Revenue Intelligence & Executive BI
The Single Source of Truth across marketing, CRM, and backend. No more CPA fairy tales from the ad networks. True customer acquisition cost, real LTV, AI-ready for 2026.
- BigQuery
- dbt
- Fivetran
- Airbyte
- Looker Studio
- Power BI
What we build
Meta reports €3.50 CAC. HubSpot says €47. Stripe shows €89 in actual acquisition cost. Three numbers, three systems, one bank account. This is not a reporting problem. It is an architecture problem.
Revenue Intelligence & Executive BI is the data architecture that consolidates those three numbers into one. Marketing spend from the ad platforms, lead stages from the CRM, revenue from the payment backend, all loaded into BigQuery, transformed in dbt, served to Looker Studio or Power BI. One source, one calculation logic, one dashboard per audience.
Prerequisite: Clean server-side data collection. Loading marketing data into a warehouse that is fed by broken tracking only automates the error. See Measurement & Privacy Engineering.
The architecture
Four phases, each with one clear responsibility. No one-tool Swiss-army-knife. Each phase uses the tool that fits its job.
Pull data from the source systems
Tools: Fivetran, Airbyte
Pre-built connectors for Stripe, HubSpot, Meta Ads, Google Ads, Salesforce, Shopify. Incremental loads, schema-drift handling, backfill mechanics. What used to cost two weeks of custom Python per source is configuration today.
Central warehouse in the EU region
Tool: Google BigQuery (EU multi-region)
Storage and compute separated, pay-per-query. No infrastructure to babysit, no clusters to size. EU multi-region satisfies GDPR data-residency requirements. SCCs and DPA with Google Cloud are signed.
Business logic as code
Tool: dbt (data build tool)
Raw tables become consolidated models: dim_customers, fct_orders, mart_marketing_attribution. Versioned in Git, tested automatically, documented. When the sales team changes the CAC definition, it changes in one place, not in twelve dashboard filters.
Dashboards, models, alerts
Tools: Looker Studio (formerly Data Studio), Power BI, Slack
Executive view for leadership, one page, PDF-ready. Operational views for marketing and sales. Predictive LTV per acquisition cohort. Anomaly alerts in Slack when ROAS or lead volume drifts from forecast.
We do not build isolated dashboards. We build resilient ELT pipelines on which dashboards are the end product, not the project.
Who it's for
CMO / VP Marketing
Defend the marketing budget in front of the CFO. Need a report that shows CAC and LTV from Stripe data, not from Meta attribution. One page, no explanation slide in front of it.
Founder / Leadership
Want to know which channel brings actual customers, not which channel claims the conversion. Need the bridge between marketing spend and the bank account.
Head of Data / BI
Have BigQuery. Need the marketing and CRM sources cleanly connected and modelled in dbt. Without custom Python connectors that break on every Meta API update.
Performance teams (E-commerce)
Want ROAS, POAS, and real margin in one view. Not across three tools running three different attribution models.
The tier model
Two maturity stages. Tier 1 is the foundation; Tier 2 does not work without it. Building predictive models on inconsistent data automates hallucinations.
Foundation
Clean marketing-to-CRM mapping. BigQuery as the central data foundation. dbt models for the core metrics (CAC, ROAS, conversion funnel). An attribution scaffold everything else builds on.
- Fivetran setup for the relevant sources (Meta, Google Ads, HubSpot, GA4)
- BigQuery project in EU region, IAM, cost controls
- dbt project with
staging,intermediate,martlayers - Looker Studio dashboards for marketing and leadership
Delivery: 4 to 8 weeks. Fixed price after audit.
Executive BI & Predictive
Stripe and backend integration. LTV cohort analysis. Anomaly detection (statistical and ML-based). Forecasting at channel and cohort level.
- Stripe API and payment backend in BigQuery
- LTV models per acquisition cohort (channel, campaign, period)
- Anomaly alerts: ROAS drift, conversion anomalies, data-quality breaks
- Forecasting layer (Prophet or ML-based depending on data volume)
Prerequisite: Tier 1 is live, or a comparable foundation exists.
Scope in detail
Source systems we typically connect
- Paid advertising: Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads
- CRM: HubSpot, Salesforce, Pipedrive
- Payment / backend: Stripe, Adyen, Mollie, custom Postgres
- E-commerce: Shopify, WooCommerce, Magento
- Analytics: GA4 (BigQuery Export), Plausible CE
- Marketing automation: Klaviyo, HubSpot Marketing
What ships as dbt models
dim_customers,dim_campaigns,dim_productsfct_orders,fct_sessions,fct_marketing_spendmart_cac_by_channel,mart_ltv_cohorts,mart_executive_summary- Data-quality tests per model (dbt tests, freshness checks)
- Documentation via dbt docs (what is calculated how, who owns it)
Engagement depths
Three depths. Clear scopes.
