Sales Insight
Creating a space for actionable sales and business insights that drive impact.
(Domain)
Analytics
(Year)
2025
(Services)
Product Design
About
The Product
Bridging customer feedback with business intelligence to unlock sales insights and revenue impact.
A feedback intelligence platform that turns customer voices from product telemetry, support, reviews, and call transcripts into actionable insights. Feedback is structured into a taxonomy of keywords and themes, surfaced through search, dashboards, and quantitative tools.
As customer needs evolved, the flat keyword/reason model couldn’t support business-scale feedback analysis. The Sales Insights initiative introduced two core systems, a Knowledge Graph (linking Feedback, Users, Accounts, Opportunities, Products, and Stores) and an Adaptive Taxonomy (multi-level Keywords, Themes/Sub-themes, and Intent Categories). Together, they power revenue-driven analysis like Win/Loss attribution, account-level trends, and product-impact quantification.
Context
& Problem
As usage scaled, recurring platform-level constraints surfaced:
Limited scalability for business context: Manual taxonomy upkeep and flat structure restricted visibility into sales or account-level impact. Users relied on metadata filters as workarounds, which surfaced only customer-voice insights.
Flat data model blocking relational insights: Metadata was stored as flat properties, preventing users from mapping relationships (e.g., multiple opportunities under an account). This limited cross-object analysis and revenue-focused queries.
Unaligned, high-maintenance taxonomy: The legacy keyword/reason setup led to overlapping labels, inconsistent granularity, and heavy manual cleanup, diverting teams from insight generation.
These constraints limited the platform’s ability to deliver sales-oriented insights, such as revenue impact, account trends, and deal-level signals, while increasing time-to-insight for customer-facing teams.
Goals
Based on user feedback and evolving business needs, the Sales Insight initiative aimed to bridge customer voice with sales performance through scalable, business-aligned, and actionable insights.
Enable cross-object insights: Link Feedback, Accounts, Opportunities, and Products to uncover revenue-impact correlations and overall product health.
Integrate business-aligned predictions: Replace flat keywords/reasons with an Adaptive Taxonomy (L1 <> L3; Themes <> Sub-themes) for deeper, contextual insights.
Support sales report consumption: Introduce dedicated views for tracking deals, account trends, and business requirements.
Initial Research
We began by auditing customer tickets, VoC interviews, and analytics to uncover recurring needs around connecting customer feedback with business and sales impact.
Key observations:
Users built queries using sales or business-related metadata to infer revenue-linked insights, a workaround due to missing native visibility.
Many tracked such records manually or used external analytics tools since the platform only quantified data by feedback volume, not business value.
A competitive benchmark of analytics and feedback platforms revealed that progressive disclosure models (tree structures or breadcrumb trails) were standard for navigating business insights and understanding cross-object relationships.
These findings validated the core issues, limited correlation between feedback and business metrics, and reinforced the need to design a scalable, interconnected system for sales insights and revenue analysis.
Lo-Fi Design
I explored two primary approaches for visualizing the Knowledge Graph:
Structured taxonomy extension, expanding the existing hierarchy with a dedicated Sales Insights tab, showing how business and sales fields connect within the taxonomy.
Free-form graphical view, a visual exploration model that allowed users to navigate interconnected insights more intuitively, linking themes, sub-themes, and sentiments to business outcomes.
Additionally, I designed an early concept for a Sales Reports section, enabling users to browse and consume deal-level insights more easily.
Internal and external testing showed strong interest in the Knowledge Graph concept but mixed feedback on usability and contextual clarity. As a result, the team decided to prioritize improving core sales insight workflows, querying, analysis, and insight consumption, before re-scoping the Knowledge Graph as a standalone future initiative.
Hi-Fi Design
High-fidelity prototypes focused on integrating sales and business intelligence across core product areas:
Pulse: A dedicated space to browse and consume sales/business reports, allowing users to dive into deals, view business-driven predictions (use cases, win/loss insights, risks), and access supporting feedback records for deeper context.
Updated widgets: Introduced new visual components to quantify and display sales and business metadata, surfacing key metrics and trends.
