Experiment Collision Map: exposure-overlap auditing for experimentation platforms
A map for experimenters that shows which users are in multiple tests at once to prevent skewed results.
A view for feature-flag and experimentation platforms that shows how users overlap across many concurrently running experiments as one proportional Venn diagram, flagging contaminated exposure cells before results are read.
Concept
An "experiment collision map" for experimentation platforms: a view that shows, for all experiments currently running, how many users belong to each combination of experiments, rendered as a quasi-proportional Venn diagram. Product teams use it to catch contamination — users simultaneously enrolled in five or more experiments whose metric movements cannot be cleanly attributed — and to set up mutual-exclusion groups or holdouts accordingly. Most users sit in only a few experiments, so most intersections are empty and are pruned from the diagram, which is exactly the sparse regime where many-set Venns stay legible. Regions clamped at the minimum drawable radius carry their true user counts so small cells are not misread.
Why now
nVenn2 [0] shows that quasi-proportional Venn diagrams with more than nine sets are now interpretable and that the algorithm is consistently faster than its predecessor for six or more sets, with runtime driven by the number of non-empty regions — matching the sparse exposure structure of concurrent experiments, where most users are in few tests. It is available as a deployed Python package (nVennPy), so platform integration is feasible today rather than speculative. Enterprise product organizations commonly run dozens of concurrent experiments, yet current tooling offers only pairwise overlap checks or raw SQL, leaving interaction effects invisible until metrics look wrong.
Written by GLM · 11 Oct 2026
AI assessment
A highly focused visualization tool that solves a specific pain point in experimentation platforms using a newly feasible algorithm for high-dimensional set overlap.
- Evidence strength 5/5
- The idea directly maps the nVenn2 algorithm's specific strengths—handling >9 sets and scaling with non-empty regions—to the sparse nature of experiment exposure.
- Market pull 4/5
- Enterprise experimentation platforms (Statsig, Eppo) have a clear need to manage interaction effects and contamination, which currently requires manual SQL or limited pairwise checks.
- Novelty & moat 3/5
- While the 'Campaign Overlap Map' uses the same visualization mechanism, applying it to experiment contamination in a feature-flagging context is a distinct and valuable use case.
- Feasibility 5/5
- The existence of a deployed Python package (nVennPy) makes the core engine a plug-and-play integration for a small engineering team.
- Wedge clarity 5/5
- The wedge is a single, sharp feature: a 'Collision Map' view to identify contaminated exposure cells before reading results.
- Simplicity / focus 5/5
- The proposal avoids 'platform' creep, focusing exclusively on one visualization tool for one specific problem.
Scored by Gemma 4 · 11 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.
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Who benefits
- Statsigcompany
Exposure and holdout management is core to its experimentation platform; a collision map is a natural premium feature for its enterprise customers running many concurrent tests.
- LaunchDarklycompany
Its customers run many concurrent flags and experiments; a visual overlap audit improves randomization hygiene and reduces support escalations about surprising metrics.
- Eppocompany
As an experimentation platform for data teams, interaction effects from concurrent tests are a recurring customer question this view answers directly.
Research it builds on
- nVenn2: faster, simpler generalized quasi-proportional Venn diagramsSamuel Pis-Vigil, María González-Pereira, Magda R. Hamczyk et al. · 2026 · 1 citationsAll ideas from this paper →