Coaching Dashboard 2.0
Coaching Dashboard 2.0
Coaching Dashboard 2.0
An AI-enhanced dashboard built to amplify coaching effectiveness and simplify session management.
An AI-enhanced dashboard built to amplify coaching effectiveness and simplify session management.
An AI-enhanced dashboard built to amplify coaching effectiveness and simplify session management.
B2B SaaS | AI powered Coaching & Assessments
Product Designer
2025
Convin
TL;DR
Led the end-to-end design of the Coaching Dashboard 2.0 used by sales and support teams.
Replaced a noisy, one-size-fits-all dashboard with role-based experiences for Stakeholders, Managers, and Agents.
Designed the foundational UX architecture to support the introduction of AI coaching alongside manual workflows.
Post-revamp, clients like Traya, Khatabook, Housing, and AVFL actively use the dashboard to track performance and assign assessments.
Achieved 70%+ increase in assessment completion rates and faster “time to insight” for managers.
Led the end-to-end design of the Coaching Dashboard 2.0 used by sales and support teams.
Replaced a noisy, one-size-fits-all dashboard with role-based experiences for Stakeholders, Managers, and Agents.
Designed the foundational UX architecture to support the introduction of AI coaching alongside manual workflows.
Post-revamp, clients like Traya, Khatabook, Housing, and AVFL actively use the dashboard to track performance and assign assessments.
Achieved 70%+ increase in assessment completion rates and faster “time to insight” for managers.
OVERVIEW
In high-performance sales and support environments, coaching is often deprioritized, not due to lack of intent, but due to poor visibility and fragmented data. Without clear visibility into team performances over varying timeframes, managers struggle to identify trends and take meaningful action.
Coaching Dashboard 2.0 is a comprehensive redesign of Convin’s coaching analytics experience, built to support managers, team leads, and agents across different hierarchy levels.
Beyond enhancing the existing dashboard structure, this version introduced AI Coaching - a new capability that brought automated session tracking, parameter-level analysis, and AI-driven performance insights into the coaching workflow for the first time.
The project focused on designing a structured, multi-level dashboard system along with session tracking, performance metrics, leaderboard logic, and AI-based coaching analysis. This required translating complex session types, role-based permissions, and newly introduced AI logic into a scalable, insight-driven dashboard architecture.
In high-performance sales and support environments, coaching is often deprioritized, not due to lack of intent, but due to poor visibility and fragmented data. Without clear visibility into team performances over varying timeframes, managers struggle to identify trends and take meaningful action.
Coaching Dashboard 2.0 is a comprehensive redesign of Convin’s coaching analytics experience, built to support managers, team leads, and agents across different hierarchy levels.
Beyond enhancing the existing dashboard structure, this version introduced AI Coaching - a new capability that brought automated session tracking, parameter-level analysis, and AI-driven performance insights into the coaching workflow for the first time.
The project focused on designing a structured, multi-level dashboard system along with session tracking, performance metrics, leaderboard logic, and AI-based coaching analysis. This required translating complex session types, role-based permissions, and newly introduced AI logic into a scalable, insight-driven dashboard architecture.
Iterated layouts and empty states
THE CHALLENGE
The existing dashboard attempted to serve all users through a single experience and failed to serve any of them well.
Core Problems
No hierarchy between strategic insights and operational tasks
Managers had to interpret raw metrics to infer action
Agents were exposed to analytics they couldn’t act on
The structure could not scale to support upcoming AI-driven coaching
The existing dashboard attempted to serve all users through a single experience and failed to serve any of them well.
Core Problems
No hierarchy between strategic insights and operational tasks
Managers had to interpret raw metrics to infer action
Agents were exposed to analytics they couldn’t act on
The structure could not scale to support upcoming AI-driven coaching
RESEARCH & STRATEGY
To understand how coaching was currently being used, I worked closely with Product Managers and the Customer Success team who regularly interacted with managers and agents.
The research focused on:
How managers tracked coaching progress across teams
Where visibility gaps existed in the current dashboard
How session types (manual coaching, assessments) were being used
What metrics actually influenced performance conversations
How permissions and role hierarchies impacted dashboard access
My learnings:
Managers needed both a macro view (multi-team performance) and micro insights (rep-level breakdowns) without switching contexts
Coaching data was fragmented across views and lacked hierarchy-based clarity
The current dashboard did not support trend analysis over time.