Dashboard templates: Designed business-focused dashboards highlighting organization health, top/bottom performers, and key insights requiring attention.
Alpha testing with internal teams and pilot customers validated improved hierarchy clarity and strong demand for sales insights. These prototypes were launched in early access, forming the foundation for the product’s sales insight pivot and future roadmap validation.
Outcome
The initial release received strong positive feedback, giving users a direct and intuitive way to access sales and business insights. It also helped in validating the future scope for a progressive, hierarchical knowledge graph, enabling deeper exploration through linked themes, sub-themes, reports, and feedback.
The feature drove tangible business outcomes, helping close 3–4 new customer deals, renew 5–6 existing contracts, and played a key role in securing the company’s $20M Series A fundraising.
Takeaway
This was my first project leading a product-wide pivot, helping me learn how to design for platform-level change while balancing ideal experiences with delivery constraints. It emphasized the value of strategic validation, phased releases, and iterative scaling for complex, high-impact initiatives.
What worked: Building on the existing customer voice framework made it easier to extend familiar patterns into sales insights, allowing users to quickly adapt to new reports and business-driven predictions.
What could have been better: Tight timelines meant prioritizing a functional foundation over deeper validation. As a result, early Knowledge Graph iterations lacked adoption. Spending more time upfront with users to map existing sales insight workflows and workarounds could have led to a more aligned and scalable solution.
Sales Insight
Creating a space for actionable sales and business insights that drive impact.
(Domain)
Analytics
(Year)
2025
(Services)
Product Design
About
The Product
Bridging customer feedback with business intelligence to unlock sales insights and revenue impact.
A feedback intelligence platform that turns customer voices from product telemetry, support, reviews, and call transcripts into actionable insights. Feedback is structured into a taxonomy of keywords and themes, surfaced through search, dashboards, and quantitative tools.
As customer needs evolved, the flat keyword/reason model couldn’t support business-scale feedback analysis. The Sales Insights initiative introduced two core systems, a Knowledge Graph (linking Feedback, Users, Accounts, Opportunities, Products, and Stores) and an Adaptive Taxonomy (multi-level Keywords, Themes/Sub-themes, and Intent Categories). Together, they power revenue-driven analysis like Win/Loss attribution, account-level trends, and product-impact quantification.
Context
& Problem
As usage scaled, recurring platform-level constraints surfaced:
Limited scalability for business context: Manual taxonomy upkeep and flat structure restricted visibility into sales or account-level impact. Users relied on metadata filters as workarounds, which surfaced only customer-voice insights.
Flat data model blocking relational insights: Metadata was stored as flat properties, preventing users from mapping relationships (e.g., multiple opportunities under an account). This limited cross-object analysis and revenue-focused queries.
Unaligned, high-maintenance taxonomy: The legacy keyword/reason setup led to overlapping labels, inconsistent granularity, and heavy manual cleanup, diverting teams from insight generation.
These constraints limited the platform’s ability to deliver sales-oriented insights, such as revenue impact, account trends, and deal-level signals, while increasing time-to-insight for customer-facing teams.
Goals
Based on user feedback and evolving business needs, the Sales Insight initiative aimed to bridge customer voice with sales performance through scalable, business-aligned, and actionable insights.
Enable cross-object insights: Link Feedback, Accounts, Opportunities, and Products to uncover revenue-impact correlations and overall product health.
Integrate business-aligned predictions: Replace flat keywords/reasons with an Adaptive Taxonomy (L1 <> L3; Themes <> Sub-themes) for deeper, contextual insights.
Support sales report consumption: Introduce dedicated views for tracking deals, account trends, and business requirements.
Initial Research
We began by auditing customer tickets, VoC interviews, and analytics to uncover recurring needs around connecting customer feedback with business and sales impact.
Key observations:
Users built queries using sales or business-related metadata to infer revenue-linked insights, a workaround due to missing native visibility.
Many tracked such records manually or used external analytics tools since the platform only quantified data by feedback volume, not business value.
A competitive benchmark of analytics and feedback platforms revealed that progressive disclosure models (tree structures or breadcrumb trails) were standard for navigating business insights and understanding cross-object relationships.
