casestudies-signals

Signals turns silent client data into actionable insights for our sales teams. By centralizing digital touchpoints like webinar attendance and email clicks, we give our crew the context they need to reach out. The goal is to replace generic pitches with personalized conversations that actually meet client needs.

Problem Statement

In institutional sales, we often have plenty of data but very little context. Our sales teams were frequently "shooting in the dark," reaching out to clients without knowing their current priorities. The industry data revealed a clear disconnect: 73% of B2B customers expect us to understand their unique needs, 71% say most sales interactions feel purely transactional, and 46% of reps feel they have sufficient data about buyer intent. We saw a massive opportunity to use data to bridge this gap. That was the origin of Signals.

My Role

As the lead designer, I was responsible for all aspects of the project, from discovery and heuristic alignment to rapid iterative prototyping and enterprise integration. I conducted audits of the legacy state, interviewed front-line crew members to understand cognitive load, worked with marketing and data science to map behaviors using the Jobs to Be Done (JTBD) framework, and collaborated with architects to ensure seamless UX within our existing CRM and Azure Active Directory environments.

Overview

The goal of the Signals program was to transform raw data into actionable intelligence. We built an ecosystem that provides: Centralized Visibility — Bringing company and contact-level interests into one location. No more "data hunting" across siloed platforms. Qualified Interactions — We moved the needle from "making calls" to having high-value engagements aligned with specific growth plays. Marketing & Business Alignment — By optimizing our tech stack, we created a shared language between marketing efforts and sales results.

Target Users

Target users for this application were institutional sales teams, front-line sales representatives (crew members), and marketing teams who needed to bridge the gap between client data and actionable insights. The platform serves sales reps who need to prioritize prospects, prepare for high-stakes calls, and follow up in real-time based on client behavior.

What is a "Signal"?

What exactly is a "Signal"?

A Signal is a digital footprint. It is the behavior of a client, prospect, or consultant that tells us what they care about before we ever jump on a call.

The Program Ecosystem

In the first phase, we aggregated activity from across our digital and physical touchpoints:

Events: In-person activity and webinar attendance.
Digital: Website visits, email interactions, and "Book a Meeting" requests.
Engagement: Seismic LiveSend activity and Consultant Hub downloads.

The Vision: Intelligent Sales Enablement

The goal of the Signals program was to transform raw data into actionable intelligence. As the lead designer, I focused on three core outcomes:

Centralized Visibility: Bringing company and contact-level interests into one location. No more "data hunting" across siloed platforms.
Qualified Interactions: We moved the needle from "making calls" to having high-value engagements aligned with specific growth plays.
Marketing & Business Alignment: By optimizing our tech stack, we created a shared language between marketing efforts and sales results.

The Process

From Data Chaos to Design Clarity

Designing Signals was not just about building a dashboard; it was about creating a mental model for how sales teams interpret human behavior at scale.

Discovery & Heuristic Alignment: I began by auditing the legacy state, interviewing front-line crew members to understand the cognitive load they faced when toggling between multiple disconnected platforms.
Defining Taxonomy via JTBD: Working with marketing and data science, we mapped behaviors to the Jobs to Be Done (JTBD) framework. This helped us weigh which signals mattered most based on actual sales situations.
Rapid Iterative Prototyping: I moved from lo-fi wireframes to high-fidelity prototypes to test the information hierarchy. The challenge was displaying seven different data sources without overwhelming the user.
Enterprise Integration: A significant portion of the work involved collaborating with architects to ensure the UX functioned seamlessly within our existing CRM and Azure Active Directory environments.

Strategic UX Artifacts

1. Friction Audit (Heuristic Evaluation)

Before designing, I conducted a redline audit of the legacy workflow to quantify why our teams were struggling.

Heuristic Violation	The Legacy Experience	Impact on the Business
Data Fragmentation	Toggling between 4+ tools to piece together a client's story.	Loss of Context: The "moment of intent" often passed before data was found.
High Cognitive Load	Relying on memory rather than recognition for client activities.	Increased Error: High risk of outdated or generic outreach.
Lack of Visibility	No visual hierarchy for "Lead Warmth" or intent levels.	Wasted Effort: Reps chased "noise" while high-value signals sat buried.

2. The Signals Job Story Map (JTBD)

To ensure the design was rooted in human intent, I developed these Job Stories to prioritize features based on the rep's situational needs.

The Context (When...)	The Motivation (I want to...)	The Success Metric (So that...)
Pre-call prep: Preparing for a high-stakes call.	See a summary of recent webinar and download history.	I can lead with an insight rather than a generic pitch.
Morning prioritization: Starting the day with 50+ prospects.	Filter the contact list by the "warmest" recent signals.	I spend my energy on the leads most likely to engage.
Real-time follow-up: A prospect clicks a LiveSend link.	Receive a notification detailing exactly what they are viewing.	I can reach out with a relevant resource while top-of-mind.

3. Signal Taxonomy & Information Architecture

The "magic" of the platform is in how we organized the chaos. I architected a hierarchy that moved data through a three-stage filter: Collection, Categorization, and Prioritization.

Interests (Topic-Based): "What should I talk about?" (e.g., ESG, Fixed Income).
Intent (Action-Based): "When should I call?" (e.g., "Book a Meeting" vs. a simple email open).

4. Service Blueprint (Systems Thinking)

Signals acts as a bridge between Marketing and Sales. This blueprint maps the handshake between the "Front Stage" (the Sales Rep's dashboard) and the "Back Stage" (the Marketing tech stack).

Front Stage: Real-time Alerts, Intent Scoring, and Contact Timelines.
Line of Visibility: The interface that translates complex data into simple insights.
Back Stage: Azure AD security, CRM synchronization, and the multi-channel data engine.

5. The "Intent-to-Interaction" Journey Map

This map tracks the emotional and operational shift of a Sales Rep, moving from the frustration of "Data Hunting" to the confidence of "Strategic Consulting."

Phase 1 (Prioritization): From "Shooting in the dark" to an Intent-Led Feed.
Phase 2 (Identification): From fragmented research to a Unified Timeline.
Phase 3 (Synthesis): From guesswork to Categorized Meaning.
Phase 4 (Engagement): From a transactional pitch to a Consultative Partnership.

Outcomes

By delivering the right message to the right audience at the right time, Signals has fundamentally changed the front-line experience.

The Impact

From Transactional to Consultative — Reps now enter meetings knowing exactly what topics a client has been researching.

Operational Efficiency — We eliminated the manual friction of tracking client behavior across multiple disconnected systems.

Increased Effectiveness — Sales and marketing materials are now tailored to actual buyer intent, rather than guesswork.

Conclusion

Phase 1 was about visibility. As we look toward the future, we are exploring how to integrate generative AI to synthesize these signals into automated "Pre-meeting Briefs," making the transition from data to dialogue even more seamless.