Explaining Benchmarking to LPs: Executive English for Quartile Rankings and Methodology (quartile rankings explanation language)

Step 1 – Establish the benchmarking frame

Benchmarking in private markets is the practice of placing a manager’s performance in a structured, comparable context so that Limited Partners (LPs) can judge results against an appropriate peer group. Because private market returns are reported as cash-based multiples and interim valuations, LPs rely on benchmarking to interpret what a given fund’s numbers mean relative to funds of the same type and vintage. Benchmarking is not just a ranking exercise; it is a methodology for turning standalone figures into decision-grade signals about manager quality, pacing, and risk.

At the center of this frame are four core metrics. First, Distributions to Paid-In (DPI) measures realized proceeds relative to capital called. It focuses on cash actually returned and is particularly important later in a fund’s life. Second, Total Value to Paid-In (TVPI) combines both realized (distributions) and unrealized value (net asset value) over capital called. TVPI is the headline multiple that captures the full picture of value created so far, acknowledging that part of the value is still on paper. Third, Residual Value to Paid-In (RVPI) isolates the unrealized component: the value of remaining assets relative to capital called. Early in a fund’s life, RVPI drives TVPI and can be volatile as valuations move with exits and write-downs. Finally, Public Market Equivalent (PME) and its variants (such as APME or Kaplan–Schoar PME) compare the fund’s cash flows with a public index to assess outperformance against a liquid benchmark. PME-based comparisons help LPs understand whether illiquidity and complexity have been rewarded beyond what public markets would have provided.

Benchmarking ties these metrics to a well-chosen peer set, most commonly by vintage year and strategy. Vintage year is critical because it aligns funds that invested under similar market conditions and faced comparable opportunity sets. Grouping by vintage helps avoid cross-cycle distortions—comparing a 2007 buyout fund to a 2015 buyout fund can be misleading given differences in entry valuations, credit availability, and exit environments. In addition, strategy and region segmentation ensures that like is compared with like: buyout with buyout, venture with venture, Europe with Europe, and so forth. A carefully constructed peer set is the foundation that makes a manager’s DPI, TVPI, RVPI, and PME/APME intelligible rather than isolated numbers.

LPs also examine the stage-of-life of the fund when reading these metrics. For early vintages, KPI emphasis leans toward RVPI and pacing, with recognition that DPI is necessarily low. Mid-life funds are judged by the evolution of TVPI and the realism of unrealized marks. Later-stage funds are weighed more heavily on DPI and persistence of TVPI during exit activity. Across all stages, PME provides a reference to public market alternatives, offering a reality check for absolute multiples.

Step 2 – Demystify quartiles

Quartiles translate peer-set distributions into accessible categories. Conceptually, quartiles are cut points that divide a ranked list of funds into four equal parts by performance. The first quartile (Q1) represents top performers—the highest 25% in the chosen metric. The second quartile (Q2) is above the median but below Q1. The third quartile (Q3) lies below the median and above the bottom 25%. The fourth quartile (Q4) is the lowest-performing segment. In practice, the interpretation depends on the metric: for multiples like TVPI and DPI, higher values are better. For some risk or loss metrics, lower may be better. Clear communication specifies the direction of desirability.

Quartile mechanics begin with defining the peer set and the metric to be ranked. Funds are arranged from highest to lowest on, for example, TVPI within a given vintage and strategy. Percentile cutoffs—25th, 50th (median), and 75th percentiles—are calculated, and each fund’s position relative to those cutoffs determines its quartile. Because private markets have unequal dispersion across strategies and cycles, the distance between quartiles can vary materially. In a high-dispersion vintage, the TVPI difference between the 75th and 50th percentile might be large, making movement between Q2 and Q1 meaningful. In a compressed vintage, small changes could shift funds across quartile boundaries, so one should report results with appropriate humility.