No retainer trap.
Audit Sprint
We audit what's wrong. Report + prioritised action plan.
plus statutory VAT · fixed price for a clearly bounded scope
Included in the fixed price
- 1 domain
- 1 analytics property
- 1 tag manager / tracking setup
- 1 CMP
- up to 5 core conversions
- 10 working days
- PDF report + 90-min walkthrough
What you get
- Full analysis of your existing setup
- Prioritised report with concrete action items
- Walkthrough call with the team (90 min)
- No follow-up contract, no retainer obligation
When it fits
When the setup works but the numbers are being argued about internally. Or you're unsure what from a UA→GA4 migration still holds.
For e-commerce, multiple domains or App + Web: Audit Sprint Plus, €3,900 net fixed price. Bonus: 50 % of the Audit Sprint credits toward a Build Sprint commissioned within 30 days.
Request an Audit SprintBuild Sprint
Fresh build or restructure of a tracking setup.
plus statutory VAT · final fixed price after scope definition
Typical scope
- 1 domain (multi-domain on request)
- 1 analytics property (GA4 or Piwik PRO)
- server-side container (Stape or own cloud)
- 1 CMP with Consent Mode V2
- up to 15 events / conversions
- 4–8 weeks delivery
- Blueprint, QA sign-off, handover docs
What you get
- Measurement Blueprint for your dev team
- GTM + server-side setup incl. CMP integration
- Full QA against the blueprint with sign-off
- Handover docs + 30-day post-launch support
When it fits
When analytics is structurally broken and fixing it in-flight costs more than a clean rebuild.
Managed Evolution
Ongoing partnership. Analytics as a product, not a one-off project.
plus statutory VAT · monthly cancellation after the minimum term
Included in the monthly price
- up to 3 domains under active care
- GA4 + server-side stack maintenance
- monthly roadmap + sprint planning
- QA on every release deploy
- Slack channel, < 4 h response (Mon–Fri)
- monthly report + executive summary
- 3-month minimum, then monthly
What you get
- Monthly development + feature rollouts
- Ongoing QA on every deploy
- Executive reports + dashboard evolution
- Slack support with guaranteed response times
When it fits
When analytics has to grow with you (new campaigns, new products, new data sources) and you don't want to build that team internally.
All prices net, plus statutory VAT. For companies in Germany, Austria and Switzerland.
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Why dbt instead of plain SQL views in BigQuery?
dbt enforces three things raw views do not: Git versioning, automated tests per model, and a clean layer separation between source data and business logic. With seven sources feeding ten downstream metrics, the view-only approach breaks on the first schema change. A view that nobody tests is a bomb on a fuse. dbt fixes that in days, not in weeks of incident response.
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How do you connect HubSpot data in a GDPR-compliant way?
Through the official HubSpot API via Fivetran. Fivetran supports [EU multi-region](https://www.fivetran.com/docs/getting-started/data-residency); the sync runs in EU-Frankfurt and the data does not leave the EU. PII fields (email, phone) can be excluded or pseudonymised per pipeline. With SCCs and a DPA in place, the connection stays audit-clean. An on-prem alternative via Airbyte Self-Hosted is available when the cloud vendor is contractually ruled out.
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What does the architecture cost monthly to operate?
A typical mid-market configuration (5 sources, ~100M rows/month, 4 dbt models daily, 2 production dashboards) lands at €300–800 BigQuery, €200–500 Fivetran depending on connector mix, plus €50 dbt Cloud if used. Looker Studio is free; Power BI Pro is €10 per user. Operating cost scales with data volume, not with report count. Custom-ETL maintenance falls away.
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Why BigQuery and not Snowflake or Redshift?
For DACH mid-market setups, BigQuery is the more pragmatic tool in most cases: no cluster sizing, EU multi-region out of the box, native GA4 integration via the export, transparent pay-per-query pricing. Snowflake wins for multi-cloud scenarios and very high concurrency. Redshift sits more naturally in AWS-centric stacks. We recommend the warehouse to fit the situation, not the preference.
Next step
Which CAC is right: Meta, HubSpot, or Stripe?
An Audit Sprint clarifies in 10 business days which number is wrong in which system and what a consolidated data foundation looks like. Prioritised report. 60-minute walkthrough call. No follow-on contract, no forced retainer.