Product Design and User Experience
Product Design and User Experience
In my 5+ years at Gridspace, we designed the future of positive interactions between customers and companies. Our focus was to improve contact center experiences and internal processes for the world’s largest consumer brands using the latest ML and proprietary LLM technologies.
This design environment allowed users to edit pre made call handling flows from popular templates developed in house or create new ones based on unique customer needs.
Each node in the flow when clicked opened a panel that allowed for configuration.
The introduction of tabs allowed for easy switching between the nodes main purpose. In the case of a bot node, these were routing, creating a playbook, which is a list of rules for how to handle issues and a coaching view for correcting LLM outputs.
What is going on in my company today? This is the single question every executive wants to know. This Dashboard was designed to alert individual calls to stay ahead of developing problems, while also displaying baseline metrics for contact center activity and emerging trends for continued monitoring.
Clicking into a trend card would serve up all the conversations that a user can choose to review as well as export graphs for reports.
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Using the latest ML and AI technologies, each call was processed, summarized for intent and emotional valence, and labeled in real-time.
This view’s intent was to provide a historical snapshot of a customer’s interaction with the company, tracking key events as well as a summary of all conversations.
I was able to spend time with managers who oversaw teams of agents and map their process for coaching and ensuring quality and compliance.
This view would monitor the progress of individual managers' work queues and provide metrics for reporting.
In addition to viewing the performance of the department as a whole, users were able to click on individual managers to gauge progress.
This view allowed users to pose questions and then interact with the models output. Very similar to Chat GPT but trained specifically on customer data with a proprietary LLM.
This feature allowed the creation of outbound caller campaigns targeting current or prospective customers by enabling AI agents to help automate business development departments.
Each campaign was tracked and configurable - such as the number of allowable call attempts and the definition of call time windows to deter abuse of the feature.