---
title: Markin + Amplitude: from behavioural insight to revenue decisions
url: https://markin.ai/compare/markin-and-amplitude
kind: stack
description: Amplitude observes behaviour and tests features. Markin turns that into sized revenue decisions per customer, across channels, measured against a holdout.
updated: 2026-09-03
---

# Markin + Amplitude: from behavioural insight to revenue decisions

> Amplitude is product analytics with experimentation and recommendations: it observes behaviour, builds cohorts, runs feature experiments and can recommend content toward a predictive goal. Markin sits above it and converts that observation into sized revenue decisions per customer, which action, in which channel, at what cost, with a holdout attached. Amplitude shows what is happening; Markin decides what to do about it, in revenue terms.

## In short

- Amplitude: What is happening in the product, and which feature variation or item performs? Output: Behavioural analysis, cohorts, experiment significance, ranked recommendations.
- Markin, the decision + execution layer: Given what we see, which revenue action is worth taking for this customer, and what is it worth? Output: A ranked, sized decision per customer,  including hold, pushed into the channels that can act on it.
- A cohort describes a population; it does not attach a revenue value, a margin or a cost per intervention to each customer in it.
- It turns what Amplitude observes into per-customer revenue decisions with a holdout attached, and acts in the channels that can move revenue, including customers a feature experiment never reaches.

## What each layer is

**Amplitude**, A product analytics platform with feature experimentation and content recommendations: behavioural insight, cohorts, A/B tests and AutoML item recommendations toward a predictive goal.

Answers: What is happening in the product, and which feature variation or item performs?

**Markin, the decision + execution layer**, A layer that turns behavioural signal into sized revenue decisions per customer, selecting the treatment with the highest expected incremental value and assigning a control group.

Answers: Given what we see, which revenue action is worth taking for this customer, and what is it worth?

## Side by side

|  | Amplitude | Markin, the decision + execution layer |
| --- | --- | --- |
| Question it answers | What is happening in the product, and which feature variation or item performs? | Given what we see, which revenue action is worth taking for this customer, and what is it worth? |
| Primary input | Instrumented events, user properties, experiment variants, predictive goals. | Behaviour and cohorts from analytics, outcomes, margins, costs, contact history, constraints. |
| Primary output | Behavioural analysis, cohorts, experiment significance, ranked recommendations. | A ranked, sized decision per customer,  including hold, pushed into the channels that can act on it. |
| Usual owner | Product, analytics and experimentation. | Growth, data science and revenue leadership. |
| How it's measured | Significance on the experiment metric; recommendation take rate and relevance. | Incremental revenue and ARPU against a holdout. |

## What stays unsolved when analytics is running well

A mature Amplitude setup shows exactly what is happening and tests features well. Insight does not decide itself: turning 'users who do X churn more' into 'who to save, with what, at what cost, and who to leave alone' is a separate job, and it is normally done in a planning meeting and a spreadsheet.

- A cohort describes a population; it does not attach a revenue value, a margin or a cost per intervention to each customer in it.
- An experiment proves a feature works on a metric; it does not decide who should receive the rolled-out feature, or who should be held.
- Recommendations maximise a predicted engagement goal, not incremental revenue net of contact cost, so the highest-relevance item is not always the highest-value action.
- Holdout groups measure the program's combined lift, not the incremental revenue of each commercial decision as it is made.

## How the two run together

1. **Context in.** Markin reads behavioural context where it already lives,  Amplitude events and cohorts, warehouse, CDP, billing, plus outcome history. Observation becomes one of the inputs to the decision, not the output.
2. **Decision.** Markin generates and sizes revenue opportunities, ranks them per customer, chooses a treatment and assigns a control group. The output is one decision per customer, with an expected value attached.
3. **Activation into channels.** The chosen decision is written back as user attributes or triggered events, so engagement and lifecycle channels act on it. Where a feature needs validation, the decision can feed an Amplitude Experiment as targeting.

## Amplitude's own decisioning layer

Products: Amplitude Analytics, Amplitude Experiment, Recommendations (Activation), Personalization, Holdout groups

Amplitude is product analytics with an experimentation and recommendations layer. It observes behaviour, builds cohorts, runs feature experiments and can recommend content toward a predictive goal. It does not size the revenue opportunity behind the behaviour or decide cross-channel actions in margin and cost terms.

**What it optimises**

- Amplitude is a product analytics platform that turns instrumented events into behavioural insight, funnels, retention, cohorts and user journeys. [Vendor docs: Amplitude docs, Recommendations (Activation)](https://amplitude.com/docs/data/audiences/recommendations)
- Amplitude Experiment runs feature experiments using a sequential testing method that keeps results valid whenever they are viewed and ends experiments early, on average, with fewer observations. [Vendor docs: Amplitude docs, Sequential testing for statistical inference](https://amplitude.com/docs/feature-experiment/under-the-hood/experiment-sequential-testing)
- Recommendations (Activation) use AutoML to determine which items are most likely to maximise each user's predicted goal, for use in personalisation campaigns. [Vendor docs: Amplitude docs, Recommendations: help users reach their goals](https://amplitude.com/docs/data/audiences/recommendations)

**Documented constraints**

- Amplitude's holdout groups measure the long-term, combined lift of an experimentation program as a whole, not the incremental revenue of a per-customer commercial decision across channels. [Vendor docs: Amplitude docs, Holdout groups](https://amplitude.com/docs/feature-experiment/advanced-techniques/holdout-groups-exclude-users)
- The optimisation unit is an experiment metric or a content recommendation toward a predictive goal. Revenue at stake, margin and contact cost per customer are not part of the recommendation decision. [Vendor docs: Amplitude docs, Build a recommendation](https://amplitude.com/docs/data/audiences/recommendations-build)

