ServiceNow Travel and Hospitality United Kingdom

Southeastern Railway

Customer Service Automation

Southeastern Railway was processing Delay Repay claims, complaints, incident reports, and fraud checks across disconnected systems, which delayed compensation, forced temporary hiring during disruptions, and made trend detection and reporting far too manual.

70% of Delay Repay claims automated
95% case classification across 300+ complaint types
99% reduction in major incident reporting time
Average Delay Repay response time fell to 2.9 days

Reported impact

Business outcome

Southeastern automated about 70% of Delay Repay claims, cut major-incident reporting from 96 hours to four minutes, achieved about 95% complaint-type classification accuracy, paid many automated claims within 24 hours, improved customer satisfaction from about 82% to 87%, and reduced average Delay Repay response time to 2.9 days.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

Southeastern and partner UP3 rebuilt customer operations on the ServiceNow AI Platform by centering everything on Customer Service Management and a trusted shared data foundation. Delay Repay claims, complaints, assisted travel bookings, lost property, and customer inquiries now flow through one platform instead of scattered systems. Operational, ticketing, and compensation data are unified so the business can automate Delay Repay at scale, give agents a complete view of each passenger interaction, and detect fraud more reliably. On top of that foundation, ServiceNow Otto generates major-incident reports in minutes instead of days and produces concise case summaries for service staff, while Virtual Agent handles a large share of webchat requests. This combination of shared data plus AI automation turned a compensation workaround into a reusable customer-service platform that supports better decisions, faster reimbursements, and same-day issue detection.

Data flow

Customer, ticketing, operational, and compensation data now enter one ServiceNow foundation, where Customer Service Management routes claims and service issues end to end. ServiceNow Otto analyzes those interactions to generate reports and summaries, while Virtual Agent handles conversational inquiries before staff step in on cases that need human judgment.

Solution architecture

3 components · 3 layers
  1. Compute
    • ServiceNow Otto Generates major-incident reports and concise case summaries from ServiceNow interaction data.
  2. Serving
    • Virtual Agent Handles webchat inquiries automatically so staff can focus on more nuanced customer cases.
  3. Orchestration
    • Customer Service Management Unifies claims, complaints, bookings, and customer inquiries on a single operational platform.

Architecture clues

  • A single major incident can generate approximately 20,000 claims in a month.
  • The virtual-agent chatbot went live in four weeks and now handles 70% of webchat inquiries.
  • UP3 connected operational systems, ticketing data, compensation workflows, and fraud detection into one ServiceNow platform.

Evidence from the source

Southeastern now processes approximately 70% of Delay Repay claims automatically.
Southeastern selected Customer Service Management and collaborated with ServiceNow partner UP3 to unify customer operations on a single platform.
UP3 connected operational systems, ticketing data, compensation workflows, and fraud detection into one ServiceNow platform.
Using ServiceNow Otto, the tool analyzes interactions within ServiceNow and generates a complete report in four minutes.
With approximately 95% accuracy, Southeastern spots emerging trends and safety issues the same day cases are submitted.
Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. ServiceNow

    Southeastern Railway customer story

    First-party
    https://www.servicenow.com/customers/southeastern-railway.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • Sitemap last modified Aug 28, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Aug 28, 2026 · ServiceNow · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.