Promotional Fare Design
Network Planning & Scheduling › Pricing & Fare Management · 19 L4 steps · 6 phases · 9 decision gates · Updated 2026-03-18 19:28
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Process Flow Diagram (BPMN)
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L4 Process Steps
| Step | Step Name | Role / Swim Lane | System | Input | Output | KPI | Dec? | Exc? |
|---|---|---|---|---|---|---|---|---|
Phase 1 1.1 |
Extract revenue shortfall signals by route | Revenue Management Analyst | Amadeus Revenue Management (NRM / AltéaRM) | Current booking pace reports, load factor actuals, revenue-per-ASM trend | Ranked list of routes with revenue shortfall vs. target | Routes identified with load factor <75% at 60-day departure horizon | N | N |
| 1.2 | Benchmark competitor promotional fares | Pricing Analyst | ATPCO | Competitor fare filings, OAG schedule data, ATPCO tariff feeds | Competitive fare landscape report by market and travel window | Competitor fare monitoring coverage ≥95% of target markets | N | N |
| 1.3 | Validate demand gap and competitive case | Senior Pricing Manager | Amadeus SkyCAST | Revenue shortfall list, competitive fare landscape report, demand elasticity model | Approved target market list or no-action record | Decision cycle time <2 business days from trigger | Y | N |
Phase 2 2.1 |
Define target markets and travel windows | Pricing Analyst | Amadeus SkyCAST | Approved target market list, historical promo booking curve data | Promotion scope document: routes, travel dates, sale window, fare class targets | Scope document completed within 1 business day of approval | N | N |
| 2.2 | Model promotional fare levels and AP rules | Pricing Analyst | PROS RM | Promotion scope document, demand elasticity curves, competitor fare benchmarks | Draft fare ladder with advance purchase, minimum stay, and cap rules | Modelled incremental revenue uplift ≥8% over base forecast | N | N |
| 2.3 | Simulate revenue and load factor impact | Revenue Management Analyst | Amadeus SkySYM | Draft fare ladder, current seat inventory allocation, booking pace baseline | Scenario simulation report: projected load factor, RASM, yield dilution estimate | Projected system load factor ≥83%; projected RASM ≥ current period RASM | Y | N |
| 2.4 | Draft detailed fare rules and blackout dates | Pricing Analyst | ATPCO | Approved fare ladder, simulation report, peak-period calendar | Complete fare rule set: Cat 2 (Day/Time), Cat 5 (Advance Purchase), Cat 6 (Min Stay), Cat 14 (Blackouts) | Fare rule completeness: all ATPCO mandatory categories populated before submission | N | Y |
Phase 3 3.1 |
Submit fare package to Pricing Committee | Senior Pricing Manager | SAP S/4HANA Finance (FI/CO) | Fare rule set, simulation report, revenue impact summary | Pricing Committee review submission with financial P&L impact | Submission to decision cycle ≤3 business days | Y | N |
| 3.2 | Conduct DOT advertising compliance review | Legal & Regulatory Counsel | Amadeus Altéa PSS | Approved fare rules, proposed marketing copy, sale and travel date windows | DOT compliance sign-off or revision request | 100% of promotional fares reviewed against DOT 14 CFR Part 399 before publication | Y | Y |
| 3.3 | Issue formal fare design approval record | Director of Pricing | SAP S/4HANA Finance (FI/CO) | Pricing Committee approval, DOT compliance sign-off | Fare design approval record with authorisation timestamp | Approval record issued within 1 business day of final compliance clearance | N | N |
Phase 4 4.1 |
File promotional fare records in ATPCO | Tariff Filing Specialist | ATPCO | Fare design approval record, complete fare rule set | ATPCO tariff records filed; transaction IDs logged | Filing-to-distribution latency <4 hours; ATPCO error-free submission rate ≥99.5% | Y | Y |
| 4.2 | Verify fare pickup in all GDS channels | Distribution Analyst | Sabre GDS | ATPCO transaction IDs, Travelport and Amadeus GDS access | GDS fare availability confirmation across Sabre, Travelport, Amadeus | GDS pickup verified within 6 hours of ATPCO filing; parity across all 3 GDS ≥99% | Y | N |
| 4.3 | Publish fare via NDC API Gateway and direct.com | Digital Distribution Analyst | NDC API Gateway | GDS-confirmed fare records, direct channel pricing API configuration | Promotional fare live on direct.com, mobile app, and NDC-connected OTA partners | Direct channel price parity with GDS ≥99.9%; NDC offer publication latency <2 hours | Y | Y |
Phase 5 5.1 |
Activate promotional fare and initiate sale | Pricing Operations Manager | Amadeus Altéa PSS | Confirmed channel activation record, marketing launch confirmation | Promo fare live status confirmed; booking class availability open in Altéa | Sale activation within 30 minutes of planned launch time; zero mismatch between advertised and bookable fare | N | Y |
| 5.2 | Monitor booking pace and load factor daily | Revenue Management Analyst | Amadeus Revenue Management (NRM / AltéaRM) | Real-time booking transactions, load factor dashboard, pace vs. forecast | Daily promo performance report: bookings, load factor, revenue vs. target | Daily booking pace within ±15% of forecast; load factor trajectory toward ≥83% at departure | Y | N |
| 5.3 | Adjust inventory caps based on pace signal | Revenue Management Analyst | Amadeus Revenue Management (NRM / AltéaRM) | Daily promo performance report, pace vs. forecast variance | Revised booking class caps or sale window extension decision | Intervention decision time <4 hours from pace alert; yield dilution contained to <3% of base fare RASM | N | Y |
Phase 6 6.1 |
Pull post-campaign revenue and yield report | Revenue Management Analyst | AWS Redshift | Closed booking records, promo fare transaction logs, flight departure actuals | Post-campaign report: total incremental revenue, load factor achieved, RASM vs. baseline | Report generated within 5 business days of sale close; variance from projection documented | N | N |
| 6.2 | Assess yield dilution and cannibalisation | Senior Pricing Manager | Amadeus SkySYM | Post-campaign revenue report, base-fare RASM pre-promo, cabin mix actuals | Cannibalisation assessment: dilution %, affected routes, net revenue verdict | Net revenue positive vs. no-promo scenario at ≥95% confidence; cannibalisation rate <10% | Y | N |
| 6.3 | Archive campaign benchmarks to data lake | Data & Analytics Engineer | AWS Redshift | Post-campaign report, cannibalisation assessment, fare rule set | Campaign record persisted in data lake; benchmarks available for future promo calibration | Campaign record available in data lake within 2 business days of analysis sign-off | N | N |
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