Peer-set formation directly affects quartile outcomes. Including or excluding small funds, sector specialists, or geographic variants can shift percentiles. Similarly, rolling vintages (e.g., grouping 2012–2014 as a single cohort) change the composition and can dilute cycle-specific effects. LPs should look for consistency: quartiles should be stated relative to an explicit and stable definition of the peer set. If a manager changes definitions across updates, LPs may interpret that as cherry-picking.

Outliers and dispersion deserve explicit attention. A few funds with extremely high TVPI can stretch the distribution’s upper tail, raising the 75th percentile and making first-quartile status rarer. Conversely, pronounced underperformers can depress the lower tail. LPs interpret quartiles alongside percentile ranks and absolute metrics. A fund might be first quartile by a narrow margin in a tight distribution, or second quartile but close to the 75th percentile in a wide distribution. Clarity about dispersion helps LPs calibrate how “strong” a quartile is in economic terms.

Finally, quartiles are a communication device, not a substitute for valuation quality or durability of cash flows. LPs view quartile ranks in the context of track record persistence, interim NAV practices, and exit pathways. A first-quartile TVPI with low DPI and high RVPI late in the fund life invites questions about realizations and mark realism. Conversely, a second-quartile TVPI with high DPI and stable PME outperformance may read as more robust.

Step 3 – Methodology matters

Methodology determines credibility. LPs expect transparency on data sources, inclusion rules, weighting choices, and refresh cadence. Common data sources include Burgiss, Cambridge Associates, and Preqin. Each source has strengths and coverage nuances. Burgiss is known for cash-flow level data sourced from GPs and LPs, enabling robust PME calculations. Cambridge Associates offers long-standing benchmarks with curated peer sets and advisory insights. Preqin provides broad coverage and flexible cuts by region and strategy. Because coverage and timeliness differ, the same fund can rank differently across sources. Disclosing which database and the exact cut used (e.g., Burgiss Buyout—North America—Vintage 2012) is essential.

Inclusion and exclusion rules must be precise. Key questions include:

• Gross vs. net: Benchmarks typically use net-of-fee, net-of-carry returns, and managers should report on a net basis for comparability. Mixing gross and net figures introduces upward bias.
• Unrealized marks policy: State whether NAVs are reported under fair value standards (e.g., ASC 820/IFRS 13), and whether write-ups/write-downs are recognized promptly. NAV conservatism or aggressiveness can affect interim TVPI and RVPI.
• Currency and FX treatment: Confirm that all returns are in the same currency and whether FX impacts are hedged or unhedged. Cross-currency comparisons can distort multiples and PME if not normalized.
• Fund type filters: Ensure like-for-like comparisons by strategy (e.g., core buyout vs. growth equity vs. venture) and region. Avoid mixing sector-focused and generalist funds unless the objective is explicitly sectoral.

Weighting choices also shape the benchmark. Equal-weighting treats each fund equally, which is useful for assessing manager skill independent of size. Capital-weighting (also called value-weighting) reflects the experience of a dollar invested, which can better approximate an LP’s aggregate exposure. Because large funds can dominate capital-weighted results, disclose the choice and rationale. For statistical stability, some LPs examine both cuts: equal-weighted medians to understand manager dispersion, and capital-weighted results to understand portfolio-level impact.

Refresh cadence refers to how frequently the benchmark and the fund’s figures are updated. Private market data are typically refreshed quarterly, with lags ranging from 45 to 120 days. Early in the year, there can be a heavy synchronization effect as Q4 audits finalize. A credible disclosure states the as-of date for both the fund’s data and the benchmark, and specifies whether subsequent events (e.g., signed exits not yet closed) are reflected. LPs are wary of “as-of mismatches,” where a fund’s latest NAV is compared against a prior-quarter benchmark, or where selective events are included for the fund but not in the peer set.

Common pitfalls include small sample sizes (small N), selection bias, and unrealized volatility. Small N can produce unstable quartiles; a peer set of 10 funds makes each observation move the percentiles significantly. Selection bias arises if only top managers report to a database or if underperforming funds delay reporting; this can lift median multiples. Unrealized volatility is particularly relevant in late-stage venture and growth equity, where NAVs can swing with public comparables and financing rounds. LPs expect these caveats to be acknowledged when presenting quartiles.

