Interactive Custom Product Design Assistance With Live-Agent Collaboration
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Solution Overview
Problem
Managing customer service response times across multiple messaging platforms with varying customer expectations is challenging, especially when agents need to handle concurrent conversations with different urgency levels, leading to potential cost savings but suboptimal service quality.
Innovation Solution
A Robotic, Optimized Clienteling Communications platform (ROCCo) using machine learning and natural language understanding to dynamically adjust response times based on conversation momentum, incorporating automated agents to assist live agents with user information and cross-platform communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agents handle concurrent conversations across multiple messaging platforms with different response time expectations, then customer service coverage is improved, but service quality deteriorates due to inability to meet varying urgency requirements
Solution Approach 1:
The system segments incoming messages into different priority levels (urgent, normal, low priority) based on conversation momentum analysis. This segmentation allows agents to focus on high-priority conversations first while still maintaining coverage across multiple platforms, resolving the contradiction between broad coverage and quality service delivery.
Solution Approach 2:
The system dynamically changes the response time parameter for different message threads based on calculated momentum values. High momentum conversations receive shorter response time targets, while low momentum conversations are assigned longer targets. This parameter adaptation enables agents to maintain service quality across varying urgency levels while managing concurrent conversations.
2Loss of energy
If longer response times are permissible for non-urgent queries, then staffing requirements are reduced and costs are saved, but customer satisfaction may be compromised
Solution Approach 1:
The system dynamically adjusts response time goals for each message thread based on real-time momentum calculations. Rather than applying a static response time policy, the system adapts targets to match actual conversation urgency, allowing longer periods for low-priority queries without compromising satisfaction of urgent matters.
Solution Approach 2:
The system continuously monitors conversation momentum and provides feedback to both the routing engine and agents. This feedback mechanism allows the system to adjust response time expectations based on evolving conversation dynamics, ensuring that extended response times for non-urgent queries do not negatively impact customer satisfaction.
3Reliability
If agents focus on high-priority urgent queries, then service quality for urgent matters is improved, but coverage for lower-priority conversations deteriorates
Solution Approach 1:
The system maintains continuous monitoring and routing of all message threads regardless of priority level. While agents focus on high-priority conversations, the system continuously manages lower-priority threads, keeping them active in the queue and assigning them to agents when capacity becomes available, ensuring ongoing coverage without sacrificing urgent query response quality.
Solution Approach 2:
The system implements periodic re-evaluation of conversation momentum and priority levels. This allows lower-priority conversations to be periodically reassessed and promoted to higher priority if momentum increases, ensuring that coverage is maintained across all threads while preserving the ability to focus on urgent matters when needed.
Data Source
AI summary
A method for assisting a user in designing a custom product includes transmitting, to a display provided on a user device, a user interface comprising an interactive model of the custom product; monitoring user interactions with the interactive model; determining, based on the monitored interactions that the user requires assistance; and transmitting, to a display of a live agent, an agent interface operative to enable the live agent to view the user's design, communicate with the user, and collaboratively modify the design.