more gift officers. This is definitional and serves as a dataset validity check.

Total Staff FTE <-> Research FTE (ρ = +0.810, n = 500): Same logic. Research headcount scales with overall department size.

MGO FTE <-> Total Revenue (ρ = +0.803, n = 500): Organizations with more gift officers report more fundraising revenue; direction is ambiguous but expected.

MGO FTE <-> MG Revenue (ρ = +0.795, n = 500): Same pattern, specific to major gift revenue.

Research FTE <-> MGO FTE (ρ = +0.778, n = 500): Research and frontline staffing scale together, suggesting coordinated investment rather than trade-offs.

MGO FTE <-> Research Spend (ρ = +0.773, n = 500): Frontline and research budgets co-scale with organizational size.

Research Spend <-> Total Revenue (ρ = +0.723, n = 500): Budget scales with size. No surprise here.

Research FTE <-> Total Revenue (ρ = +0.721, n = 500): Bigger organizations invest more in research headcount.

Research Spend <-> Prospects Qualified (ρ = +0.728, n = 500): Higher budgets produce more qualified prospects—the spending-output link is real.

Research FTE <-> Prospects Qualified (ρ = +0.714, n = 500): More researchers produce more qualified prospects. The strongest operational finding.

Research Spend <-> MG Revenue (ρ = +0.710, n = 500): Research spending and major gift revenue travel together.

Tool Spend <-> Total Revenue (ρ = +0.699, n = 500): Larger organizations spend more on tools. Researchers and technology are complements, not substitutes.

Research FTE <-> MG Revenue (ρ = +0.688, n = 500): More researchers in organizations raising more in major gifts.

Research FTE <-> Tool Spend (ρ = +0.671, n = 500): More researchers buy more tools—confirming they are complementary investments.

Tool Spend <-> Prospects Qualified (ρ = +0.669, n = 500): Higher tool investment associated with more qualified output.

Research FTE <-> Prospects Researched (ρ = +0.633, n = 500): More researchers research more prospects. Definitional but important to verify.

MG Threshold <-> Revenue (ρ = +0.619, n = 500): Larger organizations set higher major gift floors. Scale-driven.

Portfolio Size <-> Revenue (ρ = +0.598, n = 500): Bigger organizations manage larger portfolios.

MGO FTE <-> Portfolio Size (ρ = +0.485, n = 500): More gift officers equals larger aggregate portfolio. Moderate and expected.

PDMI <-> Revenue % Attributed (ρ = +0.465, n = 391): Organizations with higher maturity attribute a larger share of major gift revenue to research.PDMI <-> Revenue Influenced (ρ = +0.453, n = 500): The headline finding. Maturity predicts self-reported revenue influenced. Strongest non-scale correlation.

Continuing to monitor

These relationships are statistically significant but moderate in strength. They merit continued attention in future waves of the study.

Research FTE <-> PDMI (ρ = +0.425, n = 500): More research staff is associated with higher maturity, but when controlling for organizational size (within the $20M–$99.9M revenue band), this correlation vanishes (ρ = 0.060, p = 0.55, n = 99). Research FTE may be a proxy for organizational size, not an independent driver of maturity.

Lookup Frequency <-> PDMI (ρ = +0.417, n = 500): This is the strongest behavioral (non-size-driven) predictor of maturity in the dataset. Organizations where researchers look up donors more frequently—daily or weekly, rather than monthly or rarely—score meaningfully higher on PDMI. The finding is intriguing because lookup frequency is a habit, not a resource. It may be the single most actionable recommendation in the study: look things up more often.

Batch Screening Freq <-> Qualified (ρ = +0.398, n = 500): Organizations that screen more frequently qualify more prospects. This is one of the five core hypothesis tests.

Batch Screening Freq <-> PDMI (ρ = +0.391, n = 500): Regular screening discipline correlates with organizational maturity. Together with lookup frequency, this suggests that research cadence matters more than research budget.

PDMI <-> Assignment Rate (ρ = +0.384, n = 500): More mature organizations assign qualified prospects at higher rates. This is a core finding.

MGO FTE <-> PDMI (ρ = +0.383, n = 500): More gift officers correlate with higher maturity. Likely a scale effect.

Pct Assigned <-> Revenue Influenced (ρ = +0.326, n = 500): Higher assignment rates are associated with more research-influenced revenue. The pipeline works when prospects actually reach gift officers.

Research-to-MGO Ratio <-> PDMI (ρ = +0.290, n = 500): Organizations that invest proportionally more in research relative to frontline staff tend to be more mature.

Research-to-MGO Ratio <-> Prospects Qualified (ρ = +0.275, n = 500): A higher research-to-MGO ratio produces more qualified prospects.

Portfolio Size <-> PDMI (ρ = +0.276, n = 500): Larger portfolios associate with higher maturity. May reflect organizational scale.

