Skill analysis
Skill analysis
Skill analysis
Timeline
2024 - 2 months
Team
3 Engineers, PM, Learning Experience
Outcome
Morphed performance data into a benchmarking and coaching tool no one else in the industry had built.
Context
Context
Context
Uhubs is a B2B SaaS platform that helps revenue leaders build high-performing sales teams. It uses performance and people data from the Pulse, a 360 competency assessment, to diagnose skill gaps and create tailored development plans that drive growth.
Uhubs is a B2B SaaS platform that helps revenue leaders build high-performing sales teams. It uses performance and people data from the Pulse, a 360 competency assessment, to diagnose skill gaps and create tailored development plans that drive growth.
The problem
The problem
The problem
Managers were responsible for developing their teams, but they lacked clarity on what actually made top performers successful.
They had access to performance data and assessment scores. But the real questions remained unanswered: What specific competencies drove success? Which skill gaps mattered most? Where should coaching time be spent?
Without answers, coaching became reactive. Managers waited for problems to surface, then scrambled to fix them.
The result was expensive. One-size-fits-all feedback. Repeated performance issues. Frustration on both sides. And retention problems that created constant hiring costs.
Every morning, reps started from scratch.
They’d spend the first part of their day explaining who they’re looking for: criteria, company size, industry, role. Then they’d scour the data. Then they’d research each prospect individually. They’d jump to LinkedIn to verify freshness. They’d check the company website to confirm technologies.
They’d map the buying committee. Then they’d draft the outreach.
This entire cycle happened every single day. By the time they were ready to reach out, they’d already context-switched five times and lost focus.
Every morning, reps started from scratch.
They’d spend the first part of their day explaining who they’re looking for: criteria, company size, industry, role. Then they’d scour the data. Then they’d research each prospect individually. They’d jump to LinkedIn to verify freshness. They’d check the company website to confirm technologies.
They’d map the buying committee. Then they’d draft the outreach.
This entire cycle happened every single day. By the time they were ready to reach out, they’d already context-switched five times and lost focus.
What we learned
What we learned
What we learned
We conducted interviews with managers responsible for developing their teams. We mapped their workflow: identifying a performance concern, diagnosing the issue, creating a development plan. We wanted to understand where the real friction lived.
We started with a full discovery phase.
We ran a big round of user interviews to understand the actual workflow of reps when they were searching for leads, prospecting, and qualifying prospects.
We weren’t looking for solutions, at this point. We were looking for pain points.
Where did the time actually go? Where did they get stuck?
We started with a full discovery phase.
We ran a big round of user interviews to understand the actual workflow of reps when they were searching for leads, prospecting, and qualifying prospects.
We weren’t looking for solutions, at this point. We were looking for pain points.
Where did the time actually go? Where did they get stuck?
01
01
Visibility wasn't the problem, interpretation was
Managers already had data. Performance assessments. Employee records. Multiple reports. The bottleneck wasn't access; it was making sense of it all. They needed to know what to do with the data.
02
Benchmarking doesn't exist
Managers could see individual scores, but couldn't compare them to top performers. Without a benchmark, "good" was undefined. They had no way to know what success actually looked like.
Reps had plenty of leads. The problem was noise. They spent hours manually filtering and prioritising just to disqualify prospects that didn’t fit.
03
Coaching priorities are unclear
Even when skill gaps were visible, managers didn't know which ones mattered most. Coaching happened randomly instead of strategically, focused on whatever problem surfaced first.
Designing the comparison framework
Designing the comparison framework
Designing the comparison framework
We knew the answer had to make comparison effortless. Managers needed to see what differentiated top performers at a glance. And crucially, which competencies their reps were falling behind on so they could bring them up to speed and maximize their revenue potential.
We already had a spider chart in the Pulse report - a static view that compared one person against benchmark data. It was useful, but limited. Two variables max.
But the feedback was strong. Managers kept coming back to that chart. They wanted more from it.
So we asked: what if we took that familiar visual and made it flexible? What if managers could overlay any variables they wanted? Compare a rep against the team average, against a top performer, against another rep. Layer multiple data sources on one chart. See all the gaps at once instead of jumping between views.
