Designing Signal Inbox: A smarter way to give agents instant customer context
A case study on using jobs-to-be-done and the double diamond to turn fragmented session data into clear, actionable signals.
-
Name
Signal Inbox - A live stream of behavioral signals and session context.
-
Phase
MVP -- Phase 1
- Business
-
My Role
End-to-end Product Design & Leadership, UX research, system modeling, interaction design, replay integration
-
Impact summary
- Reduced context switching during triage and initial response
- Improved comprehension of customer behavior in under 5 seconds
- Transformed replay from a separate tool into contextual intelligence
- Figma project
- FigJam project
Overview
Support agents rely on clarity. In the first moments of a conversation, they quickly assess who they’re helping, what happened recently, and which signals matter for the issue at hand. But in most CX tools, this context is scattered — buried in logs, timelines, sentiment dashboards, and analytics. Agents jump between multiple systems to reconstruct a customer’s story.
Signal inbox was designed to solve this gap. It brings together the most important behavioral cues, session activity, and emotional patterns into a single, prioritized feed that agents can interpret in seconds. Instead of piecing together data across tools, agents get immediate clarity on what the customer did, felt, and experienced — right before deciding how to respond.
This case study explains how I used jobs-to-be-done and the double diamond process to identify the agent’s decision moments, define the clarity gaps, and design a context model that reduces cognitive load. It breaks down the reasoning behind each design choice, from signal prioritization to hierarchy and the role of replay intelligence.
The result is a context system that turns raw behavioral data into actionable clarity.
Discovery to delivery
Discovery
Identified friction points through research analysis and interviews. JTBD helped reveal what users were really trying to achieve.
Define
Synthesized insights into key problems and prioritized them. JTBD guided problem framing and design focus.
Explore
Ideated and prototyped solutions to test how Signal Inbox could improve resolution. JTBD ensured alignment with user goals.
Deliver
Delivered end-to-end UX, prototypes, UI components, and design specs—all tied to measurable impact
Uncovering CX struggle through behavior and intent
We conducted workflow mapping, listening sessions, and behavioral observation inside live support environments. A universal pattern emerged: during the first 30 seconds of a conversation, agents hunted for clarity.
Discovery outcome
This phase validated a clear opportunity to build a shared visibility layer for CX work — one that removes guesswork and exposes the customer’s journey as behavioral evidence, not hearsay. This informed our product brief and paved the way for the Define phase.
Key insight themes
-
Lack of behavioral context stalls resolution
Struggle: Agents and CX teams often received vague or incomplete customer complaints. -
Escalations stem from internal blind spots
Struggle: Developers and product managers lacked visibility into what the customer experienced before bugs were reported. -
Manual reconstruction is error-prone and time-consuming
Struggle: CX and support agents manually pieced together customer journeys from logs, CRM notes, or multiple tools. -
Analytics miss the human story
Struggle: Quantitative tools (dashboards, funnels) failed to explain why users struggled. -
Frontline support workarounds mask systemic issues
Struggle: Agents developed their own hacks (e.g. screen shares, mock logins) to resolve tickets.
The JTBDs
Core JTBD
JTBD: “When I open a customer conversation, help me see what the customer recently did and experienced so I can respond accurately and avoid unnecessary questions.”
Secondary jobs
- When I’m preparing my initial response, help me understand the signals that shape this customer’s expectations.
- When an issue feels unclear, help me identify the likely root cause without searching across systems.
- When I’m short on time, reduce cognitive load by showing only what’s relevant to this specific issue
- Help me feel confident that I’m not missing anything important.
JTBD: Support Agent (SA)
JTBD: When investigating bugs or regressions, I want to Signal Inbox the exact steps users took, so I can debug faster without reproducing manually.
Motivations
- Reduce time-to-resolution.
- Deliver confident, accurate responses on the first touch.
- Increase CSAT and deflection of unnecessary escalations.
JTBD: CX Analyst
JTBD: “When support patterns emerge or a new release drops, I want to investigate sessions in bulk, so I can spot usability or workflow problems and influence product fixes with evidence.”
Motivations
- Drive systemic improvements and remove UX friction.
- Detect usability pain points early.
- Improve top call drivers and reduce volume through insights.
JTBD: Product Manager
JTBD: “When CX or support flags recurring friction points, I want to validate the impact by seeing real user sessions, so I can prioritize confidently and advocate for fixes with stakeholders.”
Motivations
- Validate and quantify customer pain with behavioral evidence
- Confidently prioritize product backlog items/li>
- Confidently prioritize product backlog items
- Close the loop with CX and support teams using hard evidence
JTBD: Developer
JTBD: “When a bug is assigned to me, I want to Signal Inbox what the user did leading up to the error, so I can reproduce the issue and fix it faster without guessing or recreating edge cases.”
Motivations
- Reproduce bugs reliably and reduce fix time.
- Spend less time decoding vague Jira tickets.
- Improve velocity and code quality.
