Retail customer service teams are under pressure to reduce cost-to-serve while managing rising volumes and real-time expectations. Yet a significant share of interactions, order status, FAQs, simple troubleshooting are still reaching live agents.
Organizations addressing this challenge are increasingly adopting scalable AI-driven customer experience solutions that balance automation with human support.
In this case, the retailer achieved 73% chat containment, resolving nearly half of all interactions without agent involvement.
In this case study, a Global Retailer deployed an AI chatbot on web chat to tackle exactly that problem: reducing live‑agent workload while maintaining a consistent customer experience.
The objective was clear: increase self‑service adoption and lower cost‑to‑serve without introducing CX risk. The solution focused on high‑frequency use cases like order status, FAQs, and light troubleshooting, paired with intentional escalation paths for when human support was needed.
This hybrid model reflects how modern customer care strategies are evolving, combining automation with human intervention when it matters most.
Over the campaign period, the chatbot supported thousands of customer interactions across 29 languages, delivering sustained containment and autonomous resolution through an operationally stable channel with ongoing optimization.
This aligns with cxperts’ approach to building balanced CX ecosystems, where AI enhances, and does not replace, human support.
Most importantly, the program demonstrated how AI self‑service can create real capacity relief for agent teams, freeing them up to focus on complex, high‑value conversations instead of repetitive work.
The full case study breaks down:
- how performance was measured (opened vs engaged vs resolved vs escalated)
- the containment and automated resolution results over time
- the governance and tuning model that kept performance consistent
- how AI self‑service translated into cost‑to‑serve impact