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HuanYu Global Insight

Remove the noise.
Find the signal.

The B2B market research industry has no shortage of confident assertions.
What it lacks is an independent reference point — a benchmark not written
by the vendors being measured.

Explore Benchmarks → Deep Analysis

Why HYMBS Exists

Architecture, benchmarks, standards — these are the path. The destination is letting those trapped by bad data see where they actually stand.

In the current B2B market research ecosystem, vendors define what "quality" means, sell against their own definition, and report on their own performance using their own metrics. This loop structurally forecloses the possibility of comparison.

HYMBS exists outside that loop. Our work is to construct an independent reference framework — starting with verifiable phenomena, running them through falsification protocols, and producing benchmarks that hold up when anyone applies them, including a competitor.

// Core Proposition

In a market without independent benchmarks, "industry best practice" is effectively the practice of whoever is most profitable. When every party measures its own performance against its own standard, quality becomes a marketing label rather than a verifiable claim. HYMBS publishes the measurements no one else is taking.

The HYMBS Framework

01 · Truth

Cutting through to structural facts

Industry truth is not the same as industry consensus. Most widely cited market research conclusions have never been independently verified at the methodology level. The first step is identifying the cognitive bias embedded in how findings are framed — particularly when the framing originates with a commercially interested party.

Truth isn't always comfortable. It sometimes means that a high-ticket project's ROI cannot be demonstrated, or that a widely adopted metric is measuring the wrong variable entirely.

T
02 · Benchmarks

Without a benchmark, judgment is intuition

The evaluation of B2B data quality currently lacks externally verified reference points. What passes for "industry benchmarks" is almost entirely self-reported by panel vendors — which eliminates the possibility of cross-provider comparison on a common scale.

HYMBS benchmark work covers three layers: data integrity (verifiable source and response quality), panel composition (genuine professional attributes), and methodological validity (from instrument design through analysis logic). We do not accept vendor self-report as benchmark input.

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03 · Standards

Standards are benchmarks made consistent

A standard is not a declaration — it's the systematic expression of benchmarks that have been repeatedly verified. A genuine standard has one required property: if a competitor applies it, the conclusions should converge.

In market research, that condition is currently not met. Different organizations define "completion rate" and "fraud rate" in fundamentally incompatible ways, with no structural incentive for harmonization. HYMBS standards work starts from quantifiable benchmarks and derives operationally comparable definitions.

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Cognitive Method

Phenomenon → Falsification → Benchmark → Truth

This isn't a rhetorical structure — it's the basic logic path of knowledge verification. In a market research context, each step is progressively harder to execute and carries a higher risk of being short-circuited by commercial interest.

Phenomenon Identification

Name the widely accepted but unverified assumptions: high completion rate equals data quality, large sample guarantees representativeness. The credibility of these claims rests on repetition, not empirical verification.

Falsification Testing

Identify the falsifiability conditions for each assumption, then test them when data is available. Most industry consensus shows significant fragility on first rigorous examination — this is methodology, not criticism.

Benchmark Quantification

After falsification, establish independently measurable benchmark parameters. Benchmarks must be operational definitions — not "high quality" but "under a three-stage validation protocol, pass rate falls within X%–Y%."

Latest Insights

Recent Research & Analysis

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B2B benchmark distortion analysis
Benchmark Research
The B2B Panel Benchmark Illusion: Can Vendor Self-Report Function as Industry Reference?
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April 2026
Completion rate paradox research
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The Completion Rate Paradox: Why Higher Rates Sometimes Indicate Worse Data Quality
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March 2026
AI synthetic respondent detection
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The AI Synthetic Respondent Detection Gap: Coverage Limits of Current Screening Protocols
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Engage With the Work

Research questions, benchmark disputes, or methodology challenges?

HYMBS accepts benchmark challenges and verification requests from research institutions, enterprise buyers, and independent researchers. We do not endorse vendors, and we do not publish conclusions that have not been independently verified.

Submit a Research Request View Current Benchmarks →