COMPARE/Markin and your stack
+Markin + MoEngage: revenue decisions before the push
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
MoEngage is an insights-led engagement platform: analytics, segmentation and cross-channel orchestration across push, in-app, email and SMS, with Sherpa AI optimising content and timing. Markin sits before it, deciding which revenue opportunity is worth acting on per customer and what it is worth. MoEngage still delivers.
In short
- MoEngage: How do we reach this segment across mobile channels, with which message and when? Output: Delivered messages, flow state and engagement analytics.
- Markin, the decision + execution layer: Which opportunity justifies contact for this customer, and what is it worth? Output: A ranked, sized decision per customer, including hold, written into MoEngage.
- Content and send-time optimisation improve a message that someone already decided to send.
- It decides which of the many things you could send is worth sending to each customer, and hands that decision to MoEngage to deliver through the flows you already maintain.
Last updated: . Claims about other vendors link to the source they come from.
What is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
6.0M
customers at $9 ARPU / month
Addressable revenue
$440.6M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$74.9M – $154.2M
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 numbersWhat your stack does today.
01
MoEngage
A customer engagement platform combining behavioural analytics, segmentation and cross-channel orchestration across push, in-app, email, SMS and web, with AI-assisted content and timing.
02
Markin, the decision + execution layer
A layer that generates and sizes revenue opportunities per customer, ranks them on expected incremental margin, selects the treatment and decides when to stay silent.
Side by side
The differences that change outcomes.
| Dimension | MoEngage | Markin, the decision + execution layer |
|---|---|---|
| Question it answers | How do we reach this segment across mobile channels, with which message and when? | Which opportunity justifies contact for this customer, and what is it worth? |
| Primary input | Events and user attributes, segments, campaign and flow definitions, content. | Customer context, outcomes, margins, contact history, constraints, past experiment results. |
| Primary output | Delivered messages, flow state and engagement analytics. | A ranked, sized decision per customer, including hold, written into MoEngage. |
| Usual owner | Lifecycle, CRM and mobile growth marketing. | Growth, data science and revenue leadership. |
| How it's measured | Open, click and conversion rates, campaign-level uplift. | Incremental revenue and ARPU against a randomised holdout. |
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 MoEngage 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. |
The unsolved part
What stays unsolved when MoEngage is running well
Mobile channels are cheap, so programmes tend to expand until the base is saturated. Cheap contact hides the real cost: opt-outs, fatigue and revenue that would have arrived anyway being counted as a win.
- Content and send-time optimisation improve a message that someone already decided to send.
- Nothing prices the contact: a push worth two cents in margin still occupies the only slot a customer will tolerate today.
- Concurrent flows are resolved by caps and eligibility rules rather than by expected value.
- Campaign reporting counts conversions after a send; it does not separate incremental revenue from demand that existed anyway.
The actual difference
Markin is not another decisioning engine.
Markin is not a decisioning engine. A decisioning engine ranks actions a human already defined. Markin works like a data science and growth team: it forms its own hypotheses about why ARPU is stuck, marketing, product, pricing or technical, sizes them, executes them inside the systems you already run, and reads each one against a holdout.
| A decisioning engine | Markin | |
|---|---|---|
| Where the hypothesis comes from | A human authors it. The engine chooses between options someone already approved. | Markin authors it. It reads the base, finds where revenue is leaking or unclaimed, and writes the hypothesis itself. |
| What it is allowed to question | Message, offer, channel, timing, inside the campaign surface it was given. | Anything that moves ARPU: onboarding friction, pricing and packaging, a feature nobody adopts, a payment failure spike, a broken deeplink. |
| Who does the analysis | Your analysts, before and after. The engine optimises; it does not investigate. | Markin does the analysis. Sizing, segment definition, experiment design and readout are automated end to end. |
| Where it stops | At the recommendation. Someone still has to build and launch it. | It launches. Markin executes inside your existing platforms and product surfaces, then closes the loop on the result. |
| Throughput | As many hypotheses as your roadmap has room for, typically a handful per quarter. | Hundreds in parallel, every one carrying a control group. |
| What happens when it is wrong | The programme keeps running until someone reviews it. | It is retired automatically. Failing to beat control is a normal, cheap outcome. |
A decisioning engine picks the best action from a list you wrote. Markin writes the list, and runs it in your stack.
