Consumer expectations have moved faster than most enterprise contact center strategies. Customers who track parcels in real time, resolve bank disputes via app, and book appointments at midnight expect the same immediacy from their appliance manufacturer when a washing machine breaks down on a Sunday evening. The channel they reach for first is no longer the phone. It is WhatsApp.
For a company operating across 25+ European markets in ~25 languages, with brands spanning every segment of the home appliance market, the challenge is compounded. A single, inconsistent customer service experience - slow, office-hours-only - erodes brand equity across every market where a query goes unanswered or a resolution takes too long.
The economics are equally urgent. With 3.3 million annual calls generating 4.5 million minutes of agent time, even modest containment improvements translate into significant cost savings.
But the opportunity is not only defensive. Automated interactions create the infrastructure for cross-selling, proactive communication, and service appointment management, turning a cost center into a revenue contributor.
The company's goals were to:
When the appliance retailer began its contact center transformation in 2024, the digital channels were already in place. WhatsApp, live chat, and web were all operational. But there was a fundamental gap: the only self-service alternative was a community page that the team themselves described as not very successful and not user-friendly.
But the dedicated Business Project Manager and a Business Project Owner assigned to drive it forward were pulled away by an unrelated major product safety campaign in the UK mid-implementation, leaving the IT team, led by the Contact Center Technology Manager, to carry the project largely alone. It was not the ideal setup for a business-critical initiative, and the team recognized it as the project's most significant internal challenge.
The company ran parallel evaluations for CCaaS and conversational AI in 2024, ultimately arriving at a clear strategic conclusion: the existing CCaaS platform was stable and well-integrated with Salesforce - no case for replacement.
What was missing was an intelligent automation layer capable of handling voice and digital channels simultaneously with genuine conversational capability. Druid AI was selected primarily on the strength of its predictable pricing model for digital channels and a partnership approach that the team described as significantly more engaging throughout the evaluation process.
The UK proof of concept was implemented in three months and deployed across voice, WhatsApp, and web AI agent. The deployment was designed to run at 100% agent-first routing from the outset: every inbound contact handled by the AI agent first, with conditional escalation to a human when needed. The solution is fully integrated with both CCaaS and CRM.
The solution includes several core capabilities:
Best-of-breed beats all-in-one when the existing stack is solid
The appliance retailer’s decision to enhance its existing CCaaS platform rather than replace it was grounded in a clear evaluation: the infrastructure worked. Adding a specialized AI layer delivered the automation capability without the disruption, cost, and risk of a full platform migration.
Cross-functional ownership is critical to transformation
When IT had to carry the project alone, the deployment succeeded, but the experience confirmed that AI initiatives without dedicated, accountable business ownership deliver less than they should. Formalizing a permanent cross-functional AI team for Phase 2 is now a stated priority.
Start with containment, expand with confidence
The phased approach, proving value in the UK before committing to a multi-market rollout, gave leadership the evidence needed to drive expansion. The €539K UK return is not just a financial result. It is the internal business case for the next 24 markets.
Complexity is a reason to start, not a reason to delay
Appliance customer service is inherently unstructured: customers call about products they cannot always identify, issues they cannot always describe, and repairs that span multiple systems and teams. The assumption that this complexity makes automation difficult proved wrong. A well-configured AI agent can bring structure to ambiguous interactions, and the messier the process, the greater the efficiency gain.