Market Matching: Methodology
How Market Matching works technically
Market Matching finds the best control markets for a given test market by comparing pre-period behavioral similarity on an outcome metric (sales, revenue, units, etc.).
The process has six stages:
- Map and transform data
- Validate and balance the panel
- Generate geo fingerprints
- Score and rank control candidates
- Evaluate balance and quality
- Publish results and report
Stage 1: Data mapping and transformation
Column mapping
Auto-detects:
- date
- location (market/geo)
- outcome (KPI used for similarity — sales, revenue, etc.)
User overrides are supported for outcome, location, and date columns.
Panel balancing
- Standardizes column names
- Aggregates duplicates
- Handles missing data
- Drops geos with excessive missingness
Stage 2: Validation
Checks ensure the dataset is ready for matching:
- minimum geos and time periods,
- missingness thresholds,
- structural completeness.
Validation issues are surfaced in the quality output.
Stage 3: Geo fingerprinting
Each market gets a behavioral fingerprint from its outcome time series:
| Feature | What it captures |
|---|---|
| mean_outcome | Average level |
| std_outcome | Volatility |
| trend | Direction of movement (robust slope) |
| seasonal_strength | How seasonal the series is |
| spikiness | Outlier frequency |
| autocorr | Week-to-week persistence |
| residual_variance | Noise after detrending |
| outcome_range | Min–max spread |
| outcome_cv | Relative variability |
These features power similarity scoring between test and control markets.
Stage 4: Control candidate scoring
Input use cases
| Mode | What you provide | What the system does |
|---|---|---|
| Test + control | Both markets specified | Evaluates the pair and returns similarity/balance |
| Test only | Test market(s) only | Auto-selects and ranks best controls |
| Neither | No markets specified | Auto-selects test markets and controls by outcome similarity |
Ranking
- Candidate controls are scored by similarity to each test market
- Results are filtered by minimum similarity threshold
- Top controls are retained (subject to max control cap)
- An autonomous quality gate can filter poor-quality pairs
Stage 5: Balance and quality evaluation
Balance (SMD)
Standardized Mean Difference (SMD) measures how comparable test and control markets are on key features. Lower absolute SMD values indicate better balance.
A pair is considered balanced when SMD stays within the configured threshold across features.
Quality score
Combines:
- similarity strength,
- balance quality,
- and validation issue penalties.
Produces an overall score (0–1) and per-match diagnostics.
Stage 6: Output publication
Results include:
- primary matching table (test → ranked controls),
- quality assessment,
- similarity chart data,
- treatment-level drilldown,
- PDF report and standardized CSV.
Data requirements
| Requirement | Practical guidance |
|---|---|
| History | Enough weeks for stable fingerprinting |
| Geos | Multiple candidate markets for meaningful matching |
| Outcome | Single KPI for similarity (sales, revenue, units, etc.) |
| Frequency | Weekly time series is typical |
Matching method options
Three algorithms are available. See market-matching-models for details:
| Method | Speed | Depth |
|---|---|---|
| balanced | Standard | Full fingerprint set, euclidean similarity |
| causal | Standard | Propensity-score-based matching |
| quick | Fast | Core features only (mean, std, trend) |
What can go wrong
- No similar markets found for the test geo
- High similarity but poor balance (SMD fails)
- Test market dropped during data cleaning
- Quality gate removes all candidates (fallback may salvage top pair)
- Outcome metric changed mid-study
For interpretation guidance, see market-matching-results.