The spider chart became our comparison medium - the thing that made multifaceted benchmarking possible. Managers could ask any question about competency differences and see the answer immediately.
The system needed three layers to solve these problems at once.
A data layer that ingests behavioural and buying signals from LinkedIn and the CRM. What reps search for. Who they add. Which deals they’re working. This data becomes the foundation for everything else. It answers Learning 01 because it’s verifiable. Real data from real sources.
An ML layer that learns patterns from that user history. Over time, it understands which prospects matter to each rep, which buying signals correlate with deals, what a good lead actually looks like for them specifically. This is how we solve Learning 02. Not by showing everything. By learning what matters and prioritising ruthlessly.
An AI layer that sits on top and adds context. Why this account? How to engage? What’s the right message? This is the layer that brings it all together and solves Learning 03. Instead of reps jumping between tools to answer these questions, the AI surfaces the answers directly.
The system needed three layers to solve these problems at once.
A data layer that ingests behavioural and buying signals from LinkedIn and the CRM. What reps search for. Who they add. Which deals they’re working. This data becomes the foundation for everything else. It answers Learning 01 because it’s verifiable. Real data from real sources.
An ML layer that learns patterns from that user history. Over time, it understands which prospects matter to each rep, which buying signals correlate with deals, what a good lead actually looks like for them specifically. This is how we solve Learning 02. Not by showing everything. By learning what matters and prioritising ruthlessly.
An AI layer that sits on top and adds context. Why this account? How to engage? What’s the right message? This is the layer that brings it all together and solves Learning 03. Instead of reps jumping between tools to answer these questions, the AI surfaces the answers directly.
What we built
What we built
What we built
Skill Analysis is a competency comparison tool that helps managers understand why employees succeed or struggle.
Horizon.
Horizon is an AI engine that learns a rep’s ideal customer profile (ICP) through both manual input and behavioural analysis. It watches what they search for, who they add to their CRM, which deals they’re working. The machine learning layer learns their pattern without them having to describe it every single time.
But Horizon isn’t just a recommendation engine. It’s designed to solve the three problems we found in discovery.
Horizon.
Horizon is an AI engine that learns a rep’s ideal customer profile (ICP) through both manual input and behavioural analysis. It watches what they search for, who they add to their CRM, which deals they’re working. The machine learning layer learns their pattern without them having to describe it every single time.
But Horizon isn’t just a recommendation engine. It’s designed to solve the three problems we found in discovery.
01
01
Compare high and low performers directly
Instead of generic scoring, we put top and bottom performers side-by-side. Managers saw immediately which competencies separated them. This created an instant benchmark. They could now see what "good" actually looked like.
02
02
Surface skill gaps as coaching opportunities
When a gap appeared, it became actionable. Managers could identify which competencies would have the biggest impact if developed. Coaching became strategic, not reactive.
We don’t show reps every possible lead. We surface the prioritized few. Each prospect comes with buying signals explaining why they matter right now. We rank recommendations based on buyer readiness and ICP fit. Reps spend their time on real prospects, not filtering noise.
03
03
Make data interpretation, not collection, the focus
We consolidated fragmented signals into one view. Instead of jumping between multiple reports, managers had one place to understand their team's profile, compare individuals, and plan development
The outcome
The outcome
The outcome
Skill Analysis became one of the platform's most valuable tools. Managers moved from reactive troubleshooting to proactive development.
They could identify competencies that drove high performance, benchmark employees against those standards, focus coaching efforts on what mattered, and have evidence-based conversations instead of gut-feeling ones.
Skill Analysis became one of the platform's most valuable tools. Managers moved from reactive troubleshooting to proactive development.
They could identify competencies that drove high performance, benchmark employees against those standards, focus coaching efforts on what mattered, and have evidence-based conversations instead of gut-feeling ones.
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Let’s have a conversation.
hello@nasrien.com
© 2026 Designed by Nasrien
•
Made with Figma and Framer.
Let’s have a conversation.
hello@nasrien.com
© 2026 Designed by Nasrien
•
Made with Figma and Framer.