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Market Matching: Workflow

Workflow at a glance​

  1. Define test markets and objective
  2. Prepare data inputs
  3. Submit Market Matching job
  4. System computes candidate controls
  5. Review match quality outputs
  6. Decide to accept, refine, or rerun

Step 1: Define the matching objective​

Before running:

  • identify the test market(s),
  • define the outcome metric,
  • decide whether controls are auto-selected or explicitly provided.

Helpful question: "Do these controls represent a truly comparable baseline?"

Step 2: Prepare data inputs​

Minimum expectations:

  • clear date field,
  • clear location/market field,
  • clear outcome metric,
  • enough historical coverage for stable pre-period comparison.

Best practice:

  • ensure consistent data grain over time,
  • remove known data anomalies before matching,
  • avoid mixing incompatible market definitions.

Step 3: Submit Market Matching job​

Jobs are submitted asynchronously:

  • request is created,
  • background worker executes matching,
  • artifacts and metrics are published.

Pipeline options typically include:

  • matching method (balanced, causal, quick),
  • max controls,
  • minimum similarity threshold,
  • optional explicit control list.

Input use cases​

Market Matching supports three common input modes:

  1. Test and control both provided
  • You provide test_locations and control_locations.
  • System evaluates the provided pair(s) and returns similarity/balance diagnostics.
  1. Test provided, control not provided
  • You provide test_locations only.
  • System auto-selects and ranks the best controls from the remaining candidate markets.
  1. Neither test nor control provided
  • You provide no explicit markets.
  • System auto-selects candidate test markets and controls using historical similarity on the selected outcome metric (for example sales, revenue, or other KPI).

Step 4: Automated matching sequence​

Behind the scenes:

  1. Field mapping and normalization
  2. Data transformation and panel balancing
  3. Validation checks
  4. Geo fingerprint generation
  5. Control candidate scoring
  6. Balance/quality evaluation
  7. Report and metrics publication

Step 5: Outputs become available​

After completion, review:

  • primary match results,
  • quality assessment,
  • similarity chart,
  • treatment-level drilldown,
  • PDF report and standardized CSV.

Step 6: Decision checkpoint​

Choose one path:

  • Accept matches: quality is sufficient for downstream use
  • Refine setup: adjust test markets, filters, or method
  • Rerun: when quality is weak or controls are not credible

Pre-run checklist​

  • Test markets are finalized
  • Outcome metric is defined and stable
  • Date and location columns are correct
  • Sufficient historical window is available
  • Similarity threshold strategy is decided

Post-run checklist​

  • Ranked controls reviewed for each test market
  • Similarity scores reviewed
  • Balance/quality metrics reviewed
  • Low-quality or unstable matches flagged
  • Go/no-go decision documented

Common workflow pitfalls​

  • Choosing controls only by geography intuition
  • Accepting high similarity without checking balance
  • Ignoring warning comments in quality output
  • Using overly short history windows
  • Treating one run as final without sensitivity reruns