Augmented Intelligence Assistant for Customer Support Agents
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Solution Overview
Problem
Customer support agents often lack initial information about customers, including their complaint history and social media interactions, which hinders effective support provision, as they need to manually retrieve order information and assess customer needs during interactions.
Innovation Solution
A system that aggregates customer information from various sources, including current interactions, social networks, order databases, and complaint history, to determine customer intent and sentiment, and provides actionable insights to support agents through a generated interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If customer support agents manually retrieve order information and customer data from multiple sources during interactions, then they can provide comprehensive support, but the time required to gather information increases and support efficiency decreases
Solution Approach 1:
The system proactively retrieves and aggregates customer information from multiple sources (order databases, complaint history, social media) before the customer interaction begins. This preliminary action ensures that all relevant customer data is already available to the agent when the interaction starts, eliminating the need for manual retrieval during the interaction and reducing information gathering time.
Solution Approach 2:
An intermediary system is introduced between the customer interaction and the multiple data sources. This intermediary automatically queries, aggregates, and presents relevant customer information from various sources (order databases, complaint history, social media) to the agent, serving as a mediator that consolidates information retrieval into a single automated process rather than requiring the agent to access multiple systems manually.
2Measurement precision
If the system aggregates information from multiple sources including social networks and complaint history databases, then customer insight accuracy improves, but system complexity increases
Solution Approach 1:
The information aggregation system is segmented into specialized modules, each responsible for retrieving data from specific sources (order database module, complaint history module, social media module). Each module handles a particular data source independently, processing and filtering information according to its specific requirements. This segmentation allows the system to manage complexity by dividing the aggregation task into manageable, specialized components rather than a single monolithic system.
Solution Approach 2:
The system employs universal data processing components that can handle multiple data sources and formats through standardized interfaces. The aggregation framework is designed to work with various information types (structured database records, unstructured social media posts, complaint histories) using common processing logic, allowing the system to maintain accuracy across diverse sources while reducing overall complexity through reuse of standardized components.
3Ease of operation
If the system provides detailed aggregated customer information and actionable insights to agents, then support quality improves, but the complexity of the interface and processing increases
Solution Approach 1:
The interface presents information with local quality by tailoring the display of aggregated customer data to the specific context and needs of the agent. Different types of information (order status, complaint history, social media sentiment) are presented with appropriate levels of detail and formatting based on their relevance to the current interaction. This allows the interface to provide comprehensive information without overwhelming the agent, as each piece of information is presented in the most appropriate form for its specific purpose.
Solution Approach 2:
The system extracts and highlights only the most relevant actionable insights from the aggregated customer information, separating critical data from less important details. Instead of presenting all raw aggregated data, the system identifies and extracts key findings (such as urgent complaints, recent order issues, or notable social media mentions) and presents them prominently to the agent, reducing interface complexity by focusing on what truly matters for the current interaction.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for assisting an agent in providing support to a customer. The methods, systems, and apparatus include actions of obtaining interaction information regarding an interaction between a customer and an agent, identifying the customer from the interaction information, aggregating the information regarding the customer from multiple sources, determining an intent of the customer from the interaction information and the aggregated information, determining a sentiment of a customer from the interaction information and the aggregated information, determining a particular action to indicate to the agent based on the aggregated information, the intent, and the sentiment, and generating an interface to provide the agent based at least on the particular action.