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.
Their decisioning layer
What MoEngage decides, and where it stops.
MoEngage pairs analytics with cross-channel orchestration and an AI layer, Sherpa, that optimises message content, timing and recommendations. The objective it is given is engagement and conversion inside MoEngage campaigns, not incremental margin across the customer base.
Products referenced: Sherpa AI, Smart Recommendations, Sherpa Content Optimization
What it optimises
Sherpa AI is presented as a combination of predictive and generative AI that maximises campaign engagement by predicting and sending the right message at the right time.
Vendor pageMoEngage, AI capabilities pageMoEngage documents recommendation use cases as combinations of its own Recommendation and Web Personalization features, used to raise average cart size inside MoEngage campaigns.
Vendor docsMoEngage Help, Recommend products to increase cart size
Documented boundaries
The stated objective is campaign engagement and higher conversions, not incremental revenue net of margin and contact cost, so suppressing contact is not an outcome the optimiser is rewarded for.
Vendor pageMoEngage, AI capabilities pageOptimisation and personalisation operate over MoEngage's own channels and campaign objects, so touchpoints MoEngage does not deliver are outside the decision.
Vendor docsMoEngage Help, Recommendations and Web Personalization
What the evidence actually says
MoEngage was named a Strong Performer in The Forrester Wave™: Cross-Channel Marketing Hubs, Q1 2023, an independent evaluation of cross-channel orchestration rather than of revenue decisioning.
Analyst reportForrester Wave™, Cross-Channel Marketing Hubs Q1 2023MoEngage has also been placed by Gartner in the Magic Quadrant™ for Multichannel Marketing Hubs, again a category defined by orchestration and delivery rather than by incremental revenue.
Analyst reportMoEngage, Gartner Magic Quadrant™ for Multichannel Marketing Hubs
Where Markin is different.
Optimises revenue, not engagement
Sherpa maximises the chance a message works. Markin decides whether the message is worth sending at all, ranked on expected incremental margin, with hold as a legitimate output.
Hypotheses beyond the campaign surface
Onboarding friction, plan and pricing changes, and technical health issues are in scope for Markin, not only content, timing and recommendations.
Holdout-verified ARPU
Every decision carries a control group, so growth is reported as incremental ARPU rather than as conversion attributed to a campaign.
Architecture
How the two run together
Context in
Markin reads context from the warehouse, billing and product systems, plus MoEngage engagement and delivery history.
Decision
Opportunities are generated and sized, ranked per customer on expected incremental margin, a treatment is chosen and a control group assigned.
Activation back into MoEngage
The decision is written as a user attribute or a custom event, so existing flows and campaigns pick it up. Channel caps, quiet hours and consent stay in MoEngage.
The last step is execution, not a hand-off. Markin does not email a recommendation to someone who then has to build it: it launches the treatment inside MoEngage and your product surfaces directly, with the holdout attached, and reads the result itself.
The loop
Execution is a step in the loop, not a hand-off.
- 01
Observe
Markin reads the behavioural, transactional and product signal you already collect, continuously.
- 02
Hypothesise
It writes the hypothesis itself, marketing, product, commercial or technical, and states the expected direction.
- 03
Size
Each opportunity is ranked by expected value, so the queue is ordered by money rather than by opinion.
- 04
Design
Segment, treatment, guardrails and a randomised holdout are set before anything ships.
- 05
Execute
It launches inside the systems you already run, your engagement platform, your product surfaces, your APIs. Nothing waits on a build queue.
- 06
Read
Results are measured against the holdout over a full window, so novelty is not mistaken for effect.
