What is Salesken’s Post Call Analytics (PCA)?
Post Call Analytics (PCA) is a conversation intelligence platform that transforms customer conversations into actionable business insights. It analyzes calls, meetings and other multi-participant discussions to generate coaching recommendations, quality evaluations, customer intelligence, revenue insights, and operational metrics.
My role
Lead Product Designer
2023–2024
Teams
Product + Design + Engineering + QA
Problem
Organizations conduct thousands of customer conversations every month, but most of that knowledge remains trapped inside recordings and transcripts. Teams lacked a scalable way to transform conversations into actionable insights for coaching, quality assurance, customer intelligence, and revenue operations.
Outcome
Designed a configurable conversation intelligence platform that transformed customer conversations into actionable business intelligence. The system enabled organizations to analyze calls, automate evaluations, generate coaching insights, and surface revenue and customer trends through AI-powered workflows.
90%
Competitive Parity
~30%
User Base Growth
22
New Features Shipped
99%
Intent Coverage
Business Impact after deployed
~80%
Faster QA Reviews
75%
​Client Satisfaction
71%
Improved Lead Qualification
60%
Faster Market Adaptation
Customer Impact after deployed
So the system needed to answer some questions
When should recommendations appear? | What information deserves attention? | How much information is too much? | When should AI stay silent? | How do we build trust in AI recommendations?
This turns the problem from interface design into attention and decision-support design.
The user’s Core problem.
Traditional workflows forced users to switch between multiple tools, documents, and dashboards while managing conversations in real time.
1. Listen actively
2. Build rapport
3. Follow process requirements
4. Handle objections
5. Capture information
6. Search for relevant resources
Product Design
Workflow Architecture
AI Interaction Design
Design System
Engineering Collaboration
Feature Design
​Edge Case Solutions
What was my contribution?
Understanding the Workflow:
A system designed to help sales representatives navigate conversations, surface relevant information, and receive contextual recommendations without disrupting the flow of a live call during the live call.
The system interacted with : QA/PCA | CRM Integrations | Admin Systems | Desktop Applications | Mobile Applications
Design decisions needed to support the broader ecosystem while remaining useful during live conversations.
Reduce Cognitive Load
Information must be scannable in seconds.
Build Trust Gradually
AI recommendations should feel assistive rather than intrusive.
Prioritize Actions Over Data
Representatives need guidance, not analytics.
Surface Information Only When Necessary
Users should never feel overwhelmed by recommendations.
Design Principles
Key Design Decisions:
Decision 01
Progressive Information Disclosure
Rather than showing everything immediately, information was revealed based on context and urgency.
Decision 02
Recommendation Prioritization
High-value recommendations received stronger visual emphasis while lower-priority insights remained available without competing for attention.
Decision 03
Workflow Consistency
Interaction patterns were standardized across multiple RTC experiences to reduce learning effort.
Outcomes
AI-assisted sales conversations | Reduced workflow fragmentation | Faster access to relevant information | More consistent user experiences | Improved integration with the wider Salesken ecosystem
Key Takeaway
Designing RTC reinforced a simple lesson: The most difficult challenge in AI products is not generating intelligence. It is deciding when, where, and how that intelligence should appear within a user's workflow.
Would you like to know more?
These projects offer just a glimpse into the creative process behind them, not the case studies. To learn more, feel free to drop me an email. I'm usually quick to respond, typically within a day.