Introduction
Traditionally, customer service chatbots were designed to drive the customer through a pre-defined flow with a narrow outcome in mind: check the status of an order, transfer money, or file a request. These chatbots are still there, serving the same use cases, now just a little bit better with LLMs applied in some of the steps.
However, by architecting with LLMs in the center, we can follow the intent of the user, allowing for much more complex use cases: comparing products, mapping business needs to solutions, and potentially even generating tailored quotes and offers.
Who Benefits
Customers: Experience a new level of self-service, with personalized responses and solutions tailored to their specific needs and preferences.
Sales and Service Agents: Can focus on more complex and nuanced interactions with customers whose initial questions are already answered.
Businesses: Benefit from improved customer satisfaction, enhanced brand loyalty and reduced customer service costs.
Business Value
Improved Customer Satisfaction: Generative AI personalizes customer interactions by providing tailored solutions, recommendations, and support based on individual customer needs.
Increased Productivity: Generative AI can handle basic customer inquiries automatically, reducing wait times and resolving issues faster.
Cost Savings: By automating tasks and improving efficiency, generative AI reduces the workload on human agents, leading to cost savings in customer service operations.
Capabilities
Personalized Recommendations and Promotions: Generative AI can leverage customer needs and expressed intent to generate personalized product recommendations and tailored promotions.
Product Configuration: Customers can upload their requirements and business needs, and receive proposed configurations of (complex) products.
Multi-lingual Explanations: AI-centric chatbots can not only offer support in multiple languages, but also explain domain-specific concepts and features in layman terms.