COMPARE/Markin and your stack
Markin + Segment or Tealium: turning customer context into revenue decisions
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
Segment and Tealium collect events, resolve identity, govern consent and route customer data to downstream tools. Markin sits above that plumbing and decides which commercial opportunity is worth acting on for each customer. Collection, consent and routing stay exactly where they are.
What is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
3.0M
customers at $21 ARPU / month
Addressable revenue
$378.0M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$64.3M – $132.3M
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
Segment or Tealium
Customer data infrastructure: event collection across web, mobile and server, identity resolution into unified profiles, consent and tag governance, and routing of that data to downstream destinations.
02
Markin, the decision + execution layer
A layer that consumes that governed context and produces ranked, sized commercial decisions per customer, each with a treatment, a control group and a measured incremental result.
Side by side
The differences that change outcomes.
| Dimension | Segment or Tealium | Markin, the decision + execution layer |
|---|---|---|
| Question it answers | Is this data collected, resolved, compliant and delivered to the right tools? | Given everything we now know, which opportunity is worth acting on and how much is it worth? |
| Primary input | Tracking calls, source system records, consent state, destination configuration. | Governed customer context, outcome history, margin, contact costs and constraints. |
| Primary output | Unified profiles and traits, governed event streams, audiences in destinations. | A ranked decision per customer, including the decision not to act. |
| Usual owner | Data engineering and martech operations. | Growth, data science and revenue leadership. |
| How it's measured | Event delivery, identity match rate, consent compliance, destination uptime. | Incremental revenue and ARPU against a holdout. |
The unsolved part
What good data infrastructure still leaves open
Segment and Tealium answer whether the data is trustworthy, compliant and where it needs to be. That is a hard problem and worth solving properly. It is a different problem from deciding what the data is worth acting on.
- A trait is a fact about a customer. It carries no estimate of the revenue at stake or of what an intervention would change.
- Audiences are rules; a customer can satisfy many at once and nothing arbitrates between them commercially.
- Consent tells you what you may do, not what you should do.
- Once the pipes are reliable, the constraint becomes how many well-designed interventions the team can conceive and test per quarter.
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 Segment and Tealium decides — and where it stops.
Both platforms ship machine learning, and both apply it to the same job: scoring how likely a customer is to do something. That score then feeds a rule-based journey. A propensity score is an input to a decision; it is not a decision, and neither platform claims to arbitrate between competing opportunities.
Products referenced: Segment Predictions, Twilio CustomerAI, Twilio Engage; Tealium Predict ML™, AudienceStream
What it optimises
Segment Predictions lets you predict the likelihood that users will perform any event tracked in Segment, stored as computed traits on the user profile.
Vendor docsTwilio Segment docs, PredictionsTwilio positions Predictions as a way to uncover behavioural patterns and surface high-value audiences most likely to convert without calling in a data science team.
Vendor pageTwilio, Predictions product pageTealium Predict ML™ predicts the likelihood of customers achieving a defined goal and uses that prediction to define audience segments or engagement rules.
Vendor pageTealium, Predict ML product page
Documented boundaries
Segment Predictions is gated to the Business tier with the Unify Plus add-on, so propensity scoring is not available on lower plans.
Vendor docsTwilio Segment docs, Using PredictionsScores are computed traits. Execution logic still lives in Engage's rule-based journey builder rather than in a learning arbitration engine.
Vendor docsTwilio Segment docs, PredictionsTealium Predict ML is tied to AudienceStream and is described as business-friendly, per-goal modelling used for segmentation, not cross-channel offer arbitration.
Vendor pageTealium, Predict ML product page
What the evidence actually says
Neither Twilio Segment nor Tealium publishes an uplift figure for its predictive layer on its public product pages.
Vendor pageTwilio, Predictions product pageTwilio CustomerAI, launched August 2023, bundles Predictions with generative and voice capabilities across Engage, Flex and Segment as an AI-ready CDP, a data-and-scoring positioning rather than a decisioning one.
Vendor pageTwilio, CustomerAI launch release
Where Markin is different.
