Synthetic User Research Benchmarking Framework

This project focused on building a rigorous, repeatable framework to evaluate whether AI-generated synthetic users can produce insights comparable to real human research. The challenge was to move beyond ad hoc testing and define a statistically grounded system that could support responsible adoption of synthetic research.

Context and brief

Synthetic research tools were emerging rapidly, but there was no consistent or credible way to evaluate whether their outputs could be trusted. Teams risked either over-relying on unvalidated outputs or ignoring potentially valuable tools altogether.

Objective

Design a benchmarking framework that could systematically compare synthetic and human research outputs, define clear thresholds for alignment, and establish guardrails for safe usage.

Role and responsibilities

I designed and documented the full benchmarking process and statistical framework, ensuring it was both methodologically robust and operationally usable.

This included defining the end-to-end workflow, from shared research design and preregistration of evaluation criteria, through to replicated study design and structured comparison across human and synthetic outputs.

I also defined how results should be interpreted, ensuring that synthetic outputs were evaluated relative to natural human variability rather than a single benchmark.

Approach

The framework was built around replicated studies, running multiple human and synthetic datasets to establish a baseline of human variability.

Evaluation was structured across multiple levels:

Distributional similarity of responses
Alignment of signals such as rankings and concept performance
Overlap in qualitative insights and themes

I defined a minimal but robust statistical battery including effect size comparisons, distribution tests, rank correlations, and similarity measures, combined with a clear RAG-based interpretation system.

Importantly, the framework emphasized practical interpretation over statistical significance alone, focusing on whether outputs would lead to the same business conclusions.

Output

A complete benchmarking framework including process, statistical methods, interpretation rules, and reusable evaluation assets.

Impact

This enabled teams to test and adopt synthetic research in a controlled and credible way, reducing risk while unlocking efficiency gains. It also established clear boundaries for when synthetic outputs could and could not replace human research.