Markin + Amplitude integration
Behavioural signal in, hypotheses out.
What is a Amplitude AI agent?
A Markin and Amplitude setup reads behavioural cohorts, funnels and feature adoption as signal for hypotheses about revenue, then decides and measures the action elsewhere. Amplitude explains what happened; Markin decides what to do about it and proves it.
Product analytics is very good at showing that something is wrong and silent about what to do next. Markin reads the same events and produces a ranked, sized queue of actions instead of a chart.
What Markin reads
- Event streams and cohort definitions
- Funnel and retention curves
What Markin writes back
- Cohort membership reflecting Markin decisions, for your own analysis
How the connection works
| Pattern | What it means here |
|---|---|
| Read only | Markin reads signal from this system. Nothing is written back and no schema is changed. |
| 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. |
What can you ask Markin to do in Amplitude?
- How do I turn Amplitude insights into actions automatically?
- How do I find the product friction that costs the most revenue?
- Can Amplitude cohorts trigger a next best action?
- What is the difference between product analytics and decisioning?
What Markin does in Amplitude
- 01Read cohorts, funnels and retention curves.
- 02Read feature adoption and session-level behaviour.
- 03Detect friction points that correlate with lost revenue.
- 04Convert a behavioural finding into a sized hypothesis.
- 05Feed the resulting decision to the channel that can act on it.
- 06Match Amplitude cohorts to holdout membership so reads stay clean.
- 07Read post-action behaviour back as the outcome signal.
- 08Report which product frictions produced the largest recovered revenue.
Scope of work
The growth work behind Amplitude
The list above is what Markin touches in Amplitude. 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.
- Derive behavioural features from event history without a new pipeline.
- Reconcile the same customer across billing, CRM, product and support identities.
Find where revenue is leaking
Normally a quarterly analyst deep dive.
- Detect churn risk building in a segment before it shows in the monthly number.
- Detect involuntary churn from failed payments, card expiry and retry behaviour.
Explain why
Normally a two-week investigation pulled off the roadmap.
- Run the investigation automatically and return the drivers with their evidence.
- Separate mix effects from real behaviour change.
Write hypotheses worth funding
Normally a workshop, limited to the ideas in the room.
- Attach the expected revenue effect and the population it applies to.
- Attach the evidence and the assumption each one depends on.
Decide per customer
Normally segment rules refreshed when someone has time.
- Attach a plain-language reason and an expiry to every decision.
- Suppress an action rather than send a weak one, and log why.
Execute in the tools you already run
Normally a ticket, then a slot in next month's calendar.
- Recompute idempotently so a replay never double-sends.
- Roll a decision batch back cleanly when something looks wrong.
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.
- Hand the team a portfolio they can read, question and overrule.
- Retire programmes automatically when they stop beating control.
Teams use Amplitude together with
Amplitude questions
- How does Markin connect to Amplitude?
- Read only, Attribute write-back. Markin reads signal from this system. Nothing is written back and no schema is changed.
- What does Markin read from Amplitude?
- Event streams and cohort definitions; Funnel and retention curves.
- What does Markin write back into Amplitude?
- Cohort membership reflecting Markin decisions, for your own analysis
- 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.