Post-Flight Survey & NPS Management
Customer Experience & Loyalty › Customer Experience Operations · 17 L4 steps · 6 phases · 7 decision gates · Updated 2026-03-18 22:06
📊
Process Flow Diagram (BPMN)
📋
L4 Process Steps
| Step | Step Name | Role / Swim Lane | System | Input | Output | KPI | Dec? | Exc? |
|---|---|---|---|---|---|---|---|---|
Phase 1 1.1 |
Identify post-flight survey eligible pax | CX Analytics Manager | Amadeus Altéa PSS | Flight departure confirmation and boarded pax manifest | Eligible passenger list with contact metadata | Eligible pax coverage ≥ 95% of boarded passengers | N | N |
| 1.2 | Validate opt-in status and contact data | CRM Data Analyst | Salesforce Marketing Cloud | Eligible passenger list from Altéa PSS | Suppression list (opted-out / invalid email) and validated send list | Suppression accuracy 100% — zero survey sends to opted-out contacts | Y | Y |
| 1.3 | Generate personalised survey link and dispatch | CX Automation Specialist | Qualtrics XM | Validated send list with flight metadata (route, cabin class, flight number) | Personalised survey email sent within 24 hours of flight arrival | Survey dispatch latency ≤ 24 hours post-arrival; email deliverability ≥ 97% | N | N |
Phase 2 2.1 |
Monitor survey response rate by flight cohort | CX Analytics Manager | Qualtrics XM | Dispatch log and response ingestion feed | Real-time response rate dashboard per flight/route | Target survey response rate ≥ 20%; industry benchmark ~15–18% | Y | N |
| 2.2 | Send reminder to non-responders at 48 hours | CX Automation Specialist | Salesforce Marketing Cloud | Non-response flag from Qualtrics XM at 48-hour mark | Single reminder email dispatched to eligible non-responders | Reminder lift in response rate ≥ 5 percentage points; max 1 reminder per passenger | N | Y |
| 2.3 | Validate response completeness and ingest to analytics | CX Data Engineer | AWS S3 | Raw survey responses from Qualtrics XM export | Cleaned response dataset loaded to AWS S3 data lake | Partial response exclusion rate ≤ 5% of total submissions; ingestion latency ≤ 1 hour | Y | Y |
Phase 3 3.1 |
Classify respondents as Promoter, Passive, or Detractor | CX Analytics Manager | Qualtrics XM | Validated 0–10 NPS responses from AWS S3 | Classified respondent list: Promoters (9–10), Passives (7–8), Detractors (0–6) | NPS score = (% Promoters − % Detractors); target airline industry NPS ≥ 40 | N | N |
| 3.2 | Perform text analytics on verbatim comments | CX Insights Analyst | Qualtrics Text iQ | Open-text survey responses classified by NPS segment | Themed sentiment clusters: e.g. boarding, crew, catering, IFE, baggage | Auto-classification accuracy ≥ 85% vs. manual coding sample; ≤ 10% uncategorised verbatims | N | N |
| 3.3 | Flag detractor responses and route to recovery queue | CX Automation Specialist | Salesforce Service Cloud | Detractor records (NPS 0–6) with verbatim themes from Qualtrics XM | Service cases created in Salesforce Service Cloud with priority tier based on score severity | Detractor case creation latency ≤ 4 hours from survey submission; 100% of NPS 0–3 cases created same day | Y | Y |
Phase 4 4.1 |
Correlate NPS scores with operational performance data | CX Insights Analyst | AWS Redshift | NPS dataset from AWS S3 joined with OTP, delay codes, load factor from Amadeus Altéa | Correlation matrix: NPS vs. OTP, delay duration, cabin load, crew staffing | Correlation analysis refresh cycle ≤ 24 hours; cover ≥ 90% of surveyed flights with ops data join | N | N |
| 4.2 | Identify systemic failure drivers and escalate | VP Customer Experience | Tableau | Correlation matrix and themed verbatim clusters from AWS Redshift | Systemic issue flags (e.g. route-level catering failure, recurring IFE outage) with owning department assignment | Systemic issues identified within 48 hours of NPS batch; ≥ 80% of flags routed to correct owning department | Y | Y |
| 4.3 | Generate NPS insight report for leadership | CX Insights Analyst | Tableau | Aggregated NPS scores, segment data, systemic flags, and verbatim themes | Weekly NPS insight report: route heatmap, driver waterfall, trend vs. prior period | Report published by Monday 08:00 local time for prior week's flights; ≥ 95% data completeness | N | N |
Phase 5 5.1 |
Prioritise detractor outreach by severity tier | Customer Recovery Specialist | Salesforce Service Cloud | Detractor case queue from Salesforce Service Cloud prioritised by NPS score and LTV flag | Prioritised outreach list: Tier 1 (NPS 0–3, high-value loyalty member), Tier 2 (NPS 4–6) | Tier 1 first contact attempt within 24 hours; Tier 2 within 72 hours | N | N |
| 5.2 | Execute personalised recovery — miles or voucher | Customer Recovery Specialist | Salesforce Service Cloud | Detractor case with verified passenger loyalty profile and verbatim complaint theme | Recovery action logged: goodwill miles credited via Altéa loyalty API or travel voucher issued | Recovery offer acceptance rate ≥ 65%; average goodwill cost per case ≤ $35 equivalent | Y | Y |
| 5.3 | Confirm recovery satisfaction and close case | Customer Recovery Specialist | Salesforce Service Cloud | Recovery action confirmation and passenger acknowledgement | Case closed with recovery outcome tag; re-survey flag set if unresolved | Case resolution rate ≥ 80% within 7 days; re-escalation rate ≤ 5% | Y | Y |
Phase 6 6.1 |
Publish NPS dashboard to operational stakeholders | CX Analytics Manager | Tableau | Aggregated NPS scores, driver analysis, recovery metrics from AWS Redshift | Live Tableau dashboard: route NPS heatmap, weekly trend, detractor volume, recovery SLA status | Dashboard refresh latency ≤ 4 hours; ≥ 200 active monthly dashboard users across CX, Ops, and Commercial | N | N |
| 6.2 | Update CX improvement backlog with NPS-driven initiatives | VP Customer Experience | Salesforce Service Cloud | Systemic failure flags, driver waterfall, and benchmarked NPS gap vs. target | Prioritised improvement backlog items linked to specific NPS driver themes and owning teams | ≥ 3 backlog items added per monthly NPS review cycle; ≥ 60% of prior-cycle items in active delivery | N | N |
📋