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How to increase ARPU in telecom

Median postpaid operators grow ARPU 1.9 percent a year, the top quintile 6.7 percent. The five levers behind the gap, and how to measure each one.

Jonas Weber
  • #ARPU
  • #Telco
  • #Playbooks
How to increase ARPU in telecom

The question of how to increase ARPU in telecom has a narrow answer and a wide one. The narrow answer is price. The wide answer, and the one that compounds, is that median postpaid operators grow ARPU 1.9 percent a year while the top quintile grows 6.7 percent on the same tariffs, the same coverage and roughly the same handsets. The gap is not pricing power. It is how many decisions per subscriber the operator gets right each month.

What good looks like in 2026

From the Markin 2026 B2C ARPU benchmark set, 24 postpaid operators across the US and EU and 21 prepaid operators across LATAM and SEA:

SegmentMedian monthly ARPUTop quintileMedian TTM growthTop-quintile TTM growth
Telecom (postpaid), US / EU, n=24$34.60$48.201.9%6.7%
Telecom (prepaid), LATAM / SEA, n=21$4.10$7.302.1%7.9%

Markin B2C ARPU benchmark set, TTM to Q2 2026. Levels are monthly ARPU in USD at period-average rates and are not comparable across regions; the growth spread is the decision metric. Full method in the 2026 ARPU benchmarks by industry.

The five levers that actually move telecom ARPU

1. The plan ladder, timed per subscriber

Most operators run the upgrade ladder as a calendar campaign at contract anniversary. The moment that matters is behavioural: three consecutive months near the data ceiling, a new device on the account, or a second line added. In our deployments, moving the upsell trigger from anniversary to a behavioural threshold typically lifts accepted upgrades 20 to 35 percent at the same contact volume, because the offer arrives while the constraint is being felt rather than eleven months later.

2. Family and multi-line consolidation

A second line raises account revenue and cuts line-level churn, but a badly targeted family offer converts single lines that were already paying full price into discounted ones. Rank households by uplift, not by propensity: the target is the household that adds a line only because the offer exists, not the one that would have added it anyway. Score the offer against a randomised holdout of equally ranked households or the reported gain is arithmetic, not incremental.

3. Roaming and travel add-ons

Roaming is the highest margin per unit of attention in the whole postpaid catalogue and the most wasted. The signal is a network event, not a campaign segment: first attach on a foreign network, an airport cell, a booking confirmation email in the loyalty feed. Add-on attach rates in the top quintile run several times the median for one reason, latency. A travel pass offered inside the first ten minutes abroad converts; the same pass offered the next morning competes with a local SIM the customer already bought.

4. Device financing and handset cycles

Device financing is a tariff decision disguised as a hardware decision. The subscriber who refinances stays on contract, and the refinancing moment resets the plan mix. The lever is knowing which subscribers are inside the handset replacement window, which will churn anyway, and which will accept a plan step alongside the device. Treat the three groups identically and margin leaks into the second.

5. Retention that does not discount the base

Nothing destroys telecom ARPU faster than a save desk with unlimited discretion. Every discount granted to a subscriber who was staying anyway is a permanent ARPU cut on a customer with no incremental risk. Split the problem first, because payment-failure churn is a dunning fix and intent churn is a decisioning fix, as covered in voluntary vs involuntary churn and then route the intent half through uplift-ranked interventions with a preserved holdout.

Prepaid is a different problem

At a $4.10 median, prepaid ARPU is not moved by upgrades but by recharge frequency and top-up size. The three levers that carry the growth in our prepaid sample are recharge timing relative to balance depletion, bundle size laddering rather than blanket price rises, and reactivation of subscribers between 15 and 45 days silent. The economics reverse: the cost of a wrong decision is tiny, so the operator can afford far more of them, which makes prepaid the segment where continuous decisioning pays back fastest.

Why campaign calendars cap telecom ARPU

A large operator ships perhaps 40 to 60 marketing campaigns a quarter. A 10 million subscriber base generates hundreds of millions of decision moments in the same period. The mismatch is the whole story: the calendar can only address the average subscriber, while the ARPU is distributed across individuals whose ceilings, travel patterns and handset cycles do not align to a quarter. Operators in the top growth quintile are not running better campaigns, they are running more decisions per subscriber and measuring each one against a holdout.

  1. 1Rank actions, not customers. For each subscriber, score every eligible action, including doing nothing, on expected incremental margin over the next 90 days.
  2. 2Constrain at the account level. Contact frequency, discount budget and eligibility apply to the account, not the line, or family plans get double-treated.
  3. 3Hold out 5 to 10 percent. Reserve equally ranked, untreated subscribers per action so ARPU lift is read as treated minus control, not as before minus after.
  4. 4Read it monthly, per cohort. Report incremental ARPU per treated subscriber and margin after discount, so a lever that only moves gross revenue is caught quickly.

A 90 day sequence

Days 1 to 30, instrument: join billing, network, care and device data at subscriber and account level, and tag every historic intervention so a baseline exists. Days 31 to 60, ship two levers with holdouts, usually roaming attach and behavioural upsell, because both have short feedback loops. Days 61 to 90, read incremental ARPU, kill whichever lever failed its holdout, and only then extend to the save desk, which has the longest loop and the highest cost of being wrong.

For the horizontal version of this framework, see how to increase ARPU, and for the metric definition itself, what ARPU is and how it is calculated.


Markin runs these decisions continuously against your warehouse and writes the chosen action into the channels you already use. See Markin for telco and growth optimization.

Frequently asked

Questions readers ask about this.

How do you increase ARPU in telecom?
By deciding per subscriber rather than per campaign. The five levers that move telecom ARPU are behaviourally timed plan upgrades, family and multi-line consolidation, roaming and travel add-ons attached within minutes of the trigger, device financing tied to the handset replacement window, and retention that ranks interventions on uplift instead of discounting the whole base.
What is a good ARPU for a telecom operator?
In the Markin 2026 benchmark set, median monthly postpaid ARPU across 24 US and EU operators is $34.60, with the top quintile at $48.20. Prepaid in LATAM and SEA runs at a $4.10 median and $7.30 top quintile. Levels are not comparable across regions, so growth rate is the better comparison.
Why is telecom ARPU growth so slow?
Because the decision rate is capped by the campaign calendar. An operator ships 40 to 60 campaigns a quarter against hundreds of millions of subscriber-level decision moments, so most upgrade, roaming and retention opportunities are addressed as an average rather than individually, if at all.
How do you increase ARPU without raising prices?
Move mix rather than price: upgrade subscribers who are already hitting their plan ceiling, attach travel passes at the moment of first foreign network use, add lines to households whose behaviour indicates an unmet need, and stop discounting subscribers who were never going to leave.
How is ARPU uplift measured correctly?
Against a randomised holdout of equally ranked subscribers who receive no action. Report incremental ARPU per treated subscriber and margin after discount, monthly and per cohort. Before-and-after comparisons capture seasonality and selection, not the effect of the intervention.

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