MARKETING

Weekly UTM Drift Audit Against BigQuery Clickstream

Compares the UTM values actually appearing in your BigQuery clickstream against your approved taxonomy each week and reports rogue or off-spec tags eroding attribution.

CategoryMarketing
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule starts the drift audit
  • ActionQuery live UTM tuples + sessions from BigQueryGoogle BigQueryBigQuery
  • ActionLoad approved UTM taxonomy reference tableGoogle BigQueryBigQuery
  • LogicClassify each tuple as approved, typo, or rogue
  • ActionPersist findings to BigQuery audit tableGoogle BigQueryBigQuery
  • OutputSend ranked drift report to SlackSlack

What it does

Queries the distinct UTM combinations that real visitors hit over the past week from your BigQuery event tables, then diffs them against your canonical taxonomy of approved source/medium/campaign values. It surfaces tags that no one approved — typos, vendor-injected params, legacy campaigns — ranked by sessions affected, so you know which drift is actually costing you reporting accuracy.

When to use it

Run it weekly to catch UTM rot in the wild: links that bypassed pre-launch checks, partners appending their own params, or stale campaigns still driving traffic under wrong tags.

How it works

  1. 1A weekly schedule trigger kicks off the audit.
  2. 2A BigQuery step pulls distinct UTM tuples plus session counts from the last 7 days.
  3. 3A second BigQuery read loads the approved taxonomy reference table.
  4. 4A diff step classifies each live tuple as approved, near-match (likely typo), or rogue, weighting by sessions.
  5. 5Findings are written back to a BigQuery audit table for trend tracking.
  6. 6A ranked drift report is delivered to Slack with the top offenders and their traffic impact.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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