RESOURCES/Guide
The operating model behind sustained ARPU growth
ARPU stalls because decision throughput is fixed, not because the ideas run out. A team can plan, build and read a handful of revenue experiments per quarter, so the base is served by a few large programmes rather than by a decision per customer. Raising throughput, not headcount, is what moves the number.
The plateau is arithmetic, not talent
Most B2C businesses reach the same ceiling. Acquisition works, the lifecycle programme works, and then ARPU flattens for six quarters while the team keeps shipping. The reason is rarely a bad idea. It is that every idea has to pass through the same narrow pipe: someone has to size it, define the segment, brief it, wait for a build slot, launch it, and wait again to read it.
- Four to eight meaningful revenue experiments per quarter is a normal ceiling for a strong team.
- Each one takes 6 to 10 weeks from idea to a number anyone trusts.
- Most of that time is not thinking. It is pulling data, building lists and reconciling reports.
- So the base gets a handful of programmes, and everything else stays untested.
What actually changes
The operating model shift is narrow and specific. Only one variable moves; everything else follows from it.
01
Move from segments to decisions
A segment is a compromise made because you cannot afford a decision per person. Once decisions are cheap, the compromise stops paying for itself. The same base, decided individually, has a materially higher ceiling than the same base split into five segments.
02
Make hold a real option
Contact economics are asymmetric: the cost of contacting a customer who was going to buy anyway is invisible in attributed reporting and very visible in a holdout. An operating model that cannot output 'do nothing' will overspend permanently.
03
Let failure be cheap
If a failed test costs a quarter, teams only test things they expect to win, which is exactly the set of hypotheses with the least information in them. When failure costs nothing, the interesting hypotheses get run.
04
Keep judgement human
Constraints, margin floors, brand and regulatory limits, and what deserves to scale are human calls. Throughput is not.
The scheduled model against the continuous one
| Dimension | Scheduled growth | Continuous growth |
|---|---|---|
| Unit of planning | The quarter, and a roadmap of programmes | The customer, and a decision per window |
| Who authors hypotheses | Analysts and growth managers, between other work | The system, continuously, ranked by expected value |
| Hypotheses tested per quarter | 4 to 8 | Hundreds, in parallel |
| What can be hypothesised about | Mostly campaigns and offers | Marketing, product, pricing and technical health alike |
| Cost of the 500th hypothesis | Another analyst, another quarter | Effectively zero |
| Share of decisions with a control group | The flagship programmes, when there is time | Every decision, by default |
| What the team spends its time on | Producing analyses | Setting guardrails and deciding what scales |
Sizing the opportunity honestly
ARPU maths is seductive because it applies to the whole base every month. That is also why it is easy to overstate.
Any projection should be haircut before it is presented: BCG finds 20% to 40% of measured next-best-action uplift does not survive a randomised control test, so the planning number should sit below the reported one.
Independent researchBCG, incrementality in personalisation programmes (2026)
Measure your own throughput this week
Five numbers. If you cannot produce them quickly, that is itself the finding.
- How many revenue hypotheses were tested last quarter, end to end, with a readout?
- What was the median time from idea to trustworthy result?
- What share of those had a randomised holdout?
- How many were about something other than a message or an offer?
- How many programmes currently running have never been checked against a control?
When this model is the wrong priority
- Product-market fit is not settled. Continuous optimisation of a proposition that is still moving wastes the measurement.
- The business runs on a handful of high-value accounts. Per-customer decisioning is built for large bases.
- Margin is negative at the unit level. Increasing revenue per user makes the problem larger, not smaller.
- Data on outcomes arrives months late. Throughput cannot exceed the speed of feedback.
Markin is an autonomous growth-science team for large B2C businesses. It investigates why revenue per customer is stuck, forms its own hypotheses across marketing, product, pricing and technical health, chooses the next best action for each customer, launches it through the systems the business already runs, and proves every one against a randomised holdout.
Decisioning tools choose between the actions your team already built. Markin decides what to build.
Questions people ask
- How do you increase ARPU without increasing churn?
- By ranking actions on expected incremental revenue net of margin and contact cost, rather than on conversion. An upsell that converts but raises churn risk has a negative expected value once retention is priced in, and it should lose to a cheaper action or to no action at all. This only works if churn effects are read in the same holdout as the revenue effect.
- Is ARPU or LTV the right target?
- ARPU is the right operating metric because it can be read monthly against a control group. LTV is the right strategic metric but it is a forecast, and forecasts are easy to move by changing assumptions. Use ARPU for decisions and LTV for planning.
- How many experiments should a large B2C business run?
- As many as the base can support statistically, which for a multi-million customer base is far more than any team can plan. The practical constraint should be statistical power and contact economics, not the number of people available to write briefs.
- What is a realistic ARPU improvement to plan for?
- Plan on a haircut version of whatever your first holdout-verified cohort produces, and refuse to plan on a number that has not survived a control group. Across Markin deployments the verified range on treated cohorts is +17% to +35% ARPU against a randomised holdout, which is a range across deployments in large B2C bases rather than a forecast for any specific business.