COMPARE/Markin vs Amplitude
vsMarkin vs Amplitude: analytics that explain vs a team that acts
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
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.
The short answerLast updated: August 2026
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.
01
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
02
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
The same ten questions, answered for both.
| 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 is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
2.0M
customers at $24 ARPU / month
Addressable revenue
$259.2M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$44.1M – $90.7M
incremental revenue per year
Measured on treated cohorts against a randomised holdout, read over a full measurement window rather than the first weeks. Anonymised range across Markin deployments in large B2C bases; your own holdout is the number that decides. The figures above apply that range to the reachable share of the base on this page's assumptions; they are arithmetic, not a forecast for your business.
Run it on your own numbersThe unsolved part
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.
Hypothesis space
Everything a human growth scientist would look at.
Most growth problems are not message problems. Markin is not restricted to the campaign surface: if something is holding ARPU back, it is in scope, and it gets tested the same way.
Marketing
The classic surface, but chosen per customer rather than per segment, and always against a holdout.
- Which offer this specific customer is worth making
- Channel and timing chosen per person, not per campaign
- Contact pressure and fatigue arbitrated across every programme
- Win-back economics: who is worth a discount and who is not
Product
Where the customer actually experiences the value, and where most silent revenue loss happens.
- Onboarding steps that lose customers before first value
- A feature with high retention correlation that half the base never discovers
- Paywall and upgrade prompt placement
- In-product surfaces used as a treatment arm, not just email and push
Commercial
Pricing, packaging and the shape of the offer itself, tested rather than argued about.
- Plan and bundle structure by cohort
- Discount depth against margin, not against conversion alone
- Annual versus monthly framing per customer
- Dunning and involuntary churn recovery sequences
Technical health
Anomalies nobody asked it to look for. This is the category no decisioning engine covers.
- A checkout error rate that rose on one device and one region
- Payment failures concentrated in a single issuer or method
- A broken deeplink quietly killing a high-value journey
- Latency or delivery degradation eating conversion before any message does
Think of Markin as a data science and growth team that never sleeps: it investigates, forms hypotheses, ships them into your own stack and proves each one against a control group, at a volume no human team can reach.
Job to be done
The same work, at a different throughput.
Nothing below needs a tool that does not exist. It needs the work to happen continuously instead of once a quarter, and to be proven against a holdout instead of argued about.
| Job to be done | With amplitude alone | With Markin |
|---|---|---|
| Notice that revenue per customer is drifting in a segment | Someone spots it in a dashboard review, weeks after it started. | Detected as a signal the day the drift clears noise, with the segment already sized. |
| Explain why it is happening | An analyst is pulled off the roadmap for a two-week investigation. | An investigation runs automatically and returns the drivers with their evidence. |
| Come up with hypotheses worth testing | A workshop produces the handful of ideas the room happened to think of. | Hypotheses are written continuously across marketing, product, pricing and technical health. |
| Decide which hypotheses deserve budget | Prioritised by seniority and gut feel, with no size attached. | Each one is sized in revenue and ranked before anything is built. |
| Choose the next best action for one customer | Segment rules and campaign calendars decide, refreshed when someone has time. | Chosen per customer, per moment, against everything else competing for that customer. |
| Actually launch it | A ticket to the lifecycle team, then a slot in next month's calendar. | Executed inside the systems you already run, with no new channel to adopt. |
| Prove it caused the revenue | Reported against non-qualifiers or a global holdout, if at all. | Every decision carries a randomised control group; uplift is read against it. |
| Kill what does not work | Programmes survive because nobody owns retiring them. | Failing to beat control retires the programme automatically. |
| Do all of it again next week | Capacity-bound: four to eight tests a quarter. | Hundreds of hypotheses in flight in parallel, continuously. |
Honest take
What Amplitude does better.
A comparison that only flatters one side is not worth reading. These are the cases where we would tell you to stay where you are.
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.
Where Markin fits
Not a replacement. A growth-science team on top.
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.
Evidence standard
Most of this category reports its own lift.
None of the major engagement, CDP or personalisation vendors publishes an independently verified uplift figure for its decisioning product. Where numbers exist, they come from vendor-commissioned studies or single-customer case studies with no disclosed holdout methodology. The most rigorous public research in the category is not flattering to anyone, including us, which is exactly why we build against it.
BCG reports that when organisations adopt rigorous incrementality testing, they typically find 20% to 40% of their active next-best-action programmes deliver marginal to negative lift.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)The same research flags novelty effects, new programmes show inflated early results, and recommends 8 to 12 weeks before drawing conclusions.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)Global-holdout, programme-level ROI measurements often overstate impact through halo effects, pull-forward effects and experiment contamination.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)
How Markin holds itself to it
- Every decision Markin makes carries a control group. Uplift is reported against that holdout, not against the customers who did not qualify.
- Results are read over a full measurement window rather than in the first weeks, so novelty is not mistaken for effect.
- Programmes that fail to beat control are retired automatically. Killing decisions that do not pay is part of the loop, not an annual review.
- The one figure we quote about ourselves is a range, not an average: +17% to +35% ARPU on treated cohorts against a randomised holdout, across Markin deployments in large B2C bases. We publish no industry benchmark, because we could not source one we would be willing to defend. Your holdout is the number that matters.
Time to value
90 days to a number that survived a holdout.
No replatform, no data migration, no rebuild of the channels you already run. If the first cohorts do not beat control, nothing scales and you have lost a quarter, not a roadmap.
Weeks 0–2
Read the context you already have
Markin connects to the data and the channels you run today, amplitude included. No migration, no replatform, no new source of truth.
Weeks 3–6
First sized opportunities in test
Opportunities are ranked by expected value, treatments are chosen per customer, and the first cohorts go live with a randomised holdout attached.
Weeks 7–12
First verified incremental revenue
Results are read over a full measurement window. What beats control scales; what does not is retired. Nothing scales on a number that has not survived a holdout.
Which one you should 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.
When you don’t need Markin.
- 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.
Questions buyers ask.
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.