Transforming the sales experience to accelerate scaled growth to $5B through iconic, seamless, and AI-powered digital interfaces.
Client
CrowdStrike / Salesforce
My Role
Lead UX Consultant
Domain
Enterprise SaaS · Cybersecurity
Year
2023
Tools
Figma · Miro · UserTesting
Overview
CrowdStrike, the Austin-based cybersecurity leader, had an ambitious target: scale its sales organization to $5B in annual recurring revenue. The problem was their Salesforce Sales Cloud experience had not evolved at the same pace as the company's growth — leaving sales teams navigating a fragmented, unintuitive platform that slowed productivity and frustrated the people responsible for hitting those numbers.
As Lead UX Consultant embedded with the Salesforce design team, I was tasked with designing an experience that would become a competitive differentiator internally — an iconic, seamless, and intelligent sales platform that professionals would actually want to use every day.
The Challenge
CrowdStrike's sales reps were spending more time wrestling with their tools than selling. The Salesforce platform, configured over years of incremental changes, had become a patchwork of disconnected workflows, redundant data entry, and unclear information hierarchies.
Fragmented workflows — Sales reps toggled between 6+ tabs to complete a single customer interaction, losing context at every step and wasting an estimated 2–3 hours per rep per day.
Low platform adoption — Without an intuitive experience, reps kept critical deal information outside Salesforce in spreadsheets and email, making pipeline data unreliable and forecasting inaccurate.
No AI leverage — The platform was not utilizing available AI capabilities to surface next-best actions, flag at-risk deals, or automate low-value data entry — a significant missed opportunity in a data-rich sales environment.
Accessibility gaps — Existing screens failed WCAG 2.1 color contrast requirements and had inconsistent keyboard navigation, creating barriers for sales team members with visual or motor disabilities.
Research
Before touching a wireframe, I needed to deeply understand how CrowdStrike's sales professionals actually worked — not how the system assumed they worked. I led a structured research program spanning three weeks.
18 moderated interviews with sales reps, managers, and SDRs across deal stages — enterprise, mid-market, and SMB.
Shadowed 8 sales reps during live customer calls and pipeline reviews to observe real tool usage patterns and workarounds.
Constructed future-state journey maps for 5 distinct user personas representing the full spectrum of sales roles and behavioral patterns.
Systematic audit of 40+ Salesforce screens against Nielsen's 10 usability heuristics, documenting 87 distinct pain points.
Analyzed Salesforce usage telemetry to identify drop-off points, most-used features, and features that were technically available but never touched.
Moderated usability sessions with existing users on current-state tasks, establishing baseline performance metrics for comparison.
Key Insights
Insight 01
"I have the data — I just can't find it fast enough." Speed and findability were more critical than any new feature. Reps needed fewer clicks to get to the information they already had.
Insight 02
"I trust my spreadsheet more than Salesforce." Platform credibility was broken — reps maintained parallel systems because they didn't trust the data in CRM to be accurate or current.
Insight 03
"I'd love AI that helps me prioritize, not just summarize." Reps were open to AI assistance but wanted it to be actionable — telling them what to do next, not just restating what they already knew.
Insight 04
New reps (< 6 months) struggled significantly with onboarding to the platform, extending time-to-productivity by weeks — a hidden cost the business had underestimated.
Design Process
With clear research findings in hand, I structured the design process around three guiding pillars: establish an innovation-focused mindset, define an experience foundation for consistency and efficiency, and deliver a simple, intuitive interface for successful adoption.
Deep research, persona development, and journey mapping to align the team on who we were designing for and why.
Synthesized research into a prioritized problem space — pain points ranked by frequency, severity, and business impact.
Facilitated design sprints, sketching sessions, and concept critiques to explore the full solution space before narrowing.
High-fidelity Figma prototypes, tested in moderated sessions. Iterated three rounds based on user feedback before final handoff.
Design Decisions
Three design decisions had the greatest measurable impact on adoption and task efficiency:
Unified Deal Workspace — Consolidated the 6-tab workflow into a single-pane deal view with contextual panels, reducing average task completion time by bringing all relevant data into one coherent view.
AI-Powered Next Best Actions — Surfaced intelligent recommendations (follow-up timing, deal health scores, at-risk flags) directly in the deal view, making AI assistance feel natural rather than bolted on.
Accessibility-First UI System — Rebuilt the component library with WCAG 2.1 AA color contrast, full keyboard navigation, ARIA labeling, and focus management — creating a more inclusive experience for all users.
Impact
Post-launch feedback and usability metrics showed significant improvements across all key dimensions of the sales experience:
↑ 34%
Improvement in platform adoption rate among sales reps in the first 60 days post-launch
↓ 41%
Reduction in average time to complete core sales tasks (pipeline update, deal review, follow-up logging)
↑ 28%
Increase in CRM data quality scores as reps began entering information directly rather than via workaround spreadsheets
Key Learnings
Speed of access beats breadth of features. Sales reps didn't need more capabilities — they needed faster paths to the ones they already had. This reinforced that UX strategy must start by mapping the task frequency-difficulty matrix before proposing new features.
Trust is a design problem. The parallel spreadsheet behavior wasn't laziness — it was a rational response to an unreliable system. Rebuilding trust required both design changes and a clear communication strategy around data reliability.
AI feels natural when it reduces work, not adds to it. The most successful AI integration happened when it eliminated a manual step the user was already doing — not when it added a new interaction they had to learn.