---
title: Markin + BigQuery
url: https://markin.ai/integrations/bigquery
category: Warehouses and lakes
description: Connect Markin to BigQuery: read behavioural and revenue history in place and write decisions, holdouts and causal reads back into your own dataset.
---

# Markin + BigQuery

> Signal and outcome reads without moving a row.

**Job:** Turn GA4 and transaction data in BigQuery into proven revenue actions.

A Markin and BigQuery setup reads GA4 exports, transactions and customer attributes in place, decides the next best action per customer, and writes decision tables, holdout membership and experiment reads back into a dataset you own. No extract leaves your project and no schema is changed.

Most large B2C estates already land every event in BigQuery. Markin treats it as the source of truth for both the hypothesis and its verdict, so nothing has to be reconciled between two systems later.

## What can you ask Markin to do in BigQuery?

- How do I act on GA4 BigQuery exports instead of just reporting on them?
- Can I run churn prediction and activation from BigQuery?
- How do I A/B test lifecycle actions with a holdout in BigQuery?
- How do I connect BigQuery to Braze without building a pipeline?

## What Markin does in BigQuery

- Read GA4 and product event exports partitioned by date.
- Read transaction, subscription and entitlement tables.
- Read consent state so ineligible customers never enter a decision.
- Build propensity and value features from the events already landed.
- Flag revenue anomalies by acquisition source, plan and device.
- Write date-partitioned decision tables with the reason attached.
- Write holdout membership and per-experiment reads.
- Expose a scheduled query your team can inspect and re-run.

## The growth work behind BigQuery

### Read the estate

- Rebuild the revenue baseline from orders, payments, refunds and credits.
- Derive behavioural features from event history without a new pipeline.

### Find where revenue is leaking

- Watch ARPU by cohort, plan, market, channel and tenure for drift that clears noise.
- Detect churn risk building in a segment before it shows in the monthly number.

### Explain why

- Keep the query trail so an analyst can reproduce every number.
- Run the investigation automatically and return the drivers with their evidence.

### Write hypotheses worth funding

- Write hypotheses continuously across marketing, product, pricing and technical health.
- Attach the expected revenue effect and the population it applies to.

### Decide per customer

- Cap discount and margin exposure at the level finance agreed.
- Attach a plain-language reason and an expiry to every decision.

### Execute in the tools you already run

- Open the work as a draft for approval where a human should sign off.
- Recompute idempotently so a replay never double-sends.

### Prove it caused the revenue

- Refuse to call a result that has not cleared the evidence standard.
- Publish the readout in the same place for every experiment.

### Retire, govern and hand over

- Show the whole decision trail when legal, finance or an auditor asks.
- Hand the team a portfolio they can read, question and overrule.

Full catalogue: [Everything a growth team does, running every day.](https://markin.ai/growth-work)

## Activation patterns

- **Attribute write-back.** Markin writes the decision onto the customer profile; your existing journeys read it as an entry condition. Latency is the platform's sync interval.
- **Read only.** Markin reads signal from this system. Nothing is written back and no schema is changed.

## What Markin reads from BigQuery

- GA4 and product event exports
- Transaction and subscription tables
- Customer attributes and consent state

## What Markin writes back

- Decision tables partitioned by date
- Holdout membership and experiment reads

## Used together with

- [ga4](https://markin.ai/integrations/ga4)
- [hightouch](https://markin.ai/integrations/hightouch)
- [braze](https://markin.ai/integrations/braze)
- [segment](https://markin.ai/integrations/segment)
- [stripe](https://markin.ai/integrations/stripe)
- [google-ads](https://markin.ai/integrations/google-ads)

## Related

- [Solution: revenue-expansion](https://markin.ai/solutions/revenue-expansion)
- [Solution: lifecycle-automation](https://markin.ai/solutions/lifecycle-automation)
- [Comparison](https://markin.ai/compare/data-warehouse-vs-cdp-vs-decision-layer)

## FAQ

**How does Markin connect to BigQuery?**

Attribute write-back, Read only. Markin writes the decision onto the customer profile; your existing journeys read it as an entry condition. Latency is the platform's sync interval.

**What does Markin read from BigQuery?**

GA4 and product event exports; Transaction and subscription tables; Customer attributes and consent state.

**What does Markin write back into BigQuery?**

Decision tables partitioned by date Holdout membership and experiment reads

**Do we have to move our data to Markin?**

No. Markin reads from your warehouse, product events and operational systems in place, on your compute, under the access rules your data team already set. Nothing is copied into a separate customer base and there is no vendor-side profile store to migrate off later.

**Does Markin replace our engagement platform or CDP?**

No, and it should not. Your engagement platform keeps the channel, the templates, the deliverability and the governance. Your CDP keeps identity and consent. Markin adds the layer neither has: deciding which action deserves to exist for each customer, and proving it against a holdout.

**What if the system we use is not listed?**

The four activation patterns cover almost everything: attribute write-back, triggered event, decision API and direct surface rendering. Any system that exposes an API, accepts a table, or can read a warehouse column can receive decisions. New connectors are built during deployment, typically in days.

Source: https://markin.ai/integrations/bigquery