Define Phase — Framing the Right Problem
After uncovering deep behavioral struggles across roles in the Discover phase, the Define phase helped us narrow in on the real problem. We synthesized dozens of quotes and interactions using the JTBD framework to surface role-specific struggles, identify tool-based workarounds, and quantify unmet outcomes.
What we saw in Discovery
Each role — Support Agent, Product Manager, Developer, CX Analyst — had to stitch together the user experience manually. Agents copied logs into Slack. PMs relied on secondhand screenshots. Developers asked for Zoom calls. These were not just workflow issues — they were friction points on the JTBD timeline.
Figjam
Mapping JTBD struggles
Using the JTBD struggle timeline, we placed each behavior in context. We could now see when and why users felt increasing frustration — and what moved them from tolerating the problem to seeking a new solution.
FigjamRole specific JTBD
Next, we framed each role’s goal using JTBD syntax. This helped us ground the problem in user motivation — not just feature requests. This framing guided all future design decisions and tradeoffs.
- Support Agent
- CX Analyst
- Product Manager
- Developer
The underserved opportunity
By scoring desired outcomes, we identified a clear pattern: the ability to visually understand the user’s experience — quickly and without switching tools — was highly important and low satisfaction. This was our design opportunity.
FigjamThe before state
We visualized the before-state using struggle cards. Each one tells a story of tool overload, time waste, and unnecessary effort — all symptoms of an unmet job.
Figjam
Defining the problem
From these insights, we arrived at our problem thesis: How might we reduce the time and cognitive effort needed to understand a user’s experience, without requiring multiple tools, manual summaries, or user back-and-forth? This became the foundation for Signal Inbox.
FigjamDefining success
Lastly, we defined success in measurable terms. These outcomes tied directly back to the JTBDs we uncovered — ensuring we would solve the real, high-value problems for each role.
- Reduce tool-switching time
- Increase confidence in triage
- Shorten time-to-resolution
- Remove need for manual evidence gathering
What we defined: A clear opportunity, a shared problem, and a focused path forward
Signal Inbox was no longer just a feature idea — it became a strategic enabler of fast, accurate, and collaborative issue resolution.
Framing and testing potential solutions with cross-functional teams
After mapping JTBD and pain signals across roles, we explored prototypes that addressed their most critical moments of struggle. Signal Inbox was a net-new feature, so we worked closely with Nordstrom’s CX, product, and engineering teams to validate desirability, feasibility, and usability before building.
JTBD aligned prototypes
Role specific solution paths
|
Role
|
Prototype tested
|
JTBD trigger addressed
|
|---|---|---|
| Support Agent | Signal Inbox timeline w/ escalation bookmarks | I can’t explain what happened on this call” |
| CX Analyst | Query/tag filtering + batch Signal Inbox viewer | “I’m seeing a spike in refunds but no pattern yet” |
| Product Manager | Signal Inbox + session metadata in journey tools | “I don’t know what experience caused the drop” |
| Developer | Event-based data architecture testing | “I need to know if this breaks our infrastructure” |
Speed + signals
Agents need speed. Analysts need signal. PMs need story.
Entry points
Entry points into Signal Inbox must reflect each role’s workflow.
When vs. how
“When to Signal Inbox” was as important as “how to Signal Inbox”
Hidden blockers
Storage and cost surfaced as hidden blockers—developer input early saved months
What we learned
Each role’s expectations around “Signal Inbox” were different.
Agents prioritized speed-to-resolution and wanted Signal Inboxs that aligned with escalation bookmarks. Analysts needed signal clarity—tools to batch-analyze spikes and tag patterns across sessions. PMs, by contrast, saw Signal Inbox as a narrative tool to explain unknown drops in the user journey.
These differences revealed that Signal Inbox could not be a single modal experience—it had to adapt based on workflow context. Additionally, developer feedback surfaced a critical technical constraint: storing full Signal Inboxs by default was not sustainable. By shifting to a trigger-based architecture early, we preserved feasibility without compromising value.
Delivering clarity, speed, and alignment at scale
I led the design and launch of CX Signal Inbox—a net-new feature built from scratch to accelerate issue resolution and improve cross-team alignment. Grounded in the Double Diamond and JTBD frameworks, I delivered production-ready UX flows, prototypes, UI components, filtering logic, and full design specs—shaped by real user needs and validated by results.
Support agents resolved tickets faster: Signal Inbox gave agents instant context—eliminating the need to ask users to reproduce steps or dig through logs manually.
Fewer tickets escalated to engineering: Product managers and analysts used Signal Inbox to triage bugs more effectively—flagging duplicates and eliminating false positives.
CX analysts diagnosed issues quicker: Signal Inbox surfaced event sequences and user paths that previously required stitching together logs, screenshots, and call transcripts.
Developers got what they needed—faster: Engineers used Signal Inbox links embedded in Jira tickets to directly view what happened, reducing time spent reproducing edge cases.