Simulation & Prediction
Scenario Simulation Workspace
Compare current state, no action, recommended, upside and downside scenarios for one journey or issue.
Journey
Revenue & Profitability
KPI under simulation
Total revenue (QTD)
Current
USD 142.6M
Target
No stored target
Scenario outcome cards
Base case
Current trajectory with in-flight initiatives only.
- Stated outcome
- Voice revenue decline (annualised): -$2.7M
- Risk / trade-off
- —
- Total revenue (QTD) in 6 mo
- USD 142.6M ACTUAL
Hold prices
No change to bundle pricing.
- Stated outcome
- $0
- Risk / trade-off
- Margin keeps compressing
- Total revenue (QTD) in 6 mo
- USD 158.77M SCENARIO
+6% mid-tier repricing
Reprice mid-tier data bundles only.
- Stated outcome
- +$3.2M / yr
- Risk / trade-off
- Low churn risk
- Total revenue (QTD) in 6 mo
- USD 158.77M SCENARIO
- Expected value
- Revenue generated: USD 3.2M expected; USD 1.45M legacy recorded amount — excluded from current reporting · Not verified
+10% all bundles
Broad price increase.
- Stated outcome
- +$5.1M / yr
- Risk / trade-off
- High churn risk (+0.4pp)
- Total revenue (QTD) in 6 mo
- not modelled
Not stored for this journey: Downside. Shown as a gap rather than invented.
Scenario comparison — stated value vs cost/risk (USD M)
Amounts parsed from each scenario's stored results; ★ = recommended. Non-monetary outcomes plot as zero.
Risk / return matrix
Top-left = high value for low stated cost/risk.
Total revenue (QTD) — actual vs scenario paths
Same projection as the journey Predict stage and the Forecasting page. Open full forecast →
Comparison table
| Scenario | Role | Assumptions | Projected KPI (6 mo) | Stated impact | Risk / trade-off |
|---|---|---|---|---|---|
| Base case | Current State | Current trajectory with in-flight initiatives only. | USD 142.6M | Baseline | — |
| Hold prices | No Action | No change to bundle pricing. | USD 158.77M | $0 | Margin keeps compressing |
| +6% mid-tier repricing | Recommended | Reprice mid-tier data bundles only. | USD 158.77M | +$3.2M / yr | Low churn risk |
| +10% all bundles | Upside | Broad price increase. | — | +$5.1M / yr | High churn risk (+0.4pp) |
Scenario → decision
Recommended scenario: +6% mid-tier repricing — +$3.2M / yr; trade-off: Low churn risk.
No decision is saved here; the decision is recorded in the journey at Decide.
Source data, assumptions, methodology & limitations
Source data
- Telecom KPI data (current period, target, prior)
- Historical KPI trend data (monthly history Apr–Sep 2026)
- Scenario library (assumptions, results, recommended flag)
- Value realization records (expected / actual value)
- Evidence records
Assumptions
- No action: continues the 6-month stored trend (+2.69 per month).
- Recommended action: reverses the trend at the same monthly rate (no stored target).
- Scenario outcomes, costs and risks are as stored for each scenario.
Methodology
- Scenarios of the selected journey are ordered Current State → No Action → Recommended → Upside → Downside → Alternatives.
- With an originating issue, alternatives sharing no words with the issue move to the bottom of the table.
- Projected KPI per scenario uses the shared deterministic forecast; only Current State, No Action and Recommended are modelled.
Limitations
- Scenario outcomes are governed figures stored with each scenario — they are not re-computed by a simulation engine.
- Forecasts are straight-line projections of six stored months; they ignore seasonality, shocks and interactions between measures.
- Many KPIs have no stored target; where absent, the recommended path reverses the trend at the same monthly rate.
- No downside scenario is stored for any journey; downside coverage is shown as a gap rather than invented.
| Evidence type | Item | Source |
|---|---|---|
| assumption | FX assumption All figures in USD at a constant planning rate. | Finance Planning Dataset |
| methodology | Financial Performance methodology Consolidate revenue and cost from ledger; Compare to budget and prior year; Attribute margin variance to cost drivers. | Finance Planning Dataset |
| methodology | Revenue & ARPU methodology Compute ARPU by segment and product; Decompose change into price, mix and usage; Track yield per GB. | Finance Planning Dataset |