HomeServicesRevenue Intelligence & Executive BI
Service 02 · Revenue Intelligence & Executive BI
Business intelligence your decisions can rely on
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.
First call within 48 h · 2 weeks · from €2,900 net
You're in the right place if:
Meta, HubSpot, and Stripe report three different acquisition costs.
CAC differs between the dashboard and the finance sheet.
Pipeline attribution isn't stable from month to month.
There's no shared definition of a qualified opportunity.
What we build
Two maturity stages. One foundation.
Tier 1 is the foundation Tier 2 doesn't work without. Predictive on inconsistent data automates hallucinations.
Tier 1 · Foundation
Fivetran · BigQuery · dbt · Looker StudioClean marketing-to-CRM mapping, BigQuery as the central data foundation, dbt models for the core metrics. The attribution scaffold everything else builds on.
Fivetran setup (Meta, Google Ads, HubSpot, GA4), BigQuery project in EU region with IAM and cost controls, dbt with staging/intermediate/mart, dashboards for marketing and leadership.
Tier 2 · Executive BI & Predictive
Stripe · BigQuery ML · Prophet · SlackStripe and backend integration, LTV cohort analysis, anomaly detection, and forecasting at channel and cohort level.
Stripe API in BigQuery, LTV models per acquisition cohort, anomaly alerts on ROAS drift, forecasting layer (Prophet or ML depending on data volume).
How it works
Four phases, each with one clear job.
No one-tool Swiss-army-knife. Each phase uses the tool that fits its job.
Ingestion
Pre-built connectors pull Stripe, HubSpot, Meta, Google Ads, and Shopify incrementally into the EU region.
Storage
BigQuery in EU multi-region: storage and compute separated, pay-per-query, no clusters to size.
Transformation
dbt turns raw tables into tested models. When sales changes the CAC definition, it changes in one place.
Output
Executive view on one page, operational views for marketing and sales, anomaly alerts in Slack.
Process
Four phases, fixed order.
always starts with phase 1 · no blind build
Audit Sprint
Five layers audited, findings ranked, effort estimated. The result is a report you could act on without us.
Architecture
Data contract, event design, target architecture. We fix where each number is produced and who guarantees it.
Build Sprint
Delivery in sprints, every module signed off on its own. Your team stays involved, not locked out.
Managed Evolution
Monitoring, release support, platform updates. Optional; plenty of clients run the setup themselves.
What you get
A report, not a workshop afterglow.
The Audit Sprint ends in a document: findings per layer, severity, effort, sequence. Not a slide deck full of recommendations in the subjunctive.
→ Findings with severity and reproduction path
→ Effort estimate per finding, in person-days
→ A draft data contract for the core events
→ An implementation plan another agency could execute
Deliverables · Revenue Intelligence & Executive BI
Structure taken from this page's scope of delivery. The concrete scope comes out of the audit.
Scopes
Three ways in, one starting point.
Audit Sprint
from €2,900 net
2 weeks
We audit what is wrong. Prioritised report + action plan.
Request an Audit Sprint →Managed Evolution
monthly
3-month minimum
Ongoing partnership. Analytics as a product.
Request Managed Evolution →From the integrations catalog
Tools this service works with.
Category: BI & VisualisationCategory: CDP & Event PipelinesCategory: CRMCategory: Marketing Automation
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.
Through the official HubSpot API via Fivetran. Fivetran supports [EU multi-region](https://fivetran.com/docs/using-fivetran/fivetran-dashboard/account-settings/troubleshooting/use-fivetran-multiple-regions); the sync runs in EU-Frankfurt and the data stays in the EU region. 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.
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. Data (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.
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 2 weeks 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.