Additionally, the introduction of AI Coaching required defining how automated insights would integrate naturally into existing coaching workflows rather than feel like a separate feature.
To understand how coaching was currently being used, I worked closely with Product Managers and the Customer Success team who regularly interacted with managers and agents.
The research focused on:
How managers tracked coaching progress across teams
Where visibility gaps existed in the current dashboard
How session types (manual coaching, assessments) were being used
What metrics actually influenced performance conversations
How permissions and role hierarchies impacted dashboard access
My learnings:
Managers needed both a macro view (multi-team performance) and micro insights (rep-level breakdowns) without switching contexts
Coaching data was fragmented across views and lacked hierarchy-based clarity
The current dashboard did not support trend analysis over time.
Additionally, the introduction of AI Coaching required defining how automated insights would integrate naturally into existing coaching workflows rather than feel like a separate feature.
PROCESS & EXPLORATION
The strategy centered around three principles:
1. Hierarchy-Based Architecture
Design a layered dashboard system — Multi-Team, Team, and Rep — aligned with organizational structures and decision-making levels.
2. Structured Performance Visibility
Standardize metrics (completion %, overdue logic, assessment scores) to create a consistent and measurable coaching framework.
3. Integrated AI Insights
Embed AI-driven parameter analysis within the dashboard flow so that automated insights enhance — not complicate — managerial oversight.
The goal was to transform coaching from a reactive task into a measurable, trackable performance system.
The strategy centered around three principles:
1. Hierarchy-Based Architecture
Design a layered dashboard system — Multi-Team, Team, and Rep — aligned with organizational structures and decision-making levels.
2. Structured Performance Visibility
Standardize metrics (completion %, overdue logic, assessment scores) to create a consistent and measurable coaching framework.
3. Integrated AI Insights
Embed AI-driven parameter analysis within the dashboard flow so that automated insights enhance — not complicate — managerial oversight.
The goal was to transform coaching from a reactive task into a measurable, trackable performance system.
Existing Coaching Dashboard Design
1
1
The dashboard lacked segmentation across user roles, presenting the same view regardless of user profile.
The dashboard lacked segmentation across user roles, presenting the same view regardless of user profile.
2
2
Filter Placement Feels Detached. Filters were limited and poorly placed, missing key controls like session type and agent status.
Filter Placement Feels Detached. Filters were limited and poorly placed, missing key controls like session type and agent status.
3
3
The dashboard lacked strong, action-oriented CTAs for managers, with limited pathways to drill deeper into performance trends or investigate specific issues.
The dashboard lacked strong, action-oriented CTAs for managers, with limited pathways to drill deeper into performance trends or investigate specific issues.
4
4
The leaderboard is data heavy displaying eight metrics per row, increasing cognitive load and reducing scannability.
The leaderboard is data heavy displaying eight metrics per row, increasing cognitive load and reducing scannability.
5
5
The color system lacked semantic consistency, making visual cues feel decorative rather than functional.
There was no clear visual hierarchy, with headers, filters, stats, and leaderboard competing for attention.
The color system lacked semantic consistency, making visual cues feel decorative rather than functional.
There was no clear visual hierarchy, with headers, filters, stats, and leaderboard competing for attention.
Existing Coaching Dashboard Design
1
The dashboard lacked segmentation across user roles, presenting the same view regardless of user profile.
2
Filter Placement Feels Detached. Filters were limited and poorly placed, missing key controls like session type and agent status.
3
The dashboard lacked strong, action-oriented CTAs for managers, with limited pathways to drill deeper into performance trends or investigate specific issues.
4
The leaderboard is data heavy displaying eight metrics per row, increasing cognitive load and reducing scannability.
5
The color system lacked semantic consistency, making visual cues feel decorative rather than functional.
There was no clear visual hierarchy, with headers, filters, stats, and leaderboard competing for attention.
1
There is no manner to filter out the assigned session, session types, teams etc.