**Evidence**

- Amplitude's documented performance material concerns experiment significance and recommendation relevance. No independent benchmark of Amplitude's impact on incremental revenue is published. [Vendor docs: Amplitude docs, Analyze your experiment data with the T-test](https://amplitude.com/docs/feature-experiment/experiment-theory/analyze-with-t-test)

**Where Markin differs**

- **Observes and tests; Markin decides the commercial action.** Amplitude answers 'what is happening, and which feature variation wins'. Markin answers 'which revenue action is worth taking for this customer, and what is it worth', turning observation into a decision.
- **From a metric to a revenue decision.** A recommendation maximises a predicted goal; a Markin decision maximises expected incremental revenue net of margin and contact cost, which is why hold is a legitimate output.
- **Per-customer decision across the estate.** Amplitude cohorts and experiments group users. Markin produces one sized decision per customer,  across every channel, and reports it against a holdout, not an experiment metric.

## What Markin does not replace

To be explicit about scope, because procurement will ask:

- Markin does not replace Amplitude Analytics, dashboards, funnels or cohort building.
- Markin does not run feature experiments or replace Amplitude Experiment's stats engine or holdout groups.
- Markin is not a content recommendation engine; it decides commercial actions in revenue terms.
- Markin does not manage event instrumentation or data governance.

## Where Markin fits

Markin does not replace analytics or experiments. It turns what Amplitude observes into per-customer revenue decisions with a holdout attached, and acts in the channels that can move revenue, including customers a feature experiment never reaches.

- **Insight becomes a decision, not a deck.** A behavioural finding is converted into a sized opportunity and a chosen treatment per customer, so the insight leaves the meeting and reaches the customer.
- **Recommendations are ranked by revenue, not relevance.** Treatments are selected on expected incremental revenue net of margin and cost, so the highest-value action wins the contact even when it is not the most relevant item.
- **Measured against holdout, by default.** Every decision carries a control group and reports incremental ARPU, rather than a significance read on a feature metric or a program-level lift.

Next: [Customer Decisioning](https://markin.ai/solutions/customer-decisioning)

## When Markin is not the right answer

- You cannot connect a behavioural signal to a revenue outcome, so neither analytics nor decisions can be valued.
- Your only actions are in-product feature rollouts with no commercial treatment or channel to act in.
- Your base is small enough that the team can reason about every cohort by hand.

## FAQ

**Doesn't Amplitude already have recommendations and experiments?**

Yes. Amplitude Experiment runs feature experiments with sequential testing, and Recommendations uses AutoML to suggest items that maximise a predicted goal. Both optimise a metric or an engagement goal inside Amplitude. They do not size the revenue at stake per customer, choose a cross-channel treatment in margin-and-cost terms, or decide who to hold.

**Do we have to replace Amplitude?**

No. Amplitude remains the analytics and experimentation layer. Markin reads the signals Amplitude produces, turns them into sized revenue decisions, and hands them to the channels that can act on them.

**How does the decision leave Amplitude?**

As user attributes or triggered events on the profile, so engagement and lifecycle channels can key off them. Where a feature needs validation, the decision can also feed an Amplitude Experiment as targeting.

**What is the difference between an Amplitude holdout and a Markin holdout?**

An Amplitude holdout group measures the long-term combined lift of your experimentation program as a whole. A Markin holdout is attached to each commercial decision, so the reported number is the incremental revenue of that specific action, not a program-level average.

**Who owns the output, product or growth?**

Both. Product and analytics own the insight and the experiments; growth and data science own the revenue decision. The decision layer is the shared contract between them.

**What does the first ninety days look like?**

One revenue theme, one channel, a real holdout. The point of the first quarter is a defensible incremental number, not full coverage of every cohort.

**How is Markin different from the decisioning or AI already inside Amplitude?**

A decisioning engine ranks actions a human already defined, inside the campaign surface it was given. Markin forms the hypotheses itself,  marketing, product, pricing or a technical anomaly holding growth back, sizes them, executes them inside Amplitude and your product surfaces, and reads each one against a randomised holdout. It behaves like a data science and growth team, not like an optimiser.

**Does Markin only test messages and offers?**

No. Anything a human growth scientist would investigate is in scope: onboarding friction, feature adoption, pricing and packaging, dunning, and technical health issues such as a checkout error rate or a broken deeplink quietly killing conversion. Marketing is one of four hypothesis domains, not the boundary.

**What is the business case for adding Markin on top of Amplitude?**

On the assumptions preloaded above, 2.5M customers at 16 a month, a small move in ARPU is a large number in absolute terms, because it applies to the whole installed base every month rather than to a campaign. Across Markin deployments the verified range on treated cohorts is +17% to +35% ARPU against a randomised holdout. The point is not more messages: it is finding the highest-value action per customer, launching it, and proving it against control before it scales.

**How long before it pays for itself?**

First sized opportunities are in test within six weeks and the first holdout-verified result lands inside 90 days. Payback depends on your base, margin and programme cost, the calculator on this page computes it from your own numbers, after applying the 20% to 40% haircut BCG finds when next-best-action programmes are incrementality-tested.

Source: https://markin.ai/compare/markin-and-amplitude