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Some brands have multiple event sources (GA4, Snowplow, Heap, Elevar, Blotout, etc.). Those sources can record the same touchpoint within seconds of each other, creating the risk of double-counting. For multi-source customers, SourceMedium uses deduplication rules to merge streams into a canonical view that preserves richness while preventing over-counting.

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High-level approach

For multi-source data, we consider touchpoints duplicates when core attributes match within a small time window. The dedupe fingerprint typically includes:
  • Timestamp (rounded to seconds, in UTC)
  • Standardized event name
  • UTM source / medium / campaign (with NULL normalized)
Fields like utm_content, utm_term, and click IDs are preserved as enrichment but don’t always participate in the fingerprint (so you can still do creative-level analysis without inflating counts).

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Where this shows up

These patterns are most relevant when you query MTA / journey tables, like:
  • your_project.sm_experimental.obt_purchase_journeys_with_mta_models

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Verify you’re not double-counting purchases

SELECT
  COUNT(DISTINCT purchase_order_id) AS distinct_orders,
  COUNTIF(sm_event_name = 'purchase') AS purchase_event_rows
FROM `your_project.sm_experimental.obt_purchase_journeys_with_mta_models`
WHERE event_local_datetime >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY);

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Compare touchpoint volumes by source system

SELECT
  COALESCE(NULLIF(LOWER(TRIM(source_system)), ''), '(unknown)') AS source_system,
  COUNT(*) AS touchpoints
FROM `your_project.sm_experimental.obt_purchase_journeys_with_mta_models`
WHERE event_local_datetime >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
GROUP BY 1
ORDER BY touchpoints DESC;
Single-source customers should remain unchanged; multi-source dedupe is only applied when multiple sources exist for the same order/journey.