👋
Customer support teams, spanning retail, phone, chat, and field operations, found it difficult to navigate their daily tools.
Overview
Agents struggled with an overload of tools, leading to duplicated efforts and misplaced information. Our goal was to create a new, consolidated platform that would streamline their workflow and allow them to offer best-in-class customer service.
Research
3 weeks
Strategy
3 weeks
Implementation
3 weeks
What goes here:
Mockup of cross-functional timeline
What goes here:
Mockup of cross-functional reporting for a given week
Discovery & Research
Product teams built similar capabilities across tools, resulting in duplicate functions and disparate data sources. Workflows and information found in these tools became inconsistent and unreliable.
Observation
Observing 15 live calls from 5 frontline agents
Agent interviews
Understanding current and desired workflows
Stakeholder interviews
Learning about related business challenges
What goes here:
Research findings
Lack of training / policy adherance
Overwhelming agent to customer dialogue
Dashboard lacks guidance
Strategy
The current dashboard driven workflow placed almost no restrictions on call handling. To address this, we needed to implement a system that provides clear policy guidance and dynamically delivers relevant information at each step.
Objective
Accessibility first
Objective
Role customization
Objective
AI driven guidance
What goes here:
Explorations around multi-modal scalability
Implementation
To better support both agents and the business, we've implemented AI-driven 'Paths'. These dynamic, step-by-step experiences empower agents with the necessary support while preserving the flexibility for genuine human interaction.
Playbook
Providing strategy and guidelines for consistent work
Paths Library
Making it easy to implement and share ideas across teams
Training
Equipping teams with conversational design best-practices
What goes here:
Mockup from the Playbook
What goes here:
Mockup from the Path Builder
What goes here:
Example of dialogue from a cross team training session
Outcomes
The introduction of 'Paths' allowed the business to define clearer, more effective policies, immediately saving money and increasing customer satisfaction. Agents have enjoyed the resulting consistency, and our design teams are now confident in designing for conversationally driven experiences.
$150
million
projected to be saved by end of year through credit paths
$25
million
projected to be saved by end of year through WiFi paths
Mockup of Recommended Path feature
Case study
Want to learn more?
To access the full case study, reach out to me at lindsay.bolger@hey.com.
Request access
Up next
Mercury Assistant
View work