Dynamic Pricing Engine Management
Network Planning & Scheduling › Pricing & Fare Management · 17 L4 steps · 6 phases · 6 decision gates · Updated 2026-03-18 19:33
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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 |
Ingest competitor fare data via ATPCO feed | Pricing Analyst | ATPCO | Daily ATPCO Type A/B/C fare change transactions | Competitor fare matrix by O&D pair and booking class | Feed ingestion latency ≤15 min from ATPCO publication window | N | N |
| 1.2 | Load demand signals and booking pace from NRM | Revenue Management Analyst | Amadeus Revenue Management (NRM) | Booking curve data, load factor forecasts, O&D demand segmentation | Demand signal dataset per flight-date-cabin combination | Demand signal refresh cycle ≤4 hrs for departures within 0–14 days | N | N |
| 1.3 | Validate data completeness and quality gate | Pricing Operations Analyst | AWS Redshift | Competitor fare matrix and NRM demand signal dataset | Data quality scorecard; ingestion batch approved or rejected | Data completeness score ≥95% before optimization run proceeds | Y | Y |
Phase 2 2.1 |
Configure fare class elasticity parameters | Senior Pricing Analyst | PROS RM | Historical booking data, price-response curves, market segment definitions | Elasticity parameter set by market, segment, and season | Parameter review cycle ≤30 days; elasticity model R² ≥0.85 per market cluster | N | N |
| 2.2 | Set fare floor and ceiling guardrail rules | Pricing Manager | PROS RM | Approved tariff schedule, cost floor benchmarks, revenue targets | Min/max fare bounds per booking class and market pair | Guardrail coverage 100% of active markets before each daily optimization run | N | N |
| 2.3 | Review and approve pricing model configuration | Director of Pricing | PROS RM | Elasticity parameter set and guardrail rule configuration | Approved pricing model configuration; version-tagged snapshot in audit log | Configuration approval cycle ≤4 hrs for routine updates; ≤24 hrs for structural changes | Y | N |
Phase 3 3.1 |
Run dynamic pricing optimization engine | Pricing Systems Engineer | PROS RM | Approved model configuration, demand signals, competitor fare matrix | Optimized fare recommendation set per flight-date-cabin | Optimization run completion ≤20 min for full network; recommendation coverage ≥98% of active O&Ds | N | N |
| 3.2 | Apply business rules overlay and sanity checks | Pricing Operations Analyst | PROS RM | Raw optimization output from pricing engine | Rule-adjusted fare set with full audit log of modifications | Business rule violation rate ≤0.5% of recommended fares per optimization run | N | N |
| 3.3 | Validate fare recommendations against guardrails | Pricing Analyst | PROS RM | Rule-adjusted fare set from business rules overlay | Validated fare set approved for filing; exception list flagged for manual review | Auto-approval rate ≥90% of recommendations; manual review queue cleared within 30 min | Y | Y |
Phase 4 4.1 |
File approved fare set to ATPCO | Fare Filing Specialist | ATPCO | Validated fare set with booking classes, rules, and effective dates | ATPCO fare transaction records; filed fare effective timestamps | Filing-to-live latency ≤20 min (ATPCO standard distribution cycle) | N | Y |
| 4.2 | Distribute fares across GDS and NDC channels | Distribution Systems Analyst | NDC API Gateway / Amadeus GDS / Sabre GDS / Travelport GDS | Filed ATPCO fares and NDC dynamic offer parameters | Published fares active across all indirect and direct distribution channels | GDS channel sync latency ≤30 min; NDC direct channel sync ≤5 min post-filing | N | N |
| 4.3 | Confirm fare availability in Altéa PSS | Pricing Operations Analyst | Amadeus Altéa PSS | Distribution confirmation messages from GDS and NDC channels | Fare availability audit report; PSS-confirmed live status | PSS availability confirmation within 35 min of ATPCO filing; availability accuracy ≥99.9% | Y | Y |
Phase 5 5.1 |
Monitor live fare performance and revenue metrics | Revenue Integrity Analyst | AWS Redshift / Tableau | Booking transactions, yield actuals, load factor by flight-date | Real-time pricing performance dashboard; automated alert triggers | Revenue variance vs. forecast ≤±5%; alert response time ≤15 min from threshold breach | N | N |
| 5.2 | Detect pricing anomaly and trigger override review | Revenue Integrity Analyst | PROS RM / AWS Redshift | Performance dashboard alerts and deviation threshold breaches | Override recommendation; escalation decision to Pricing Manager | Anomaly-to-action cycle ≤30 min; false positive alert rate ≤10% | Y | Y |
| 5.3 | Apply manual fare override and document rationale | Pricing Manager | PROS RM | Approved override decision with business justification | Override fare filed to ATPCO; audit trail record; model feedback flag | Override filing completion ≤20 min; override documentation rate 100% | N | N |
Phase 6 6.1 |
Assess model performance and revenue uplift | Senior Pricing Analyst | AWS Redshift | Monthly revenue actuals vs. RM optimization recommendations | Model performance scorecard; recalibration flag with supporting analysis | Dynamic pricing revenue uplift ≥3% vs. static pricing baseline; model MAPE ≤8% | N | N |
| 6.2 | Recalibrate model and release updated configuration | Director of Pricing | PROS RM | Performance scorecard and recalibration recommendations | Updated model configuration; version-tagged release with change log | Recalibration cycle ≤30 days; post-recalibration uplift improvement ≥1% within 14 days | Y | N |
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