A score ranks people, a decision ranks options
Propensity tells you who is likely to churn. It does not tell you which of nine possible interventions is worth the margin, or whether the customer would have stayed anyway. Markin ranks options per customer, not customers per model.
Consumes the scores you already have
Predictions and Predict ML traits are valid inputs to Markin. Nothing gets rebuilt; the scores stop being the end of the pipeline and become one feature in the decision.
Incrementality, not likelihood
Acting on high propensity often means paying customers to do what they were going to do. Markin optimises the uplift a treatment causes, measured against control.
Architecture
How the layers run together
Collection and governance
Segment or Tealium keeps doing what it does: collecting events, resolving identity, enforcing consent and routing data, including into the warehouse Markin reads.
Decision
Markin generates and sizes revenue opportunities from that governed context, ranks them per customer, chooses a treatment and assigns a control group. Consent state is a hard constraint on what is eligible.
Activation through existing destinations
The decision is written back as a trait or event so your existing destinations, engagement platform, ads, product, support, activate it through the routes already configured.
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 Segment and Tealium 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 is not customer data infrastructure and will not ask you to re-instrument anything. It reads the context your CDP already produces, decides what is commercially worth doing with it, and returns that decision through the same routing layer.
Consent is a constraint, not an afterthought
Eligibility inherits the consent and preference state your platform governs. A decision that cannot be acted on compliantly is never made.
No new identity graph
Identity resolution stays where it is. Markin joins on the identifiers your platform already resolves.
From traits to expected value
The output is not another trait. It is a sized opportunity with a recommended treatment and a control group attached.
Operating model
The constraint is not ideas. It is how many you can test.
| Today, with Segment and Tealium | 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 collect events or instrument your web and mobile apps.
- Markin does not perform identity resolution or maintain the profile store.
- Markin is not a consent, preference or tag management system.
- Markin does not route data to destinations; your CDP keeps that job.
- Markin does not sit beside Segment and Tealium 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
What a propensity score is worth once something decides on it
Preloaded for a base with scoring already in place through Segment Predictions or Tealium Predict ML. Scores rank people; they do not rank the competing actions, price them against margin, or hold anything back to prove the effect. The figure below is the margin that turning those scores into ranked, sized decisions can defend against control.
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
$3.7M
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
$11.3M
What a before/after dashboard would claim, with no control group.
Verified uplift
$7.9M
What survives a holdout in the central case.
Return on programme cost
4.3×
Payback
3 mo
If 20–40% of it does nothing
What it takes to prove it
To detect a 3% lift on revenue per customer you need roughly 40K customers in the control arm, about 2.7% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.
Addressable base
1.5M
Revenue at risk from churn
$204.9M
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, Segment and Tealium 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.
When you don’t need Markin.
- Collection and identity are still unreliable: a decision layer cannot compensate for missing context.
- You need consent management, tag governance or a data pipeline, that is what these platforms are for.
- Your commercial model has no repeat purchase, upgrade or retention decision to prioritise.
Questions buyers ask.
Isn't a propensity score from Segment or Tealium the same as a decision?
No. Segment Predictions computes the likelihood that a user performs a tracked event, stored as a trait and gated to the Business tier with Unify Plus. Tealium Predict ML predicts goal likelihood to define audience segments or engagement rules. Both rank people; neither ranks the competing actions you could take, prices them against margin, or measures whether the treatment caused the outcome. Markin consumes those scores as inputs.
Do we have to replace Segment or Tealium?
No. They remain the collection, identity, consent and routing layer. Markin consumes that context and returns decisions through the destinations you already configured.
Is this a second CDP?
No. Markin has no profile store and no identity graph. It reads resolved context and produces ranked commercial decisions, which is a different artefact from a unified profile.
How is consent respected?
Consent and preference state are treated as hard eligibility constraints. If a customer cannot be contacted on a channel, no decision is produced for that channel.
What if our context lives in the warehouse rather than the CDP?
That works the same way. The requirement is reliable customer context and outcome history, not a particular product category.
How is Markin different from the decisioning or AI already inside Segment and Tealium?
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 Segment and Tealium 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 Segment and Tealium?
On the assumptions preloaded above, 3.0M customers at 21 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.