Pct Assigned <-> Revenue (ρ = +0.245, n = 500): Assignment rates weakly predict total fundraising revenue. The pipeline effect, diluted by other factors.

Ratio <-> Pct Assigned (ρ = +0.217, n = 500): Better-resourced research teams have higher assignment rates. The ratio-to-action connection.

Research-to-MGO Ratio <-> Revenue (ρ = +0.217, n = 500): The ratio weakly predicts revenue. Maturity, not just staffing, matters more.

Effort per Qualified <-> Revenue (ρ = +0.175, n = 500): NEW FINDING: Organizations where qualification takes longer report higher revenue. Deeper research may produce better-quality prospects. This was null at n=220 and emerged with the expanded sample.

Qual Criteria Count <-> PDMI (ρ = +0.173, n = 500): NEW FINDING: Organizations using more qualification criteria (capacity, affinity, engagement, committee approval) score higher on maturity. More rigorous qualification standards signal more mature operations. This was null at n=220.

Effort per Qualified <-> PDMI (ρ = +0.143, n = 500): NEW FINDING: Longer qualification effort associates weakly with higher maturity. Mature organizations may invest more time per prospect. Was null at n=220.

Time to Assign <-> Revenue (ρ = +0.141, n = 368): Speed to assignment showed a weak but significant correlation with revenue. This was null at n=220; we continue monitoring.

Research-to-MGO Ratio <-> Cost per Qualified (ρ = +0.140, n = 500): Investing proportionally more in research does not reliably lower costs. The ratio predicts maturity and volume, but not unit economics.

PDMI <-> Inactive % (ρ = -0.125, n = 500): Higher maturity weakly predicts lower inactive rates, but the effect is small.

Null Findings—Noted and worth reporting

The following tests produced weak or non-significant results. Null findings are important: they tell us what doesn’t predict what we might expect.

Cost per Qualified <-> PDMI (ρ = +0.086, n = 500): Maturity has nothing to do with how much you spend per prospect. Mature organizations are not more cost-efficient on a per-prospect basis; they simply produce more prospects that reach gift officers.

Cost per Qualified <-> Total Revenue (ρ = +0.108, n = 500): Spending more per qualified prospect does not predict higher revenue. Cost efficiency is not a revenue driver at the organizational level.

Qualified per FTE <-> PDMI (ρ = -0.101, n = 500): Raw throughput per researcher does not predict maturity. Being faster does not mean being better.

Qual Criteria Count <-> Revenue (ρ = +0.016, n = 500): Using more qualification criteria does not predict higher revenue. Rigor in qualification may improve quality, but does not directly translate to dollars.

Time to Assign <-> PDMI (ρ = +0.078, n = 368): Speed to assignment does not predict maturity. This challenges conventional wisdom that faster handoffs signal better operations.

Methodology + Advanced analytical findings

Study purpose

This study provides the first large-scale benchmark of prospect development practices in North America, with the goal of giving practitioners empirical reference points for staffing, investment, operational cadences, and outcomes.

Study design

Cross-sectional survey of prospect development professionals, distributed through professional networks and associations from March through May 2026. Respondents self-selected into the study. All analyses are correlational; no causal claims are made.

Sample & data quality

An in-progress version of this report was deployed at n=220 to share some early findings. The final sample includes 500 respondents. Among these, 286 (57%) report high confidence in their data (using reports), 204 (41%) report moderate confidence, and 10 (2%) report low confidence. The geographic breakdown is 455 U.S. and 45 Canadian organizations.

Prospect Development Maturity Index (PDMI)

The PDMI is a composite index calculated as the arithmetic mean of five Likert-scale items, each coded: Strongly Disagree = 0, Disagree = 25, Neutral = 50, Agree = 75, Strongly Agree = 100. The resulting 0–100 scale is divided into four tiers: Emerging (0–39), Developing (40–59), Advanced (60–79), and Leading (80–100). The instrument achieves a Cronbach’s α of 0.761, exceeding the conventional 0.75 threshold for acceptable reliability. This represents an improvement from the preliminary dataset (α = 0.733 at n = 220), likely attributable to the greater variance contributed by the expanded and more diverse sample.

Statistical approach

All continuous measures are reported as medians with interquartile ranges (IQR) due to the ordinal and skewed nature of band-based survey responses. Spearman’s rank correlation (ρ) is used for bivariate associations. Partial correlations control for confounders using rank-based residuals. Significance is reported at α = 0.05 with common thresholds: * p < 0.05, ** p < 0.01, *** p < 0.001. Band midpoints are used to approximate continuous values from categorical response options.

Advanced analytical findings

This section presents the five core analytical tests specified in the study design, along with partial correlation results that control for organizational revenue as a potential confound.

