Stabilize the tracker cuts leadership wants, without re-fielding.
Trackers are expensive, recurring, and operationally important, but the most desired reads often sit in small cells: niche markets, switchers, lapsed buyers, and regional cuts. Booster sample is slow and inconsistent. Simulacra expands and stabilizes underpowered trackers, models future interventions, and predicts the cuts you can't read from the fieldwork.
Sample is fine. The cuts are not.
Thin priority cohorts
Gen Z switchers, lapsed loyalists, regional sub-segments — the cells leadership cares about most are the most likely to be underpowered.
Volatile wave-over-wave
Small-cell reads bounce: trends look real one wave and disappear the next, and stakeholders lose confidence in the tracker.
Booster sample tax
Fixing each thin cell costs five figures and weeks per wave. Quality of supplemental sample is rarely identical to the original field.
Learn across waves to boost low-incidence or under-sampled populations.
Backtest first
Hold out a portion of a prior wave, fit on the remainder, predict the holdout, and compare. The methodology appendix ships with the validation numbers your stakeholders will see.
Boost the cohort
Expand the priority cell inside the measured tracker schema. The generated rows predict the way your respondent population behaves, preserving segment-level relationships.
Stabilize across waves
Simulacra learns the response structure across waves. Conditioning on the current wave dampens noise on thin cells without flattening wave-level signal.
Choose the cut. Set the boost. Preview the read.
Start with a completed tracker wave, name the cell the team needs, set the boost, and preview the stabilized read inside the same measured schema.
Priority cell
The run stays inside the measured tracker variables. Unsupported cuts are flagged rather than filled in.
Boost Gen Z switchers from 38 fielded rows to a 190-row stabilized read for cuts the original wave could not support.
- Readout
- Awareness, consideration, preference, usage, switching barriers.
- Export
- CSV, SPSS-ready tables, synthetic-row flags, methodology appendix.
- Guardrail
- Synthetic rows flagged; assumptions and unsupported cuts logged.
Workflow preview. Your run uses your tracker schema, waves, priority cells, and reporting cuts.
Cross-tabs your stakeholders can defend.
Stabilized cuts with uncertainty bands
Brand health, awareness, consideration, preference, usage by region, demographic, behavioral, and loyalty cuts — with explicit uncertainty.
Methodology appendix
We'll run a holdout backtest with readouts on performance by subgroup, known limits, and recommended use. Simulacra is stakeholder-ready.
Synthetic-row provenance
Synthetic rows are flagged in every export. Scenario assumptions logged. Audit trail per generation.
Standard exports
CSV exports, SPSS-ready tables, synthetic-row flags, and methodology appendix. Built to drop into your tracker reporting workflow.
The validations behind this use case.
Response structure preserved across 717 vars
0.005 categorical MAE. 64% below sampling noise. External discriminator within 5 pp of real-vs-real. Validating that per-cell reads in your tracker schema are at least as accurate as the field itself.
Economic benchmark80% reduction in data needed for reconstruction
Reconstructed a 2,782-person sensory study from 500 obs. Bayesian changepoint corroborated. Validating that thin tracker waves can be stabilized without doubling the sample.
Bring the last 4–6 waves. We'll stabilize the cuts leadership wants.
Bring your historical tracker data. Simulacra fits the response structure across waves and generates stabilized small-cell reads for the cohorts you specify. Standard NDA, no contract required.