RJNSEvidence you can
inspect and compare.
RJNS Research is where we will publish aggregated Instagram performance findings with the methodology, sample and metric definitions needed to understand what the numbers actually mean.
A benchmark is only useful when you know what it represents.
Before publishing a benchmark, RJNS will document the sample size, observation period, account-selection criteria, metric definitions, treatment of missing values and any segmentation used in the analysis.
- Aggregate findings rather than exposing private user information.
- Separate small exploratory samples from broader benchmark claims.
- State whether observations come from public Instagram data, user-submitted analyses or a dedicated research collection.
- Separate observed results from interpretation and recommendations.
- Show the limitations that could change how a finding should be used.
Performance questions we plan to study.
The first studies will stay close to the metrics RJNS already analyzes so the methodology remains explainable and relevant to real competitor research.
Distribution-based engagement observations segmented by a clearly defined account or content cohort.
How often posts exceed 2×, 5× or 10× a comparable baseline under a documented method.
Format-specific observations that avoid mixing unlike content types where their distributions differ.
Segmented findings designed to reduce distortion when comparing very different audience scales.
Check the finding, then check the sample.
Start with the primary result, then verify whether the sample matches the kind of account or content you are studying.
Look at sample size, period, cohort and exclusions before generalizing the result.
Confirm the numerator, denominator and missing-data treatment before comparing the number with another source.
Use the finding as context, not as a universal rule for every Instagram account.
We are not publishing benchmark numbers prematurely.
RJNS already analyzes individual public-account performance, but aggregate benchmark claims require a larger and clearly defined research dataset. Until that threshold is met, this hub documents the research areas and methodology standards rather than presenting unsupported industry averages.
Start with observable account performance.
Use the live analyzer for individual public-account research while the aggregate benchmark layer is being built.