These findings validated the core issues, limited correlation between feedback and business metrics, and reinforced the need to design a scalable, interconnected system for sales insights and revenue analysis.
Lo-Fi Design
I explored two primary approaches for visualizing the Knowledge Graph:
Structured taxonomy extension, expanding the existing hierarchy with a dedicated Sales Insights tab, showing how business and sales fields connect within the taxonomy.
Free-form graphical view, a visual exploration model that allowed users to navigate interconnected insights more intuitively, linking themes, sub-themes, and sentiments to business outcomes.
Additionally, I designed an early concept for a Sales Reports section, enabling users to browse and consume deal-level insights more easily.
Internal and external testing showed strong interest in the Knowledge Graph concept but mixed feedback on usability and contextual clarity. As a result, the team decided to prioritize improving core sales insight workflows, querying, analysis, and insight consumption, before re-scoping the Knowledge Graph as a standalone future initiative.
Hi-Fi Design
High-fidelity prototypes focused on integrating sales and business intelligence across core product areas:
Pulse: A dedicated space to browse and consume sales/business reports, allowing users to dive into deals, view business-driven predictions (use cases, win/loss insights, risks), and access supporting feedback records for deeper context.
Updated widgets: Introduced new visual components to quantify and display sales and business metadata, surfacing key metrics and trends.
Dashboard templates: Designed business-focused dashboards highlighting organization health, top/bottom performers, and key insights requiring attention.
Alpha testing with internal teams and pilot customers validated improved hierarchy clarity and strong demand for sales insights. These prototypes were launched in early access, forming the foundation for the product’s sales insight pivot and future roadmap validation.
Outcome
The initial release received strong positive feedback, giving users a direct and intuitive way to access sales and business insights. It also helped in validating the future scope for a progressive, hierarchical knowledge graph, enabling deeper exploration through linked themes, sub-themes, reports, and feedback.
The feature drove tangible business outcomes, helping close 3–4 new customer deals, renew 5–6 existing contracts, and played a key role in securing the company’s $20M Series A fundraising.
Takeaway
This was my first project leading a product-wide pivot, helping me learn how to design for platform-level change while balancing ideal experiences with delivery constraints. It emphasized the value of strategic validation, phased releases, and iterative scaling for complex, high-impact initiatives.
What worked: Building on the existing customer voice framework made it easier to extend familiar patterns into sales insights, allowing users to quickly adapt to new reports and business-driven predictions.
What could have been better: Tight timelines meant prioritizing a functional foundation over deeper validation. As a result, early Knowledge Graph iterations lacked adoption. Spending more time upfront with users to map existing sales insight workflows and workarounds could have led to a more aligned and scalable solution.
Sales Insight
Creating a space for actionable sales and business insights that drive impact.
(Domain)
Analytics
(Year)
2025
(Services)
Product Design
About
The Product
Bridging customer feedback with business intelligence to unlock sales insights and revenue impact.
A feedback intelligence platform that turns customer voices from product telemetry, support, reviews, and call transcripts into actionable insights. Feedback is structured into a taxonomy of keywords and themes, surfaced through search, dashboards, and quantitative tools.
As customer needs evolved, the flat keyword/reason model couldn’t support business-scale feedback analysis. The Sales Insights initiative introduced two core systems, a Knowledge Graph (linking Feedback, Users, Accounts, Opportunities, Products, and Stores) and an Adaptive Taxonomy (multi-level Keywords, Themes/Sub-themes, and Intent Categories). Together, they power revenue-driven analysis like Win/Loss attribution, account-level trends, and product-impact quantification.
Context
& Problem
As usage scaled, recurring platform-level constraints surfaced:
Limited scalability for business context: Manual taxonomy upkeep and flat structure restricted visibility into sales or account-level impact. Users relied on metadata filters as workarounds, which surfaced only customer-voice insights.
Flat data model blocking relational insights: Metadata was stored as flat properties, preventing users from mapping relationships (e.g., multiple opportunities under an account). This limited cross-object analysis and revenue-focused queries.
