Accenture Telecommunications United Kingdom

Virgin Media O2

Customer Service Automation

After the merger that created Virgin Media O2, the company needed to reduce siloed operations, legacy technology, and slow service experiences so it could resolve customer issues faster and deliver more personalized support across channels.

35% Improvement in net promoter score (NPS) in some areas of service
65% to 89%
80 seconds to a matter of moments
89% Percentage of complaints now closed in same day

Reported impact

Business outcome

Virgin Media O2 improved service quality and efficiency, reduced complaint handling delays, gave agents better case guidance, and used AI-driven workflows to raise customer satisfaction while lowering administrative overhead.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

Virgin Media O2 and Accenture modernized customer service around a cloud-based digital core that unifies customer data across channels and applies AI directly inside service workflows. The foundation gives agents a connected view of prior conversations and similar cases so they can predict customer needs and identify the best resolutions more quickly. On top of that core, VMO2 uses Amazon Connect as an AI-powered contact-center service spanning voice, chat, and mobile interactions. Real-time tools listen to live conversations, suggest responses, surface a next-best-action banner with relevant offers, and guide agents through documentation with an AI-assisted flow tool. A unified desktop keeps those capabilities in one place, while identity controls protect customer and agent data. The result is an operating model in which data, contact-center AI, and workflow automation work together to reduce transfers, shorten wrap-up work, and improve the consistency of customer support.

Data flow

Customer interactions from voice, chat, and mobile channels feed into the cloud-based digital core, where prior conversations and related issue history create service context. Amazon Connect and associated AI tools use that context during live conversations to recommend responses and next actions, while the AI-guided flow tool and unified desktop help agents document and resolve the case in one workflow.

Solution architecture

5 components · 4 layers
  1. Storage
    • cloud-based digital core Unifies customer data across channels so service teams can understand needs and resolve cases with better context.
  2. Serving
    • Amazon Connect Powers voice, chat, and mobile support interactions as the company's AI-enabled contact-center platform.
    • unified agent desktop Presents tools, context, and recommendations in one interface for customer-service employees.
  3. Orchestration
    • AI-guided flow tool Cuts call-documentation time and helps agents complete follow-up tasks more efficiently.
  4. Governance
    • secure identity solution Protects agent and customer data while the new service workflow operates across channels.

Architecture clues

  • A next-best-action banner is shown during live service conversations.
  • A unified desktop consolidates tools and context for agents.
  • Amazon Connect spans voice, chat, and mobile interactions.
  • The AI-guided flow tool automates call documentation steps.

Evidence from the source

"An AI-guided flow tool reduces the time it takes to document each call from 80 seconds to a matter of moments"
"The digital core also supports Amazon Connect, an AI-powered contact center service from AWS."
"These tools listen to customer interactions and draw on customer data to suggest responses to agents in real-time"
"VMO2 implemented a cloud-based digital core that accesses customer data across multiple channels."
"increasing same-day complaints closure from 65% to 89%"
Transparency Evidence & methodology 100/100 reference value · 89% 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
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Accenture

    Virgin Media O2 customer story

    First-party
    https://www.accenture.com/en/case-studies/communications-media/vmo2
    Source type
    Vendor-published customer story
    Published
    Jan 9, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jan 9, 2026 · Accenture · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.