- 07
Scale or retire
What beats control is scaled across the base. What does not is switched off automatically.
Where Markin fits
Not a replacement. A growth-science team on top.
Markin does not send push notifications. It decides which of the many things you could send is worth sending to each customer, and hands that decision to MoEngage to deliver through the flows you already maintain.
Silence becomes a decision you can measure
Hold is ranked against every treatment, so fatigue and opt-out cost enter the economics instead of being discovered later.
One decision per customer, per moment
Concurrent flows stop competing: the highest expected-value action wins the slot, whatever team built it.
Read on revenue, not opens
Every decision carries a holdout, so the number that reaches the board is incremental ARPU.
Why Markin wins
More powerful than anything on this page.
Every tool Markin is compared against was built for a job that stops before revenue moves: deliver the message, unify the data, score the lead. Markin was built for one outcome, growing ARPU, and it owns the full loop that gets there: investigate, hypothesise, launch, measure and scale, across marketing, product, pricing and technical health.
Ultra-fast learning, by design
Markin Growth Science runs the full cycle of observe, hypothesise, experiment and read in days, with hundreds of holdout-backed experiments in parallel. The system compounds learning at a pace no human team, and no campaign tool, can match.
Growth operations, made efficient
Sizing, segment design, build, launch and measurement used to be four teams and a sprint. In Markin they are one system, so the same growth operation ships more revenue action with a fraction of the coordination cost.
One outcome: ARPU
Every hypothesis is sized in expected revenue per customer, every action is judged against a randomised holdout, and everything that beats control scales across the base automatically. Nothing else on this page is measured that way.
If the goal is to grow ARPU through ultra-fast learning and run growth operations more efficiently, the choice is Markin.
Operating model
The constraint is not ideas. It is how many you can test.
| Today, with MoEngage | With Markin on top | |
|---|---|---|
| Revenue hypotheses tested per quarter | 4 to 8, whatever the roadmap had room for | Hundreds, generated and run in parallel |
| What can be hypothesised about | Messages, offers and audiences, the campaign surface | Marketing, product, pricing and technical health alike |
| From decision to live in the channel | A ticket, a build queue, a release window | Markin launches it in your existing platforms itself |
| Time from idea to a result you trust | 6 to 10 weeks of analysis, build and readout | Days, because sizing and design are automated |
| Share of decisions with a control group | The flagship programmes, when there is time | Every decision, by default |
| Coverage of the base | Top segments and the customers a rule caught | One decision per customer, across the whole base |
| Cost of testing the 500th hypothesis | Another analyst, another quarter | Effectively zero |
| What the team spends its time on | Pulling data, building lists, reconciling reports | Judgement: constraints, economics, what to scale |
Markin does not replace your data science team. It removes the ceiling on how much of the base that team can act on, and how fast it finds out whether it worked.
What Markin does not replace.
To be explicit about scope, because procurement will ask:
- Markin does not replace MoEngage orchestration, push infrastructure or channel delivery.
- Markin does not own consent, frequency caps or quiet hours.
- Markin does not replace product analytics or event instrumentation.
- Markin does not take over campaign content or creative production.
- Markin does not sit beside MoEngage making suggestions. It drives it, the action is launched there, in the system your team already knows, and the result comes back into the loop.
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.
Size it yourself
Size the decision layer on top of your MoEngage programme
Preloaded for a mobile-first consumer business running MoEngage at scale: very high reach, cheap channels, frequent contact. When contact is cheap, the expensive mistake is contacting everyone. The figure below is the incremental margin from deciding what each customer should receive, and who should be left alone.
Your base
Accounts that generated revenue in the last 30 days. Not registered users.
Recurring plus non-recurring revenue divided by active customers.
Margin on the next unit sold, not blended company margin.
Your programme today
Consented, non-fatigued, reachable on at least one channel.
Revenue lost to cancellations each month, as a share of the base.
The bet
Licences, data, incentives and the people running it.
Before any incrementality haircut. 2–4% is a defensible planning assumption.
