Five questions, and the decomposition each one needs.
These are the questions a telecom commercial analyst gets asked — in a meeting, at short notice, with an answer expected before the numbers are complete. Each one below is worked through to the point where a decision can be made, and each ends by naming the single piece of data that decides it.
Case 1 — revenue grew while customers fell
decompositionThe question. Revenue is up 5%, the customer base is down 8%. The board wants to know whether that is a good quarter. The honest first answer is that the question cannot be answered from those two numbers — but the decomposition can be produced in a minute and narrows it to one missing fact.
Decompose before answering. The same headline can be a strong result or a warning depending entirely on which customers left.
ΔRevenue = ARPU₀·ΔCustomers + Customers₀·ΔARPU + ΔCustomers·ΔARPU. The decomposition is arithmetic and always available. The one input that decides the verdict — the value mix of who left — is not in any public dataset, which is precisely why the aggregate ARPU line in a results presentation should never be read on its own.
Case 2 — the competitor cut price by 10%
decision treeThe question. Should we match? Matching is a decision about elasticity and about what the competitor is trying to buy. In a two-operator market a matched cut is usually the worst of the options: the market loses revenue and neither side gains share.
Q = Q₀ · (P/P₀)ε. Elasticity is an input here, not an estimate.
With constant elasticity, revenue has no interior maximum — if |ε| > 1 revenue always rises as price falls, and if |ε| < 1 it always rises as price rises. That is a property of the functional form, not of the market, and it is exactly why the contribution optimum (which does exist, because variable cost bites) is the one a commercial team should be looking at.
Case 3 — should we launch a new prepaid pack?
business caseThe question. Management proposes MVR 350 for 30 days and 50GB. The answer is not yes or no — it is a business case with a break-even take-up and a test design.
Management proposes a new prepaid pack. Answer with a business case, not an opinion.
| Comparable published pack (21–31 days) | Operator | Price | Data | MVR / GB |
|---|---|---|---|---|
| PREPAID | Dhiraagu | MVR 50 | 0.5 GB | MVR 102.4 |
| Prepaid | Ooredoo | MVR 50 | 0.5 GB | MVR 102.4 |
| 500MB | Dhiraagu | MVR 99 | 0.5 GB | MVR 202.75 |
| MONTHLY 125 | Dhiraagu | MVR 125 | 1.5 GB | MVR 83.33 |
| MONTHLY 150 | Dhiraagu | MVR 150 | 3.0 GB | MVR 50 |
| THREE WEEKS 275 | Dhiraagu | MVR 275 | 15 GB | MVR 18.33 |
| THREE WEEKS 300 | Dhiraagu | MVR 300 | 25 GB | MVR 12 |
| MONTHLY 350 | Dhiraagu | MVR 350 | 15 GB | MVR 23.33 |
| MONTHLY 450 | Dhiraagu | MVR 450 | 30 GB | MVR 15 |
| SALHI MONTHLY | Dhiraagu | MVR 495 | 56 GB | MVR 8.84 |
| MONTHLY 500 | Dhiraagu | MVR 500 | 35 GB | MVR 14.29 |
| 100GB (21 days) | Dhiraagu | MVR 550 | 100 GB | MVR 5.5 |
| Proposed pack | — | MVR 350 | 50 GB | MVR 7 |
The decision turns on two numbers management will not have: how many buyers are genuinely new, and what the switchers were spending before. Both are knowable from the operator's own pre-period data and neither can be inferred after launch without a control group. The recommendation that follows from this structure is procedural: launch it as a controlled test in a defined region or customer cohort, hold a matched control group out, and measure incremental revenue against them — not against last month. Comparable packs are real products from the published catalogue.
Case 4 — high-value customers are recharging less
value × probabilityThe question. Who gets the retention offer? The instinct is to target the highest churn risk. The instinct is wrong: the customers most likely to leave are usually the ones worth least, and a fixed budget spent on them protects very little. Switch the targeting policy below and watch the value protected fall.
A synthetic base of 4,000 prepaid customers, scored by an interpretable logistic model. Choose which customers a fixed retention budget should buy.
| Customer | Spend / month | Recharges / month | Days since recharge | Tenure | Data trend | Churn probability | Value at risk |
|---|---|---|---|---|---|---|---|
| C-2365 | MVR 486 | 1.0 | 45 | 11m | −65% | 80% | MVR 2,653 |
| C-0765 | MVR 419 | 0.6 | 52 | 25m | −65% | 85% | MVR 2,442 |
| C-0264 | MVR 406 | 0.8 | 51 | 13m | −59% | 85% | MVR 2,362 |
| C-0862 | MVR 423 | 0.5 | 54 | 37m | −52% | 80% | MVR 2,321 |
| C-1281 | MVR 416 | 1.0 | 51 | 4m | −31% | 77% | MVR 2,201 |
| C-2134 | MVR 389 | 1.2 | 48 | 8m | −59% | 83% | MVR 2,197 |
| C-1857 | MVR 437 | 2.0 | 51 | 42m | −65% | 71% | MVR 2,116 |
| C-2382 | MVR 407 | 0.5 | 47 | 21m | −41% | 75% | MVR 2,087 |
| C-1880 | MVR 420 | 0.8 | 58 | 43m | −30% | 72% | MVR 2,073 |
| C-1142 | MVR 427 | 0.7 | 53 | 50m | −43% | 70% | MVR 2,044 |
Scoring model: logit(p) = -1.15 + 0.055·days-since-recharge − 0.34·recharges-per-month − 0.021·data-trend − 0.018·tenure − 0.0016·spend. Coefficients are stated, not fitted. Switching the policy from value at risk to churn probability raises the average churn probability of the contacted group and lowers the value protected — which is the result the panel exists to show.
Case 5 — the new bundle grew subscriptions and revenue fell
incremental vs cannibalisedThe question. Did the launch work? Subscriptions are up, so the campaign report says yes. Revenue on the existing packs is down by more. The bridge below is how to show that in one table — and why a control group at design time is worth more than any amount of analysis afterwards.
Two existing products and one promotional price between them. The question is not how many customers took the promotion — it is what they were worth before they took it.
| Revenue bridge | Customers | Was worth | Now worth | Effect |
|---|---|---|---|---|
| Switched from product A at MVR 300 | 9,000 | MVR 2.7m | MVR 3.15m | +MVR 0.45m |
| Switched from product B at MVR 400 | 6,000 | MVR 2.4m | MVR 2.1m | −MVR 0.3m |
| New to the category | 5,000 | — | MVR 1.75m | +MVR 1.75m |
| Incremental revenue | 20,000 | MVR 5.1m | MVR 7m | +MVR 1.9m |
Set the promotion below product A's price with most take-up coming from A's existing customers and the incremental line turns negative while the headline stays large. That is the case worth being able to spot before launch, not after: a campaign can grow subscriptions, be reported as a success, and reduce revenue. The one input that decides it — what switchers were spending before — is knowable only from the operator's own pre-period data, which is why a promotion needs a defined control group at design time.
Every one of them is a headline number that cannot be interpreted until it is decomposed, and every one turns on a fact the aggregate does not contain: who left, what switchers were spending, how demand responds to price. The analytical work is not the arithmetic — the arithmetic is a few lines. It is knowing which missing fact decides the answer, and designing the measurement that produces it before the decision is taken rather than after.