Markin + BigQuery integration
Signal and outcome reads without moving a row.
What is a BigQuery AI agent?
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 Markin reads
- 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
How the connection works
| Pattern | What it means here |
|---|---|
| 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 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
- 01Read GA4 and product event exports partitioned by date.
- 02Read transaction, subscription and entitlement tables.
- 03Read consent state so ineligible customers never enter a decision.
- 04Build propensity and value features from the events already landed.
- 05Flag revenue anomalies by acquisition source, plan and device.
- 06Write date-partitioned decision tables with the reason attached.
- 07Write holdout membership and per-experiment reads.
- 08Expose a scheduled query your team can inspect and re-run.
Scope of work
The growth work behind BigQuery
The list above is what Markin touches in BigQuery. A connector is only the surface. Below is the work itself: what an analyst, a lifecycle manager, a data scientist and an experimentation lead would do between them, running continuously against your own data.
Read the estate
Normally a data engineer, once, then never refreshed.
- 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
Normally a quarterly analyst deep dive.
- 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
Normally a two-week investigation pulled off the roadmap.
- 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
Normally a workshop, limited to the ideas in the room.
- Write hypotheses continuously across marketing, product, pricing and technical health.
- Attach the expected revenue effect and the population it applies to.
Decide per customer
Normally segment rules refreshed when someone has time.
- 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
Normally a ticket, then a slot in next month's calendar.
- 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
Normally argued about, rarely measured.
- 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
Normally nobody's job, so nothing is ever switched off.
- Show the whole decision trail when legal, finance or an auditor asks.
- Hand the team a portfolio they can read, question and overrule.
Playbooks that run through BigQuery
- PlaybookRun a retention offer before renewal without paying customers who were staying anywayPre-renewal save offers on Stripe, sized by risk instead of by list membership.
- PlaybookWin back lapsed customers with a silence window instead of a discount ladderReactivation timed on returning intent, not on days since last order.
Teams use BigQuery together with
BigQuery questions
- 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.