National shoe retailer deploys complex conversational AI solution to boost serviceability for Black Friday and holiday season.
Our client, a large retailer specializing in athletic shoes and apparel online and across more than 650 stores nationwide needed to improve service speed and streamline order tracking inquiries. Their touch-tone IVR struggled to keep pace with high call volumes during the back-to-school and holiday seasons and was unable to quickly expand to accommodate their growing product catalog. Customer inquiries often fell outside of their IVR service menu resulting in an overwhelming number of agent transfers and long hold times. The retailer needed a solution that would drive efficiencies by reducing the number of calls their customer service team handled while providing an amazing customer experience.
Under a looming Black Friday deadline, the Waterfield Tech team worked quickly to review their existing customer experience and identify areas where conversational AI-powered virtual agents could make an immediate impact.
The solution layered a voice-first application in English and Spanish atop their existing Genesys platform with integrated chat capabilities. The introduction of an open-ended, natural language greeting addressed the “dead ends” customers were experiencing with the rigid touch-tone menu and enabled flexible self-service options for things like
Callers could now talk or chat with the virtual agents to inquire on items in plain language like “Where’s my order?” or statements such as “You said my package was delivered, but I don’t have it.”
Traditional natural language projects can take up to two years to plan and implement. Much to the client’s delight, Waterfield Tech launched the virtual agents in a fraction of the time- meeting the client’s Black Friday deadline.
The virtual agents returned immediate results doubling call containment and a 51% containment rate for chat interactions. Peak stability, self-service, and CSAT rates increased while agent transfers and operational costs decreased.
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