In 2025 many airlines are in the middle of moving from old “file fares + booking class” processes toward “Offer & Order” thinking. Customers expect clearer products, faster answers, and consistent prices. Internal teams want less friction. Regulators and partners want audit trails. You cannot modernize everything at once. This guide helps you decide what to build next with limited time and budget.
2. Core Problem in Plain Words
You need to show a trip, give a fair price, and confirm it without surprises. The trouble: different systems (cache, host / PSS, dynamic pricer, tax engine, payment, revenue accounting) each hold a different “truth” for a few milliseconds to a few minutes. If they drift you get “phantom seats”, price mismatches, angry customers, and manual fixes that burn staff hours.
3. Guiding Principles (Keep You Out of Trouble)
Single Source of Truth per Object: Know which system is master for seat counts, prices, tax rules, order state.
Everything Stamped: Every offer carries version numbers (inventoryVersion, pricingModelVersion, taxTableVersion).
Fail Closed, Not Random: If validation fails, clearly reprice or explain; do not auto-silent-adjust.
Measure Before Replace: Shadow new logic before turning off the old logic.
Short Feedback Loop: Daily dashboard that shows “off” signals fast (divergence, reprice rate, fallbacks).
Commit Flow: Submit token → verify signature + expiry → revalidate version numbers → accept or reprice.
5.3 Dynamic / Continuous Price Band
Why: Close the gap between forecast “best” price and nearest filed fare. Lufthansa Group has publicly discussed “continuous pricing” to fill steps between classical booking classes [1].
Steps:
Measure current price gap: average absolute (sold fare - model bid price).
Introduce smoothing (do not change more than N times per day for same flight/brand to protect trust).
Log every computed price with anchor, adjustments, final.
Metric: Gap reduction (%). Guardrail breach rate.
5.4 Product / Attribute Catalog
Why: Needed for clarity (baggage, changes, seat pitch, lounge) and to move away from only letter codes (RBDs). Qantas and other IATA participants have engaged in Offer & Order pilots that rely on attribute expression [2].
Actions:
List current brands → break into atomic attributes.
Provide safe static price anchor list for emergency mode.
5.8 Servicing Automation (Change / Refund)
Why: Manual change and refund work loads call centers and delays revenue clarity. Airlines like American Airlines and United Airlines have publicly emphasized digital self-service expansion [3][4].
Actions:
Expose /order/{id}/price-delta endpoint to simulate change before commit.
Store penalty and residual value logic in a single rules service (not duplicated in UI).
Emit OrderModified events with before/after components for accounting.
Metric: % of changes fully self-service; average handling time saved.
5.9 Security (Signed Offer / Anti-Replay)
Actions:
Sign offer token payload (HMAC or asymmetric). Include expiry (e.g. 15 minutes).
On commit, check: signature valid, not expired, versions unchanged (or reprice).
Store minimal hash of token to detect replay attempts.
Metric: Rejected replay attempts (should be near zero).
5.10 Interline / Partner Bridge
Why: Partners still expect filed fare + RBD. Air France-KLM, Lufthansa Group, and others continue to use hybrid models while adopting more dynamic elements [1][5].
Actions:
Offer includes: anchorFareCode + adjustmentAmount (positive or negative).
Sector proration: allocate adjustment proportionally by distance or revenue share rule; log method used.
If partner cannot consume token, send filed anchor fallback within allowed tolerance.
Metric: Interline rejection / dispute rate.
5.11 Data Quality & Governance
Actions:
Nightly job: rebuild “expected state” vs actual (inventory counts, order totals).
Publish “data quality score” (e.g. 99.7%) to leadership dashboard.
Define single owner per dataset (no shared orphan data).
5.12 Experimentation / A/B Harness
Actions:
Route a % of eligible searches to treatment (e.g. dynamic band). Control group remains legacy pricing.
Measure conversion, revenue per seat, refund rate difference.
Stop test early only if clear statistical signal (avoid false wins).
Metric: Experiments concluded per quarter; average time to decision.
Lufthansa Group: Has publicly described using “continuous pricing” to reduce gaps between filed fare steps and to broaden offer granularity in NDC channels [1].
Air France-KLM: Has discussed dynamic and continuous pricing experimentation in industry forums (e.g. ATPCO / conference sessions) [5].
American Airlines: Publicly pushed increased NDC adoption and emphasized modern retail offer distribution changes in 2023–2024, shifting some content earlier to modern channels [3].
United Airlines: Public communications on expanding digital self-service for changes / refunds (reducing call volume) support the “servicing automation” priority [4].
Qantas & Other IATA Pilots: Engaged in IATA Offer & Order pilots (ONE Order / modernization), highlighting attribute-based retail direction [2].
All examples reference public statements and conference or press materials. See References section for source domains. Verify any claim before internal reporting.
8. First 90 Days Action Plan
Weeks
Focus
Deliverables
1–2
Baseline & Ownership
Map masters (seat, price, tax); set KPIs (gap, divergence)
3–4
Provenance & IDs
Add correlation IDs + version fields to search output
5–6
Offer Token MVP
Signed token with offerId, inventoryVersion, expiry
7–8
Dynamic Band Pilot
Band logic on 5 markets (A/B enabled)
9–10
Event Stream
SeatDecrement + OrderCreated events (sequence, gap alerts)
11–12
Catalog v1
Attribute list + RBD mapping table; served via /catalog
A/B harness capturing uplift with control holdout.
Signed token verification pipeline (reject replay).
Risk register reviewed monthly.
12. Plain Glossary
Availability Divergence: When cached view and host view differ beyond tolerance.
Attribute Catalog: Structured list of product features (bags, changes, seat pitch).
Band (Pricing): Allowed range around an anchor fare where dynamic price can move.
Guardrail: Rule preventing unsafe or non-compliant price output.
Offer Token: Signed package describing how a price was built and its validity window.
Provenance: Recorded details of “who/what/when” for data creation.
Replay Attack: Attempt to reuse a past valid token after it should be invalid.
Shadow Mode: Run new system silently to compare with live system.
13. References & Notes
[1] Lufthansa Group public conference and ATPCO / industry presentations referencing continuous pricing (e.g. ATPCO Elevate events, public panel discussions). Source domains: atpco.net, lufthansa-group.com.
[2] IATA Offer & Order / ONE Order pilot participant communications (IATA public program materials). Source domain: iata.org.
[3] American Airlines public newsroom / distribution updates discussing NDC adoption and channel strategy (2023–2024). Source domain: news.aa.com.
[4] United Airlines public digital self-service and customer experience improvements (press releases / investor communications). Source domain: united.com / ir.united.com.
[5] Air France-KLM mentions of dynamic / continuous pricing in public industry events (e.g., ATPCO, conference sessions). Domains: corporate.airfrance.com, klm.com, atpco.net.
[6] ATPCO public material on dynamic pricing concepts and fare filing constructs. Domain: atpco.net.
[7] IATA documentation on Offer & Order architecture (NDC, ONE Order). Domain: iata.org.
Factual Note: Examples above reference public, high-level statements. Exact internal implementations, algorithms, thresholds, or commercial figures are not public and are not included. Always verify with the airline’s official publications or direct contacts before citing internally. This guide does not provide legal or tax advice.