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Conversation Insights

Eliminating manual insight reporting in contact centers with self-serve analytics platform to enable faster operational decision making.

Company
RozieAI Labs
Client
Air Canada
Timeline
Q3 2024 - Q3 2025
Role
Product Designer

Analytics Platform for Air Canada's Contact Centre Managers

Conversation Insights is an enterprise analytics platform developed by RozieAI for Air Canada's contact centre managers (users). The platform uses AI to analyze hundreds of thousands of customer conversations, surfacing trends and operational signals that help managers understand their customer issues at scale. Although initially built to meet Air Canada's operational needs, the product was designed as a scalable solution for future enterprise customers.

Air Canada contact centre managers had access to AI-generated insights, but not a way to investigate them independently

Before Conversation Insights, AI-generated insights were delivered through weekly reports prepared by RozieAI product owners. While these reports helped Air Canada teams identify emerging issues, they provided limited context for understanding where those issues were occurring or what was driving them.

Investigating an issue meant moving between reports, AWS Connect, and follow-up discussions with RozieAI stakeholders to piece together the operational context. Users therefore depended on a fragmented, people-dependent workflow to move from identifying an issue to understanding it, slowing how quickly they could make operational decisions.

Diagram showing fragmented investigation across Outlook, Teams, Excel, Word, and AWS Connect

Understanding the system - users, data, & how teams analyzed customer issues.

I first aligned with RozieAI product owners, data scientists, and Air Canada stakeholders to understand how insights were generated, delivered, and investigated. This showed me that the existing reports surfaced issues but didn't support the investigation that followed, so I focused the product on the underlying workflow rather than recreating the reports.

Users are Air Canada's contact centre managers who used insights to identify emerging customer issues, then traced those issues to specific calls and operational patterns to understand where they were occurring.

By surfacing with data scientists, I found that teams combined two complementary types of data:

RozieAI Insights

  • Primary Topics
  • Customer Intents
  • Root Causes
  • Sentiment Analysis
  • Journey Moments

Operational Metadata

  • Interaction Duration
  • Routing Profile
  • Agent Username
  • Queue Name
  • 60+ Attributes

In order to understand how users approached analyzing customer issues, I conducted 6 interviews with team members. I found that insights were rarely consumed in isolation. Instead, they served as starting points for a broader analysis into their operational metadata.

The teams consistently followed this investigation workflow:

  1. Scope

    Define a time window to frame the analysis.

  2. Identify Issues

    Detect unusual patterns and emerging customer concerns.

  3. Understand Causes

    Use summaries and transcripts to understand what customers are experiencing.

  4. Trace Operational Impact

    Identify where the issue is occurring using call records and operational data.

Design Principle: Support investigation flow, not just consumption.

Design Principle: Keep both information layers within the same workflow.

Business wanted to ship fast and engineering wasn't ready to build new components or patterns.

RozieAI needed to demonstrate value to Air Canada ahead of a contract renewal, while engineering had only days to build a working release. There wasn't enough time to design the product from scratch, so we reused patterns and components from another RozieAI product.

I couldn't change that constraint, but I could control how we validated the experience. We shipped quickly, then ran weekly sessions with Air Canada teams to observe the product in use and identify where the inherited patterns created friction.

Initial Product Hypothesis

I used the investigation workflow I uncovered in discovery to structure the first version of the product. Teams moved through four stages: Scope, Identify, Understand, and Trace. I designed the experience around that sequence, rather than treating the dashboard as a collection of charts and data.

I anchored the experience around charts because teams needed to spot patterns before deciding what to investigate. I prioritized the metrics stakeholders already used to identify issues, such as changes in topic frequency and sentiment.

Early design showing visualization charts for conversation insights

I placed operational data below the insights so teams could move from identifying an issue to tracing it back to specific queues, routing profiles, and agents. This created a direct path from insight to investigation.

Early design showing the operational metadata data table below charts

I made date and column filters shared across the charts and table because scoping was the first step in every investigation. This let teams establish their context once and carry it across both information layers.

Early design showing shared column and date filters for charts and table

The deeper constraint I was designing against: AI-derived signals and operational metadata had previously lived in separate places, forcing teams to piece together a picture across reports and systems. Combining both layers in a single scrollable view was the core structural decision, not a layout preference, but a direct response to where the workflow broke down.

Evolving designs based on user feedback and constraints

After releasing the first version of the product, we conducted weekly feedback calls with our users to identify points of friction and additional requirements. Here's a list of all the problems from multiple user test sessions.

Introducing a Dashboard with Complementary Modes:

Over a couple of development sprints, I iteratively refined the product experience based on the observations made in the user test sessions.

I redesigned Conversation Insights around the way teams actually investigated issues. The first version put everything on one scrolling page, but user sessions showed that the workflow had a natural split: Overview helped teams decide what needed attention, while Table View helped them investigate why. I turned that split into two complementary modes.

I designed Overview as the starting point for investigation. It surfaces operational metrics and scannable insight cards so teams can quickly identify where attention is needed before digging deeper.

Overview mode with operational metrics and insight cards

I replaced the trend-line chart with insight cards because teams wanted to know which issues mattered right now, not just how they changed over time. Instead of forcing every issue into one template, I designed each card around the question it needed to answer.

Insight cards showing distinct comparisons for each issue type

I designed Table View for the deeper investigation. It connects insights to individual calls and exposes the operational attributes teams needed to trace issues across queues, routing paths, and agents.

Table view showing conversation records with operational attributes
Table view illustrating horizontal scroll friction with many columns

With 60+ attributes, a fixed table made users scroll through information they didn't need. I introduced Manage Columns so each team could bring the attributes relevant to its investigation into view.

Manage Columns panel for customizing visible table attributes

I replaced the reused chip pattern with a query-based filter because investigations rarely relied on a single condition. The new pattern let teams layer conditions across customer and operational data while keeping filters shared between Overview and Table View.

Beyond design...

After shipping all the changes, we noticed significant usage within the product and the product stickiness grew as part of our users everyday workflow. Here's a few outcomes from this project.

Sustained internal use and external interest supported a renewed client contract and generated new feature requests (under NDA), reinforcing the platform's long-term value beyond the initial engagement.

How this project changed the way I design

Designing for power users changed how I think about “better” design. Conversation Insights served only 20–25 users, but they were power users who cared less about delight and more about having the right information and affordances to do their jobs. Throughout the project, I often saw opportunities to make the interface dramatically more polished, but I learned that improving the interface isn't automatically improving the product. In this context, the best design was often the one that gave users exactly what they needed, without adding anything they didn't.

I learned to design with business momentum, not against it. Conversation Insights had to move quickly, and development had already started before I could fully shape the experience. That was uncomfortable at first, but I learned that a rigid design process isn't always the right response to a fast-moving business. My role was to keep the product moving while creating enough space to observe, reason, and improve the experience as we went. Speed didn't mean abandoning design; it meant being more deliberate about where design effort mattered most.

I learned to bring engineering into the design loop early. The insight cards were a custom interaction, and I knew they would require engineering work beyond the existing component system. By keeping engineering involved early and sharing the direction before the design was finalized, I could surface technical constraints sooner and give the team time to plan for what needed to be built. The result was less handoff and more shared ownership of the experience.