Markin + Amazon Redshift integration
Direct reads against the warehouse you already operate.
What is a Amazon Redshift AI agent?
A Markin and Amazon Redshift setup is a read role plus a schema Markin can write to. It reads customer and revenue tables in place, decides what each customer should be offered next, and writes decisions and holdout assignments back for your existing activation pipeline to move.
No migration is required for Markin to start. The connection is a read role plus a schema it can write to, which is a change your data team can review in an afternoon.
What Markin reads
- Customer and revenue tables
- Event and exposure history
What Markin writes back
- Decision tables
- Experiment and holdout 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 Amazon Redshift?
- How do I add customer decisioning on top of Redshift?
- Do I need to migrate off Redshift to run next best action?
- How do I get retention actions out of Redshift and into email?
- What permissions does a decision layer need on Redshift?
What Markin does in Amazon Redshift
- 01Read customer, revenue and exposure history in place.
- 02Build the ARPU baseline per cohort and market.
- 03Rank revenue opportunities by expected value and confidence.
- 04Write decision tables to a schema you own.
- 05Write holdout membership for every launched action.
- 06Write experiment reads joinable to your existing reporting.
- 07Run on your cluster inside your existing cost controls.
- 08Leave the warehouse exactly as it was if the connection is removed.
Scope of work
The growth work behind Amazon Redshift
The list above is what Markin touches in Amazon Redshift. 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.
- Map every customer, account, subscription and plan in the systems you already run.
- Rebuild the revenue baseline from orders, payments, refunds and credits.
Find where revenue is leaking
Normally a quarterly analyst deep dive.
- Size every finding in revenue, not in percentage points.
- Watch ARPU by cohort, plan, market, channel and tenure for drift that clears noise.
Explain why
Normally a two-week investigation pulled off the roadmap.
- Show the counter-evidence, not only the supporting cut.
- Keep the query trail so an analyst can reproduce every number.
Write hypotheses worth funding
Normally a workshop, limited to the ideas in the room.
- Keep the full portfolio visible, including what was deliberately not funded.
- Write hypotheses continuously across marketing, product, pricing and technical health.
Decide per customer
Normally segment rules refreshed when someone has time.
- Pick the channel, timing and incentive level, not only the message.
- Cap discount and margin exposure at the level finance agreed.
Execute in the tools you already run
Normally a ticket, then a slot in next month's calendar.
- Route offers, retention plays and save flows to the right surface.
- Open the work as a draft for approval where a human should sign off.
Prove it caused the revenue
Normally argued about, rarely measured.
- Hold back a randomised control group on every decision, not one global holdout.
- Read uplift in revenue per customer against that control.
Retire, govern and hand over
Normally nobody's job, so nothing is ever switched off.
- Keep personal data in your systems and act on it in place.
- Show the whole decision trail when legal, finance or an auditor asks.
Amazon Redshift questions
- How does Markin connect to Amazon Redshift?
- 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 Amazon Redshift?
- Customer and revenue tables; Event and exposure history.
- What does Markin write back into Amazon Redshift?
- Decision tables Experiment and holdout 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.