Markin + Tealium integration
Real-time profiles in, committed decisions out.
What is a Tealium AI agent?
A Markin and Tealium setup adds arbitration in front of real-time routing. Tealium collects visitor profiles and streams events; Markin decides which of the possible actions is worth the contact and writes one committed decision back for the connectors to execute.
Tealium is strong at collecting and routing in real time. What it does not do is decide which of forty possible actions is worth the contact. Markin arbitrates before anything reaches a connector.
What Markin reads
- Visitor profiles and badges
- Real-time event streams
What Markin writes back
- Decision attributes
- Trigger events for existing connectors
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 Tealium?
- How do I prioritise between competing offers in Tealium?
- Can Tealium decide the next best action or only route it?
- How do I stop over-contacting customers across Tealium connectors?
- How do I add real-time decisioning to a Tealium estate?
What Markin does in Tealium
- 01Read visitor profiles, badges and audience membership.
- 02Read real-time event streams as in-session signal.
- 03Arbitrate competing candidate actions into one committed decision.
- 04Apply frequency and eligibility rules before anything reaches a connector.
- 05Write decision attributes back onto the visitor profile.
- 06Emit trigger events for the connectors already configured.
- 07Hold back a randomised control so the effect can be read.
- 08Log every suppressed action and the reason it lost arbitration.
Scope of work
The growth work behind Tealium
The list above is what Markin touches in Tealium. 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.
- Rank drivers by how much of the movement each one accounts for.
- Check whether the same driver is present in comparable segments.
Write hypotheses worth funding
Normally a workshop, limited to the ideas in the room.
- Rank the portfolio by expected value, not by seniority.
- Drop hypotheses that a past experiment already answered.
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.
- Re-test assumptions that decay, such as price sensitivity and seasonality.
- Log every read, decision and write with its reason for audit.
Teams use Tealium together with
Where this shows up
- SolutionNext-Best ActionNext-best action for enterprise growth teams
- ComparisonMarkin + Segment or Tealium: turning customer context into revenue decisionsSegment and Tealium collect, resolve and govern customer data. Markin decides which revenue opportunity is worth acting on. How the layers divide the work.
Tealium questions
- How does Markin connect to Tealium?
- 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 Tealium?
- Visitor profiles and badges; Real-time event streams.
- What does Markin write back into Tealium?
- Decision attributes Trigger events for existing connectors
- 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.