Fare Class & Bucket Management
Network Planning & Scheduling › Revenue Management · 18 L4 steps · 6 phases · 9 decision gates · Updated 2026-03-18 18:47
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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 |
Define fare class hierarchy in PSS | Revenue Management Analyst | Amadeus Altéa PSS | Fare product strategy brief, IATA booking class standards | Booking class configuration (Y, B, M, H, K, Q, V, W, L, G) in PSS | 100% of active fare products mapped to a booking class within 2 business days of product launch | N | N |
| 1.2 | Map booking classes to RM fare buckets | Revenue Management System Analyst | Amadeus Revenue Management (NRM) | Booking class list from Altéa, revenue segmentation guidelines | Fare bucket mapping table in NRM linking each booking class to O&D revenue tier | Zero mismatches between Altéa booking class codes and NRM bucket definitions at go-live | N | N |
| 1.3 | Validate fare class structure against ATPCO rules | Pricing & Tariff Analyst | ATPCO | NRM bucket mapping, published ATPCO fare records (Records 1, 2, 3) | Validation report confirming booking class eligibility rules match ATPCO Cat 25 combinability and Cat 5 advance purchase restrictions | 0 ATPCO rule violations flagged by GDS edits at filing; audit pass rate 100% | Y | Y |
| 1.4 | Publish fare class availability to GDS channels | Distribution & GDS Analyst | Sabre GDS | Validated booking class configuration, SSIM schedule file | Live availability display in Sabre, Travelport, and Amadeus GDS for all market O&Ds | GDS availability refresh latency ≤ 90 seconds from NRM availability change; GDS parity rate ≥ 99.5% | N | N |
Phase 2 2.1 |
Extract historical O&D booking data by fare class | Revenue Analyst | AWS Redshift | Altéa booking history (24 months), departure date range for planning horizon | O&D booking curve dataset segmented by fare class, day-of-week, and booking lead time | Data extraction covering ≥ 95% of historical departures within planning window; query SLA ≤ 4 hours | N | N |
| 2.2 | Run demand forecast model per O&D bucket | Revenue Management System Analyst | Amadeus Revenue Management (NRM) | Historical booking curves, competitive fare inputs from OAG Schedule Analyser | Forecast demand curve per fare bucket per O&D, including unconstrained demand estimate | Forecast MASE (Mean Absolute Scaled Error) ≤ 0.25 for 90-day horizon; unconstrained demand estimate within ±12% of actuals at departure | Y | N |
| 2.3 | Review forecast variance and approve bucket plan | Senior Revenue Manager | Amadeus Revenue Management (NRM) | NRM forecast output, analyst commentary on anomalous markets | Approved demand forecast for bucket sizing; markets flagged for manual review escalated | Forecast review cycle ≤ 48 hours before system-generated availability updates are applied; < 5% of markets requiring manual correction | Y | N |
| 2.4 | Set initial bucket inventory levels for departure | Revenue Management Analyst | Amadeus Revenue Management (NRM) | Approved demand forecast, aircraft capacity from Amadeus SkyMAX fleet assignment | Initial bucket protection levels and booking limits per fare class per flight | Bucket initialisation completed ≥ 330 days before departure for all long-haul markets; ≥ 90 days for short-haul | N | Y |
Phase 3 3.1 |
Configure EMSR-b bid price and nesting strategy | Revenue Management System Analyst | Amadeus Revenue Management (NRM) | Approved bucket protection levels, fare class revenue values, willingness-to-pay estimates | Bid price thresholds and virtual bucket nesting hierarchy loaded into NRM for each O&D | Bid price calibration complete ≥ 180 days before departure for long-haul; revenue uplift from nesting vs. partitioned control ≥ 2% RASM on calibrated markets | N | N |
| 3.2 | Monitor booking pace against expected S-curve | Revenue Management Analyst | Amadeus Revenue Management (NRM) | Real-time bookings from Altéa, expected booking curve from NRM forecast | Booking pace index (BPI) per flight per fare class; pace alerts for flights deviating > 20% | BPI monitored at T−90, T−60, T−30, T−14, T−7, T−3 checkpoints; alert resolution SLA ≤ 4 hours | Y | N |
| 3.3 | Adjust bucket thresholds based on pace deviation | Revenue Management Analyst | Amadeus Revenue Management (NRM) | Booking pace alert, current bucket availability, revised demand estimate | Updated protection levels and booking limits; change log entry in NRM audit trail | Bucket adjustment applied within 2 hours of pace alert; average revenue recovery per adjustment ≥ $8/seat on affected flights | N | Y |
| 3.4 | Open or close fare classes per booking curve triggers | Revenue Management Analyst | Amadeus Altéa PSS | NRM availability recommendation, time-to-departure trigger rules | Updated fare class availability in Altéa and reflected across GDS/NDC channels | Availability update propagated to all GDS endpoints within 90 seconds; close-out of discount classes ≥ T−3 on flights with load factor forecast ≥ 90% | N | N |
Phase 4 4.1 |
Monitor real-time bookings and cancellations by class | Revenue Management Analyst | Amadeus Revenue Management (NRM) | Live Altéa booking stream, cancellation and refund events | Real-time seat availability dashboard; auto-triggered re-open events when cancellations create sell-up opportunity | Revenue recovery from cancellation-triggered re-opens ≥ $15/seat; dashboard refresh ≤ 5-minute lag vs. Altéa booking engine | Y | Y |
| 4.2 | Process group booking requests against bucket availability | Group Sales Coordinator | Amadeus Altéa PSS | Group booking request (≥10 pax), current bucket availability, group fare contract | Group PNR with blocked inventory or counter-offer at higher fare class | Group booking response time ≤ 24 hours; group revenue contribution ≥ 8% of total flight revenue on managed markets | Y | Y |
Phase 5 5.1 |
Review exception queue for manual intervention flights | Senior Revenue Manager | Amadeus Revenue Management (NRM) | NRM exception queue (special events, comp schedule changes, distressed inventory flags) | Prioritised list of flights requiring analyst action; override justification form initiated | Exception queue reviewed twice daily (09:00 and 14:00 local); < 3% of managed flights in exception state at any time | Y | N |
| 5.2 | Apply manual bucket override with audit justification | Senior Revenue Manager | Amadeus Revenue Management (NRM) | Approved override justification, competitor fare filing from OAG Schedule Analyser, event calendar | Manual availability override applied; audit log entry with approver, rationale, and expected revenue impact | 100% of manual overrides documented in NRM audit trail; post-event review shows manual overrides outperform model recommendation in ≥ 60% of cases | Y | N |
Phase 6 6.1 |
Generate post-departure revenue performance report | Revenue Analytics Analyst | AWS Redshift | Altéa final booking close-out file, NRM forecast at departure, actual revenue by fare class | Post-departure report: RASM, load factor, yield, and spill/spoilage by bucket for each departed flight | Report generated within 48 hours of departure; RASM variance between actual and pre-departure NRM estimate reported for 100% of flights | N | Y |
| 6.2 | Recalibrate NRM forecast model parameters | Revenue Management System Analyst | Amadeus Revenue Management (NRM) | Post-departure performance reports (rolling 13 weeks), MASE trend, spill and spoilage analysis | Updated NRM model parameters; recalibration change log; improvement targets set for next review cycle | Quarterly recalibration reduces MASE by ≥ 0.02 vs. prior quarter; spill rate ≤ 3% and spoilage rate ≤ 5% on managed O&Ds post-calibration | Y | N |
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