PME methodology also deserves precision. Different PME variants handle timing and scaling differently. For example, Kaplan–Schoar PME (KS-PME) expresses value relative to the public index as a ratio, while Direct Alpha or APME derive an annualized excess return. Disclose which variant is used, the index selected (e.g., MSCI World, S&P 500), whether dividends are reinvested, and the currency basis. Misalignment here can lead to incorrect inferences about public market outperformance.

Step 4 – Executive English in action

LPs value concise, caveat-aware phrasing that is non-promotional and specific. The goal is to convey quartile positioning, scope, and limitations in one or two sentences. Below are sentence frames you can adapt:

• Peer set and metric definition: “Relative to [Data Source]’s [Strategy–Region] [Vintage Year] cohort, our Fund [Identifier] ranks in the [Quartile] on a net TVPI basis as of [Quarter/Year], with an as-of date consistent with the benchmark.”
• Direction and dispersion: “The first–second quartile threshold for this cohort is [Value], with interquartile dispersion of [Range], indicating that small changes in NAV could shift ranks.”
• DPI vs. TVPI balance: “While TVPI is [Value] in the [Quartile], DPI is [Value], reflecting the fund’s mid-life profile; unrealized value remains a material driver of current performance.”
• PME clarity: “On a [PME Variant] basis versus [Index], the fund shows [Result], calculated from full cash flows in [Currency] with dividends reinvested; index selection reflects the portfolio’s sector and region mix.”
• Weighting disclosure: “Quartile positioning is assessed against an equal-weighted benchmark; capital-weighted results are directionally similar but influenced by a small number of large funds.”
• Inclusion/exclusion rules: “All figures are net of fees and carry; benchmark constituents are net returns reported under fair value standards, excluding fund-of-funds and sector specialists.”
• Refresh cadence and as-of dates: “Both fund and benchmark data are current through [Quarter/Year]; subsequent events after the as-of date are not reflected in the quartile assessment.”
• Caveats about small N and bias: “The cohort includes [Count] funds; with a small sample size, percentile cutoffs can be sensitive to new observations and reporting lags.”
• Outliers and robustness: “This vintage exhibits elevated dispersion due to a few outsized outcomes; we therefore present percentile rank alongside quartile to show proximity to cutoffs.”
• Cross-vintage context: “For reference, the fund’s rank is [Quartile] in the single-year vintage cohort and [Quartile] in the rolling [Years] cohort; we prioritize single-year vintage for cycle alignment.”
• Like-for-like alignment: “Comparisons are limited to [Strategy/Region] to ensure like-for-like; we do not compare to broader private markets or public equity without noting methodology differences.”
• Realization emphasis: “Given our current DPI of [Value], we focus on realized performance when discussing downside protection; NAV risk remains in [Sectors], which could affect RVPI.”
• Currency and FX: “All returns are presented in [Currency]; benchmark and PME are matched to the same currency to avoid FX-driven distortions.”
• Consistency statement: “We maintain a consistent benchmark definition across reporting periods; any changes to data source or cohort are disclosed and back-reconciled.”

When delivering these messages, favor plain, precise verbs and avoid superlatives. Replace “best-in-class” with “first quartile relative to [cohort].” Replace “dramatically outperformed” with “above the 75th percentile on net TVPI and positive [PME Variant] versus [Index].” Where estimates or interim marks are material, use conditional language: “Subject to final audit,” “Based on preliminary Q2 NAVs,” or “Excluding post-period closings.” This signals discipline and reduces the risk of over-claiming.

Finally, align the metric to the decision context. For liquidity planning, emphasize DPI and net distributions. For relative-return assessment, lead with TVPI and PME. For risk and durability, discuss RVPI composition, concentration, and sensitivity to public comparables. Each statement should be anchored to the same peer set and as-of date, with an explicit acknowledgment of dispersion and sample size. This combination of methodological clarity and concise language meets LP expectations for rigor, comparability, and transparency in quartile benchmarking.