Markin + Optimove integration
Decisions as customer attributes for existing campaigns.
What is a Optimove AI agent?
A Markin and Optimove setup adds hypothesis generation and causal measurement in front of Optimove's orchestration. Markin decides the action and writes it back as a customer attribute or trigger; Optimove keeps running the campaign calendar it already runs.
Optimove already orchestrates a dense campaign calendar. Markin decides which of those plays a customer deserves this week, and when the answer is none, so the calendar stops competing with itself.
What Markin reads
- Customer attributes and lifecycle stage
- Campaign response and send history
What Markin writes back
- Customer attributes carrying the chosen action and its expiry
- Events for campaign entry
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. |
| Triggered event | Markin emits an event that starts or advances a journey. Near-immediate, and the channel keeps its own governance. |
What can you ask Markin to do in Optimove?
- What are the best Optimove alternatives?
- How do I generate new campaign ideas rather than optimising existing ones?
- Can Optimove be fed decisions from an external model?
- How do I measure incremental revenue across Optimove campaigns?
What Markin does in Optimove
- 01Read customer attributes and campaign exposure history.
- 02Read realised revenue per customer and campaign.
- 03Propose candidate actions that no campaign covers yet.
- 04Write the decided action and reason as a customer attribute.
- 05Emit trigger events for time-sensitive moments.
- 06Maintain randomised holdouts independent of campaign control groups.
- 07Read outcomes back keyed to the decision ID.
- 08Report uplift per action against its own holdout.
Scope of work
The growth work behind Optimove
The list above is what Markin touches in Optimove. 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.
- Stop an experiment early when the evidence is conclusive either way.
- Refuse to call a result that has not cleared the evidence standard.
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.
Optimove questions
- How does Markin connect to Optimove?
- Attribute write-back, Triggered event. 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 Optimove?
- Customer attributes and lifecycle stage; Campaign response and send history.
- What does Markin write back into Optimove?
- Customer attributes carrying the chosen action and its expiry Events for campaign entry
- 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.