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GeoLift: Workflow

Workflow at a glance​

  1. Define intervention and markets
  2. Prepare and validate data
  3. Submit GeoLift run
  4. System performs causal analysis
  5. Review quality and impact outputs
  6. Make a decision

Step 1: Define the question​

Before running GeoLift, align on:

  • intervention type (campaign, promo, pricing, etc.),
  • treatment markets,
  • treatment start date,
  • primary KPI (units, revenue, conversions, etc.).

Good prompt for internal alignment: "What decision are we making with this result?"

Step 2: Prepare data inputs​

Minimum practical expectations:

  • clear date column,
  • clear geo/location column,
  • clear outcome metric,
  • enough pre- and post-intervention history.

Best practice:

  • keep metric definition stable through the study,
  • avoid mixing structural breaks (major assortment/logging changes),
  • ensure controls are not exposed to treatment spillover.

Step 3: Submit GeoLift job​

GeoLift jobs are submitted and processed asynchronously:

  • request is created,
  • background worker executes analysis,
  • run artifacts are generated.

In implementation terms, the platform routes this through causal inference job infrastructure (jobs.causal_inference).

Input use cases​

GeoLift commonly runs in two input modes:

  1. Test and control both provided
  • You provide test_locations and control_locations.
  • System runs causal estimation on the specified pair(s).
  1. Test provided, control not provided
  • You provide test_locations only.
  • System derives/ranks controls from remaining candidate markets before running inference.

Step 4: Automated analysis sequence​

Behind the scenes, GeoLift runs the following sequence:

  1. Data mapping and transformation
  2. Dataset validation and balancing
  3. Treatment-date resolution
  4. Pre-treatment market characterization
  5. Test/control comparison setup
  6. Multi-model impact estimation
  7. Power and quality evaluation
  8. Report + metrics publication

This sequence is built to protect teams from acting on weak or misleading runs.

Step 5: Outputs become available​

After completion, teams can access:

  • executive-style PDF report,
  • run status and quality labels,
  • per-method impact metrics,
  • standardized output data for governance/review.

Step 6: Decision checkpoint​

Use the output to choose one of these paths:

  • Scale: strong evidence and acceptable risk
  • Refine and re-test: mixed signals or weak controls
  • Hold: no reliable impact evidence
  • Fix and rerun: data quality or technical completion issue

Suggested operating cadence​

For recurring interventions:

  • run GeoLift on a planned cadence,
  • compare run quality over time,
  • track repeated learnings by intervention type/geo cluster.

This builds a repeatable evidence engine, not one-off analysis.

Pre-run checklist​

  • Intervention and KPI are clearly defined
  • Treatment markets are finalized
  • Controls are likely untreated
  • Treatment date is accurate
  • History window is sufficient
  • Decision owner is identified

Post-run checklist​

  • Quality status reviewed first
  • Effect size and uncertainty reviewed together
  • Model agreement checked
  • Practical significance assessed
  • Explicit action (scale/refine/hold) documented

Common workflow pitfalls​

  • Running without a clear decision question
  • Treating "not significant" as "no impact" without checking power
  • Ignoring data issues and using results anyway
  • Scaling based on point estimate alone
  • Comparing runs with different KPI definitions