Unaligned, high-maintenance taxonomy: The legacy keyword/reason setup led to overlapping labels, inconsistent granularity, and heavy manual cleanup, diverting teams from insight generation.
These constraints limited the platform’s ability to deliver sales-oriented insights, such as revenue impact, account trends, and deal-level signals, while increasing time-to-insight for customer-facing teams.
Goals
Based on user feedback and evolving business needs, the Sales Insight initiative aimed to bridge customer voice with sales performance through scalable, business-aligned, and actionable insights.
Enable cross-object insights: Link Feedback, Accounts, Opportunities, and Products to uncover revenue-impact correlations and overall product health.
Integrate business-aligned predictions: Replace flat keywords/reasons with an Adaptive Taxonomy (L1 <> L3; Themes <> Sub-themes) for deeper, contextual insights.
Support sales report consumption: Introduce dedicated views for tracking deals, account trends, and business requirements.
Initial Research
We began by auditing customer tickets, VoC interviews, and analytics to uncover recurring needs around connecting customer feedback with business and sales impact.
Key observations:
Users built queries using sales or business-related metadata to infer revenue-linked insights, a workaround due to missing native visibility.
Many tracked such records manually or used external analytics tools since the platform only quantified data by feedback volume, not business value.
A competitive benchmark of analytics and feedback platforms revealed that progressive disclosure models (tree structures or breadcrumb trails) were standard for navigating business insights and understanding cross-object relationships.
These findings validated the core issues, limited correlation between feedback and business metrics, and reinforced the need to design a scalable, interconnected system for sales insights and revenue analysis.
Lo-Fi Design
I explored two primary approaches for visualizing the Knowledge Graph:
Structured taxonomy extension, expanding the existing hierarchy with a dedicated Sales Insights tab, showing how business and sales fields connect within the taxonomy.
Free-form graphical view, a visual exploration model that allowed users to navigate interconnected insights more intuitively, linking themes, sub-themes, and sentiments to business outcomes.
Additionally, I designed an early concept for a Sales Reports section, enabling users to browse and consume deal-level insights more easily.
Internal and external testing showed strong interest in the Knowledge Graph concept but mixed feedback on usability and contextual clarity. As a result, the team decided to prioritize improving core sales insight workflows, querying, analysis, and insight consumption, before re-scoping the Knowledge Graph as a standalone future initiative.
Hi-Fi Design
High-fidelity prototypes focused on integrating sales and business intelligence across core product areas:
Pulse: A dedicated space to browse and consume sales/business reports, allowing users to dive into deals, view business-driven predictions (use cases, win/loss insights, risks), and access supporting feedback records for deeper context.
Updated widgets: Introduced new visual components to quantify and display sales and business metadata, surfacing key metrics and trends.
Dashboard templates: Designed business-focused dashboards highlighting organization health, top/bottom performers, and key insights requiring attention.
Alpha testing with internal teams and pilot customers validated improved hierarchy clarity and strong demand for sales insights. These prototypes were launched in early access, forming the foundation for the product’s sales insight pivot and future roadmap validation.
Outcome
The initial release received strong positive feedback, giving users a direct and intuitive way to access sales and business insights. It also helped in validating the future scope for a progressive, hierarchical knowledge graph, enabling deeper exploration through linked themes, sub-themes, reports, and feedback.
The feature drove tangible business outcomes, helping close 3–4 new customer deals, renew 5–6 existing contracts, and played a key role in securing the company’s $20M Series A fundraising.
Takeaway
This was my first project leading a product-wide pivot, helping me learn how to design for platform-level change while balancing ideal experiences with delivery constraints. It emphasized the value of strategic validation, phased releases, and iterative scaling for complex, high-impact initiatives.
What worked: Building on the existing customer voice framework made it easier to extend familiar patterns into sales insights, allowing users to quickly adapt to new reports and business-driven predictions.
What could have been better: Tight timelines meant prioritizing a functional foundation over deeper validation. As a result, early Knowledge Graph iterations lacked adoption. Spending more time upfront with users to map existing sales insight workflows and workarounds could have led to a more aligned and scalable solution.