Consumer Research Bench (CRB) — Evaluating AI Agents for Consumer Research

Abstract

Consumer research combines evidence collection, behavioral interpretation, qualitative synthesis, quantitative classification, and business recommendation. Existing AI benchmarks evaluate web research agents, long-form research agents, browsing agents, coding agents, and general-purpose assistants, but do not directly evaluate the capabilities specific to consumer research.

Consumer Research Bench evaluates whether AI systems can transform fragmented consumer-signal data into evidence-backed consumer understanding with the interpretive depth associated with primary research and the speed of digital signal analysis.

CRB v0.1 focuses on the Consumer domain and evaluates two tracks: an Objective Track for atomic research tasks and a Report Track for open-ended consumer research synthesis. The benchmark introduces RetroSignals, a frozen consumer-signal environment, and uses a data × model ablation to separate model quality from consumer-data advantage.

Key Contributions

C1 Consumer Research Construct

Decision-ready consumer-research quality per unit time, at a fixed evidence-grounding bar.

C2 Two Evaluation Tracks

Objective atomic tasks plus open-ended report evaluation.

C3 RetroSignals

A frozen consumer-signal environment for reproducible evaluation.

C4 C-RACE

Reference-based, criteria-adaptive report scoring validated against human experts.

C5 Data × Model Ablation

Separates the contribution of consumer-signal data from model capability.

C6 Validity Gates

Hard failure rules for fabricated quotes, unsupported statistics, and untraceable evidence.

Benchmark Structure

A research brief flows through evaluation to a scored, diagnostic output.

Input

Research brief
Business context
Methodology
RetroSignals environment

Evaluation

Objective Track
Report Track
Evidence grounding
Human validation
Data × model ablation

Output

Scores
Diagnostics
Validity gates
Leaderboard
Benchmark report

Evaluation Tracks

Track A

Objective Track

Scores atomic research capabilities against expert-worked ground truth.

Example tasks

Find SignalDerive MetricValidate Claim Define CohortPopulate SetCompile Dataset

Metrics

Binary accuracyRecall / F1 Probability differenceExpert-judged match

Track B

Report Track

Scores open-ended consumer research outputs using C-RACE.

Scoring dimensions

D1 Comprehensiveness
D2 Consumer Insight Depth
D3 Methodology Fidelity
D4 Evidence & Traceability
D5 Quant–Qual Integration
D6 Contextual Fidelity
D7 Decision Readiness
D8 Readability & Structure

RetroSignals

RetroSignals is a frozen, task-linked consumer-signal environment designed to make consumer research agent evaluation reproducible over time.

Social postsComments & repliesReviews Marketplace contentCommunity discussionsSearch-result pages Creator contentVideo / audio transcriptsBrand & competitor context docs

Data Breadth

Coverage across signal types and sources.

Data Depth

Granularity and richness within each source.

Data Condition

Noise, recency, and integrity of the signal.

Relevant Signal Density

Share of signal that bears on the brief.

Validity Gates

Some failures do not merely reduce score. They invalidate a run or cap its maximum score.

Invalidates the run

Fabricated consumer quotes
Fabricated statistics
Non-existent cited evidence
No source-level traceability for central claims

Caps the maximum score

Confusing brand-owned content with consumer signal
Treating news or investor commentary as consumer behavior
Overgeneralizing from one viral post
Ignoring contradictory evidence
Producing generic insights
Recommending actions without evidence
Failing to follow the intended methodology

Example Task

Report Track · Sample Brief

Why are young urban consumers choosing matcha over coffee?

Required output

01 Executive summary
02 Key motivations
03 Consumption occasions
04 Consumer cohorts
05 Barriers & tensions
06 Evidence table
07 Opportunity spaces
08 Brand implications
09 Confidence assessment

Strong answer characteristics

A strong answer goes beyond “matcha is healthy and trendy.” It identifies specific patterns — each grounded in inspectable signal:

Clean energyMid-afternoon productivityAesthetic self-expression Coffee replacementWellness signalingAt-home ritualization PremiumizationCreator influenceTaste / price / authenticity barriers

Leaderboard

Social listening. Consuma compared with social-listening tools across four consumer-data dimensions — data universe, signal-to-noise, AI insights, and measurement & enrichments — from real queries run through each tool.

Citation

Independent CRB Working Group. “Consumer Research Bench: Evaluating AI Agents for Consumer Research.” Working Draft v0.6, 2026.

@misc{crb2026,
  title={Consumer Research Bench: Evaluating AI Agents for Consumer Research},
  author={Independent CRB Working Group},
  year={2026},
  note={Working Draft v0.6}
}

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Version History

v0.6

Current · Working DraftConstruct, two tracks, RetroSignals, C-RACE, and validity gates documented.

v0.5

Methodology DraftReport Track dimensions and C-RACE scoring drafted.

v0.4

Methodology DraftRetroSignals environment scope defined.

v0.3

Methodology DraftObjective Track task taxonomy outlined.

Participate

CRB is open to anyone — any consumer-research system, agentic, workflow-based, or human baseline.

Open, PR-based submissions. Fork the repo, run the benchmark, and open a pull request with your results and the artifacts that reproduce them — a maintainer reviews it, independently re-runs or re-scores it against the rubric and validity gates, then merges it. Entries we re-verify are tagged Verified; self-reported ones are listed as Community. Every entry discloses its models, vendor, and data sources, and the private held-out set is scored by a neutral custodian — so first-party entries can't tune to labels they can't see.

Status. The spec, rubrics, and task schema are public; the sample tasks and submission harness are being released. You can start now — read the submission guide, review the rubrics, or propose a task via PR.

Read the submission guide Open a submission PR Watch the repository

Governance

CRB is intended to be governed independently of any vendor or data provider.

Independent task authorship
Blind grading
Pre-registered criteria
Private held-out test set
Public sample tasks
Human validation
Vendor and data-provider disclosure
Verified & self-reported result tiers
Independent re-scoring of submissions
Rolling benchmark refresh