SEO analytics that speak revenue, not rankings

Reporting is the output: a document or dashboard that communicates what happened. Analytics is the infrastructure that makes the reporting trustworthy and repeatable. Most agencies deliver reporting but skip analytics. you get a PDF with rank positions every month, but no underlying data model that lets you ask a new question. We build the analytics layer first so the reporting answers real business questions instead of summarizing rank tracker exports.

GA4 retains event data for 14 months maximum, and the reporting interface does not let you join Search Console data with session data or CRM data. BigQuery removes both constraints. You get three-plus years of GSC impression and click data, SQL-level query flexibility, and the ability to join organic search data against any other data source your business has. For any client doing serious attribution work, BigQuery is not optional.

GA4 uses last-click or data-driven attribution by default. For B2B SaaS and considered purchases where the sales cycle is 30 to 90 days, last-click attribution systematically undervalues organic search (which often drives the first brand awareness touch) and overvalues the final retargeting ad. We implement multi-touch Markov chain attribution on top of BigQuery data, which distributes revenue credit across all touchpoints proportional to their actual influence on conversion. Organic typically gets 15 to 40% more credit than last-click models assign it.

The technical setup takes three to five days once we have Search Console access and a GCP project. This covers enabling the bulk export, validating the schema, writing the ingestion pipeline, and setting up monitoring. Historical data backfill (typically 16 months) runs automatically in the background and completes within 48 hours for most sites. The full analytics stack including dashboards takes three to four weeks.

A forecast document starts with your current baseline: position distribution across target keywords, estimated monthly traffic, estimated conversion rate, and average deal value or order value. We model what happens to each metric if we move specific keyword clusters from positions 20 to 30 into positions 5 to 10. The output is a table with three scenarios (conservative, base, aggressive) showing estimated monthly traffic gain and estimated monthly revenue impact. Example: 10,000 forecasted visits at a 2% conversion rate and a $150 average order value equals $30,000 per month in incremental revenue. We run this model for every major keyword cluster we target.

Both. If you have existing Looker Studio, Power BI, or Tableau assets, we audit the data model and fix the underlying reliability issues before adding new metrics. If you are starting from scratch, we recommend the tool that matches your team's technical environment and reporting cadence. We never pick a tool because it is impressive; we pick the one your team will actually use next Monday.