What it does
Builds a custom attribution model in Google Analytics (GA4) that weights traffic sources more accurately, then distributes readable reports to marketers highlighting which campaigns deserve more budget.
Why I recommend it
Out-of-the-box attribution often misleads teams, especially with long journeys. Automating a tailored model yields better spend decisions.
Expected benefits
- Accurate multi-touch insights
- Confident budget reallocations
- Shared visibility for marketing and sales
- Reduced manual spreadsheet work
How it works
Pull conversion paths from GA4 -> apply time-decay or position-based weighting -> calculate contribution per channel/source/medium -> store results in BigQuery or Sheets -> send summary via Looker Studio dashboards and scheduled email digests.
Quick start
Export last 60 days of conversion paths. Manually apply simple position-based weighting in Sheets to show how attributions change. Use that as proof of value before automating.
Level-up version
Blend CRM revenue data, include offline events, create automated anomaly alerts, and sync winning channel insights back to ad platforms for budget automation.
Tools you can use
Analytics: GA4, BigQuery, Looker Studio
Automation: Apps Script, Zapier, dbt
Visualization: Google Sheets, Tableau
Data warehouse: Snowflake, Redshift
Also works with
Mixpanel, Adobe Analytics, Amplitude data sets.
Technical implementation solution
- No-code: GA4 export -> Google Sheets macros -> scheduled email via Apps Script.
- API-based: Cloud Function pulls GA4 API data nightly -> Python calculates weights -> pushes to BigQuery + Looker Studio -> Slack digest with topline insights.
Where it gets tricky
Handling cross-device journeys, sampling thresholds in GA4, aligning marketing taxonomy, and convincing stakeholders to trust the new model.
