---
title: Markin vs Amplitude: analytics that explain vs a team that acts
url: https://markin.ai/compare/markin-vs-amplitude
kind: vs
description: Amplitude explains what happened in the product. Markin forms hypotheses on why revenue per customer is stuck, acts, and proves the result.
updated: 2026-08-20
---

# Markin vs Amplitude: analytics that explain vs a team that acts

> Amplitude is product analytics: cohorts, funnels, retention curves and experiment readouts that explain behaviour. Markin is the team that acts on that explanation. It generates and sizes revenue hypotheses, chooses an action per customer, launches it through your stack, and measures incremental ARPU against a control group.

## In short

- Amplitude: What are users doing, and where does the funnel break? Output: Charts, cohorts, experiment readouts and audience syncs.
- Markin: Which opportunity is worth acting on, for whom, and what did it return? Output: A ranked, sized action per customer, executed through your stack.
- Findings are not sized in revenue, so prioritisation is argued rather than calculated.
- Markin reads the same evidence, proposes what to do about it, sizes it, runs it against a holdout and retires what does not work.

## The short answer

Amplitude answers questions an analyst asks. Markin asks the questions itself, thousands of them, keeps the ones worth money, acts, and reports what the action returned. They are complementary: analytics without action is a report, action without analytics is guesswork.

## The two options

**Amplitude**, Product analytics with cohorting, funnels, retention analysis, session replay and experimentation, built to explain behaviour inside the product.

Choose it when teams need to understand what users do and why a funnel behaves the way it does.

- Explains behaviour
- Self-serve analysis
- Product and growth teams

**Markin**, An autonomous growth-science team that turns findings into sized hypotheses, running actions and verified incremental revenue.

Choose it when the analysis backlog is longer than the team, and insights are not converting into tested actions.

- Generates hypotheses
- Acts per customer
- Reports incremental ARPU

## Line by line

| Dimension | Markin | Amplitude |
| --- | --- | --- |
| What it is | An autonomous growth-science team: it investigates why revenue per customer is stuck and acts on what it finds. | A product analytics platform with experimentation and audience features. |
| What it decides | Which commercial opportunity deserves to exist for each customer this week, what it is worth, and when the right answer is to do nothing. | Nothing on its own. It informs the humans who decide. |
| Where hypotheses come from | Generated by Markin from customer, product, pricing and technical-health data, then sized before anyone builds anything. | Formed by analysts and PMs from charts they choose to build. |
| Scope of action | Marketing, product, pricing and technical-health hypotheses, arbitrated against each other in one queue. | Product behaviour, funnels, retention and in-product experiments. |
| How the work reaches the customer | Written back into the systems you already run, as attributes, events or API calls. Markin does not add a new customer-facing surface. | Audience syncs to downstream tools; the action itself belongs elsewhere. |
| How impact is proven | A randomised holdout on every decision. The reported number is incremental revenue and ARPU, not attributed conversions. | Strong experiment analysis when a team designs and runs the experiment. |
| Where the data sits | Reads context where it already lives, warehouse, CDP, product and billing systems. No new system of record. | Event streams from product and web, plus warehouse sync. |
| Governance and control | Every action carries its hypothesis, its expected value, its guardrails and its control group, reviewable before launch. | Analysis governance, event taxonomy and access controls. |
| Time to a verified number | One revenue theme, one channel, one holdout: a defensible incremental number inside 90 days. | Fast to insight; time to verified revenue depends entirely on the team downstream. |
| Best fit | Large B2C bases where the constraint is how many good hypotheses get tested, not how many messages get sent. | Product and growth teams that need to understand behaviour. |

## What each layer is

**Amplitude**, Product analytics: cohorts, funnels, retention, replay and experimentation over product event data.

Answers: What are users doing, and where does the funnel break?

**Markin**, A growth-science layer that generates, sizes, launches and proves revenue hypotheses per customer.

Answers: Which opportunity is worth acting on, for whom, and what did it return?

## Side by side

|  | Amplitude | Markin |
| --- | --- | --- |
| Question it answers | What are users doing, and where does the funnel break? | Which opportunity is worth acting on, for whom, and what did it return? |
| Primary input | Product and web event streams, user properties. | Warehouse, product, billing and contact history; margins and constraints. |
| Primary output | Charts, cohorts, experiment readouts and audience syncs. | A ranked, sized action per customer, executed through your stack. |
| Usual owner | Product, analytics and growth teams. | Growth, data science and revenue leadership. |
| How it's measured | Engagement, conversion and retention metrics. | Incremental revenue and ARPU against a randomised holdout. |

## What Amplitude does better

- **Exploratory analysis is a different craft.** When a human needs to interrogate a funnel, slice a cohort five ways and see the session replay, Amplitude is excellent and Markin is not a substitute. Markin proposes; people still need somewhere to look.
- **Event taxonomy and instrumentation.** Amplitude has spent a decade on the boring, essential work of tracking, taxonomy and governance. That layer is a prerequisite for anything Markin does with product data.
- **Product teams live there.** Adoption matters. If your PMs already reason in Amplitude, keeping them there and letting Markin work behind it is better than trying to move them.

## Which one to pick

**Choose Markin if**

- Insights pile up faster than they get tested.
- You need money attached to a finding before anyone prioritises it.
- Actions have to reach customers, not just dashboards.
- Incremental ARPU against a holdout is the number the board wants.
- Causes of flat revenue can be pricing or technical, not only product behaviour.

**Choose Amplitude alone if**

- The gap is understanding, not execution.
- You need self-serve exploration for a wide internal audience.
- Instrumentation and taxonomy are the current priority.
- Your experiments are already designed and read by a strong team.
- The product is early and the base is small.

## The distance between a chart and a euro

Analytics ends where the decision starts. Someone still has to notice the chart, believe it, size it, design a treatment, get it built, run it against a control and decide whether to keep it. That chain is where most insight dies.

- Findings are not sized in revenue, so prioritisation is argued rather than calculated.
- Nothing arbitrates between a product fix, a price change and a campaign.
- Experiments happen when someone has capacity to design them.
- There is no per-customer decision, only cohorts.

## Where Markin fits

Amplitude keeps explaining behaviour. Markin reads the same evidence, proposes what to do about it, sizes it, runs it against a holdout and retires what does not work.

- **Insight becomes action.** Each hypothesis carries an owner, a treatment and a control group.
- **Sized before built.** Expected value decides the queue, not conviction.
- **Closed loop.** Results feed back into the next round of hypotheses.

Next: [Product Diagnosis](https://markin.ai/solutions/product-diagnosis)

## When Markin is not the right answer

- You need self-serve exploratory analytics: that is Amplitude's job, not Markin's.
- Product instrumentation does not exist yet.
- The base is too small for holdouts to reach significance.

## FAQ

**Is Markin an Amplitude alternative?**

No. Amplitude explains behaviour; Markin decides and acts. Most customers keep Amplitude for exploration and use Markin to convert findings into sized, tested, measured actions.

**Amplitude has experiments. Why is that not enough?**

Experimentation tools run the experiments a team designs. The constraint is usually how many good experiments get designed and sized, which is the part Markin automates.

**What does Amplitude do better?**

Exploratory analysis, event taxonomy and instrumentation governance, session replay, and giving a wide internal audience self-serve access to product data.

**Does Markin need Amplitude data?**

It helps but it is not required. Markin reads product and revenue context from the warehouse; Amplitude events are one useful source among several.

**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 a large B2C base, 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-vs-amplitude