Also, repetitive, text-heavy rows reduce scannability and create scroll fatigue, especially when session titles are similar.
2
The interface lacks clear visual hierarchy, making it difficult to distinguish primary information from supporting details. Important managerial actions are hidden within secondary menus, reducing discoverability and workflow efficiency.
3
Key performance indicators, such as completion percentage, lack strong visual emphasis and urgency signaling. The layout feels like a static session log rather than a prioritized, action-oriented performance management tool.
1
There is no manner to filter out the assigned session, session types, teams etc.
Also, repetitive, text-heavy rows reduce scannability and create scroll fatigue, especially when session titles are similar.
2
The interface lacks clear visual hierarchy, making it difficult to distinguish primary information from supporting details. Important managerial actions are hidden within secondary menus, reducing discoverability and workflow efficiency.
3
Key performance indicators, such as completion percentage, lack strong visual emphasis and urgency signaling. The layout feels like a static session log rather than a prioritized, action-oriented performance management tool.
THE SOLUTION
Replaced the flat dashboard with a three-level system, aligning information depth with user responsibility.
Dashboard Pillars
Stats – Strategic insights
Assigned Sessions – Operational control
My Tasks – Individual execution
Replaced the flat dashboard with a three-level system, aligning information depth with user responsibility.
Dashboard Pillars
Stats – Strategic insights
Assigned Sessions – Operational control
My Tasks – Individual execution
Coaching Dashboard 2.0
My Tasks: AI Coaching sessions
DESIGN BREAKDOWN
Multi-Team & Team Insights
Designed for Directors and Managers to detect patterns quickly.
Global filters (Team, Rep, Date, Status) update all views dynamically
Aggregated AI-flagged parameters surface recurring skill gaps
Dual-metric leaderboards allow ranking by Completion % or Assessment Score
Multi-Team & Team Insights
Designed for Directors and Managers to detect patterns quickly.
Global filters (Team, Rep, Date, Status) update all views dynamically
Aggregated AI-flagged parameters surface recurring skill gaps
Dual-metric leaderboards allow ranking by Completion % or Assessment Score
2. Assigned Sessions (Management Hub)
A centralized command center for Team Leads.
Clear separation between “Assigned by Me” and “Assigned by All”
Drill-down analytics showing completion states across teams
Reduced dependency on manual follow-ups
2. Assigned Sessions (Management Hub)
A centralized command center for Team Leads.
Clear separation between “Assigned by Me” and “Assigned by All”
Drill-down analytics showing completion states across teams
Reduced dependency on manual follow-ups
3. My Tasks (Agent Experience)
Designed as a focus mode, not a dashboard.
Active: In-progress and new sessions prioritized
Overdue: High-visibility alerts without notification fatigue
Completed: A record of reviewed feedback and progress
Agents always know what to do next nothing more, nothing less.
3. My Tasks (Agent Experience)
Designed as a focus mode, not a dashboard.
Active: In-progress and new sessions prioritized
Overdue: High-visibility alerts without notification fatigue
Completed: A record of reviewed feedback and progress
Agents always know what to do next nothing more, nothing less.
Assigned Session Tab
Detailed team level performance view of a single coaching session
IMPACT
Adoption & Usage
Actively used by clients such as Traya, Khatabook, Housing, and AVFL
Managers regularly rely on the dashboard to track performance and assign assessments
Became the primary surface for coaching-related decisions
Engagement & Effectiveness
70%+ assessment completion rate post-revamp
Higher follow-through due to clearer task prioritization
Qualitative Signals
CSMs reported significantly reduced time spent identifying underperforming agents
Faster “time to insight” during reviews
Adoption & Usage
Actively used by clients such as Traya, Khatabook, Housing, and AVFL
Managers regularly rely on the dashboard to track performance and assign assessments
Became the primary surface for coaching-related decisions
Engagement & Effectiveness
70%+ assessment completion rate post-revamp
Higher follow-through due to clearer task prioritization
Qualitative Signals
CSMs reported significantly reduced time spent identifying underperforming agents
Faster “time to insight” during reviews
project type
B2B SaaS | AI powered Coaching & Assessments
my role
Product Designer
Company
Convin
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