Synthetic Data
Engine

From published aggregated data to reliable, privacy-compliant synthetic microdata for microsimulations and forecasts. Synthpop generates synthetic representative population and household structures from aggregated statistics and incomplete information, enabling early modeling, detailed forecasts, and privacy-compliant analyses. Synthpop uses supervised learning algorithms to model relationships between different demographic variables.

Synthetic Data Engine

How does it work?

Set the lens

Add your data

ZIP Code level synthetic data

Set the lens

1/4

1 Frame the decision

Define the business objective and geographic scope.

2 Prepare & connect data

Assemble all relevant aggregates and metadata. With the help of Synthpop, clean, align, and anonymize sources so that every variable maps to a common schema.

3 Generate synthetic microdata

Synthpop produces realistic, privacy-safe person/household records that match published totals and patterns.

4 Model & activate

Train fit-for-purpose models on the synthetic population.

User Stories

Can we size demand at the ZIP-code level with confidence?

A Go-to-Market strategist at a retail chain needs to identify where to expand next — but privacy restrictions and limited data availability make it impossible to access reliable, neighborhood-level insights. Instead of guessing based on broad regional trends, the strategist’s team uses a synthetic, privacy-preserving population generator that recreates realistic microdata from public aggregates. By applying Small Area Estimation (SAE), they can model demand at the ZIP-code level, simulate new store scenarios, and quantify potential revenue before committing capital. This transforms market entry from assumption-driven to evidence-based, leading to smarter location choices, better-targeted marketing, and higher ROI across retail expansions.

User

Go-to-Market strategist at a major retail chain, responsible for guiding expansion plans and market entry strategies.

Solution

The team uses a synthetic, privacy-preserving version of national survey microdata combined with Small Area Estimation (SAE) techniques. This enables statistically valid, hyper-local market size estimates while remaining compliant with privacy rules. Analysts can model neighborhood demand with precision and simulate different entry strategies. As a result, decision-making shifts from guesswork to data-driven simulation. The company gains accurate, defensible market sizing at the neighborhood level, reducing uncertainty in multi-million-dollar investments and achieving smarter store placements, optimized distribution, higher marketing efficiency, and maximized ROI.

Problem

Accurate neighborhood-level insights are critical for expansion, but a “data wall” blocks access to reliable microdata such as census tract or ZIP code details. Privacy laws prohibit release of granular survey data, leaving strategists reliant on broad assumptions and crude bottom-up estimates. This leads to poor capital allocation, misjudged store locations, and inefficient sales territory design all under high uncertainty.

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Synthetic Data
Engine