Verified annual impact
$6.2M
Net incremental gross margin in the central case, after the programme cost and after the share of decisioning programmes that independent research finds deliver no real lift.
Reported uplift
$14.1M
What a before/after dashboard would claim, with no control group.
Verified uplift
$9.9M
What survives a holdout in the central case.
Return on programme cost
7.9×
Payback
2 mo
If 20–40% of it does nothing
What it takes to prove it
To detect a 3.2% lift on revenue per customer you need roughly 35K customers in the control arm, about 0.9% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.
Addressable base
4.1M
Revenue at risk from churn
$251.0M
Annualised, at the current monthly rate.
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, MoEngage 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.
Top alternatives to MoEngage
Most MoEngage alternatives searches come from teams whose messaging works but whose revenue learning is slow. It is worth separating the platforms that replace MoEngage from the layer that sits above it.
| Alternative | What it is | Best for | Watch out |
|---|---|---|---|
| Braze | Cross-channel engagement platform with Canvas journeys and its own decisioning features. | Global consumer apps that need deep channel coverage and a mature partner ecosystem. | Optimisation happens inside journeys you still have to design. |
| Iterable | Lifecycle marketing platform with the Nova intelligence layer. | Marketing-owned lifecycle programmes in retail, streaming and travel. | AI outputs are scoped to Iterable's own segments and journeys. |
| Optimove | CRM marketing platform built around customer-led journey orchestration. | Gaming, betting and retail teams with heavy segmentation and campaign volume. | Strong at arbitration between campaigns, still bounded by the campaigns you define. |
| Airship | Mobile-first engagement and app experience platform. | Teams whose growth problem is app onboarding, push and in-app surfaces. | Narrower scope outside the app. |
| Markin | Agentic growth layer that generates, prices and tests revenue hypotheses above your existing stack. | Enterprise B2C teams that want more verified experiments per month and higher ARPU. | It decides and measures; MoEngage or an equivalent still delivers the message. |
When you don’t need Markin.
- You cannot connect engagement to a revenue outcome, so decisions cannot be valued.
- Your programme is a handful of transactional messages with no commercial choice to make.
- You want an engagement platform rather than a decision layer on top of one.
See it in the product
Watch it decide, experiment and execute, before you talk to anyone.
A guided tour of the Markin workspace on a live demo customer, no sales call, no setup.
Questions buyers ask.
Doesn't Sherpa AI already optimise our campaigns?
Sherpa optimises the content, timing and recommendations of campaigns your team defined, against an engagement objective. Markin decides which commercial opportunity deserves a campaign at all, sizes it in margin terms, arbitrates it against everything else the customer could receive, and reads it against a randomised holdout.
Do we have to replace MoEngage?
No. MoEngage stays the analytics and delivery layer. Markin supplies the decision that its flows execute.
How does the decision reach MoEngage?
As a user attribute carrying the chosen action and its expiry, or as a custom event that triggers an existing flow.
Does this mean sending fewer messages?
Often yes, at least at first. Where a contact has no positive expected value, the decision is to hold. Volume is not the objective; incremental ARPU is.
What does the first ninety days look like?
One revenue theme, one activation route into MoEngage, a real holdout, and a number that survives a full measurement window.
How is Markin different from the decisioning or AI already inside MoEngage?
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 MoEngage 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 MoEngage?
On the assumptions preloaded above, 6.0M customers at 9 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.
Read next
Background reading on how next best action decisioning works, from the guides behind this comparison.
Next best action marketing: what it is and how it works in 2026
Next best action marketing explained: how it works, real B2C examples, how it differs from segment campaigns and marketing automation, and what it is worth on a 2M base.
How to increase ARPU: a framework for B2C enterprises
How to increase ARPU in a large B2C business: the five levers that actually move average revenue per user, and the operating model behind them.
Opportunity Feed, now with hypothesis provenance
Every candidate action in the Markin Opportunity Feed now carries the signal, segment and prior experiment it descends from. One click to audit or ship.