Interpreting the updated test 4

In the preliminary dataset (n = 220), the relationship between spend and productivity per FTE was null (ρ = −0.058, p = 0.389). With the expanded sample, this has become a weak but statistically significant negative correlation (ρ = −0.156, p < 0.001). This does not mean spending is counterproductive; it reflects scale effects where larger organizations spend more but have more complex, time-intensive research processes. The practical interpretation is unchanged: investment and per-capita efficiency are separate levers.

The partial correlation for PDMI → Inactive % controlling for organizational revenue is now statistically significant (p = 0.040), having been borderline (p = 0.051) at n = 220. While the effect remains weak (ρ = −0.092), the larger sample provides sufficient power to detect it.

New findings in the expanded dataset

The expansion from 220 to 500 respondents produced several new findings not present in the preliminary report:

1. Cronbach’s α improved from 0.733 to 0.761, now exceeding the 0.75 reliability threshold. The PDMI is a more reliable instrument with the larger, more diverse sample.
2. Effort per Qualification now correlates with both PDMI (ρ = +0.143, p = 0.001) and Revenue (ρ = +0.175, p < 0.001). Deeper research may produce higher-quality prospects.
3. Qualification Criteria Count now correlates with PDMI (ρ = +0.173, p < 0.001). More rigorous qualification standards signal more mature operations.
4. Spend → Productivity/FTE became a significant weak negative (ρ = −0.156, p < 0.001), confirming scale effects.
5. PDMI → Inactive % partial correlation is now significant (p = 0.040), crossing from borderline at p = 0.051.
6. The field composition shifted: 54% now report only 1–2 MGOs (up from 40%), and 39% are micro shops with <0.5 FTE and 1–2 MGOs (up from 27%). The sector is smaller-staffed than the preliminary data suggested.
7. Research FTE → PDMI within the $20M–$99.9M revenue band remains null (ρ = 0.060, p = 0.55, n = 99), further confirming that FTE is a proxy for organizational size, not an independent maturity driver.

Limitations

This study relies on self-reported data from a self-selected sample. All measures are based on band-based response categories, which introduces measurement imprecision. The cross-sectional design prevents causal inference. Revenue influenced by research is a subjective estimate and may be subject to social desirability bias. The PDMI is a new index that has not been externally validated. Organizations with more mature research functions may be more likely to participate, potentially inflating maturity scores relative to the broader field. No respondents were excluded from any analysis.

Glossary 

FTE: Full-Time Equivalent. A measure of staff allocation where 1.0 = one person working full time on prospect research.

MGO: Major Gift Officer. A frontline fundraiser responsible for soliciting major gifts.

PDMI: Prospect Development Maturity Index. A 0–100 composite score measuring organizational maturity across five self-assessed practices. Cronbach’s α = 0.761.

IQR: Interquartile Range. The range between the 25th and 75th percentiles, capturing the middle 50% of the distribution.

Spearman’s ρ: A rank-based correlation coefficient ranging from −1 to +1, used for ordinal data. Values closer to ±1 indicate stronger monotonic relationships.

Cronbach’s α: A measure of internal consistency reliability for multi-item scales. Values above 0.70 are generally considered acceptable; above 0.75 is the conventional threshold.

Partial Correlation: A correlation between two variables after statistically removing the influence of a third (confounding) variable.

Qualified Prospect: A prospect who has been formally evaluated and determined to meet the organization’s criteria for major gift capacity and/or affinity.

Revenue Influenced: Self-reported estimate of major gift revenue that was influenced by prospect research activities.

Band Midpoint: The center value of a response category (e.g., “$25k–$74.9k” uses $50,000). Used to approximate continuous values from categorical responses.

About Kindsight

Kindsight builds technology that helps fundraisers make a difference. ​​Founded on over three decades of innovation, and trusted by over 4,500 organizations worldwide, Kindsight is the market leader in advancement and fundraising software, supporting the education, healthcare, and nonprofit sectors to achieve their goals through smarter, more connected fundraising. Natively built upon the Salesforce architecture, Kindsight’s Fundraising Platform is anchored in the strength and flexibility of the Ascend CRM, powered by the trusted insights of iWave data, and unified by seamless workflows and connected data.

Kindsight helps organizations discover the right donors, inspire personal connections at scale, and grow giving year after year. With industry-leading prospect research solutions, award-winning fundraising CRMs, a dynamic constituent portal, and an AI assistant built for modern fundraising, Kindsight’s product suite is truly changing the game for donor fundraising. Connect your story to donors who care about your cause—at any scale, in real time—that’s the power of Kindsight. Learn more at kindsight.io.

About Apra International

Apra International is proud to serve as the practitioner advisor for Kindsight’s 2026 Prospect Development Benchmark Report. As the professional home for data-driven fundraising professionals, Apra advances the field by providing accessible learning and career pathways to upskill and lead in a technology-enabled, ethical, and rapidly evolving philanthropic landscape. We foster an inclusive, connected and collaborative global community while leading the development and dissemination of best practices, insights, and emerging trends. Learn more at aprahome.org.