Salesforce
Conversation Intelligence
Topics
AI, SaaS, CRM
My Role
Creative Director, Team Lead
Leveraging AI-driven insights from sales calls, the Conversation Intelligence platform empowers teams to optimize performance and close deals 2.5x more effectively.
Objective
Salesforce Conversation Intelligence was initially envisioned as a tool to extend the reach of Sales Managers tasked with monitoring sales reps' calls to provide feedback and coaching. By leveraging AI to analyze call recordings, the system could automatically provide call analysis and flag important events for follow-up. Over time, this vision evolved to empower all stakeholders by refining selling strategies and fostering a deeper understanding of the marketplace through insights derived from conversations across the entire sales organization.
Key Learnings
Highly Requested Features Do Not Guarantee Success even using AI
The emergence of AI was embraced by product teams as they believed it enabled them to finally deliver long-requested capabilities. However, upon the release of AI-supported features, teams observed that some highly requested items were not being utilized as anticipated. Additionally, the nuance required for the success of some features necessitated extended periods of observation and refinement.
This experience highlighted that teams must resist the temptation to prioritize the rapid rollout of new AI features without first understanding the impact of existing ones. Continuous monitoring, research, and iteration on recently launched capabilities are essential to deliver sustained value.
Every Company Has a Unique Language
Industries, and even individual companies, utilize unique lexicons often absent from general language libraries. For language transcription and analysis tools to be truly effective, they must adapt to learning the distinct terms and syntax of specific organizations. This adaptability ensures accurate transcription and meaningful analysis that aligns with the unique communication style of each client. By focusing on this customization, teams can maximize the utility of AI-driven solutions across diverse business environments.
Outcomes
Organizations that use Conversational Intelligence report deals are 2.5x more likely to close.
Multiple patents were granted for this work. I am listed on two.
Foundation
Our research revealed that measurable events during sales calls correlate with success. Using Natural Language Processing (NLP) and machine learning, the platform was designed to identify and analyze these key events.
A few examples include:
Cadence: Successful sales meetings typically required four cold call attempts over a 20-day period.
Talk Time: Reps spoke for an average of 44% of the call, often asking four targeted questions.
Monologues: Reps' longest monologues averaged 26 seconds, increasing to 35 seconds on successful calls.
Additionally, AI identified significant keywords (e.g., competitor or product names) and intentions (e.g., objections or information requests).
Design & Development
Conceptual Storytelling
The team began by creating sketches based on our customer's needs and our own brainstorming. We wove this into a compelling story focused on a sales manager reviewing insights into her team's sales calls. One of her raps, Wyatt, was in particular need of coaching.
​
These rough sketches proved invaluable meeting with stakeholders to describe the experience we were looking to create and the objectives we wanted to align around.
​
Wireframe Explorations
Soon the team acquired a technology partner that brough the technical stack necessary to support many of our ideas.:
-
These
High Fidelity Concept Designs and Prototypes were created for presentations with the C-Suite and announcements to the marketplace at Dreamforce.
​
Beta Launch for Phone Calls
The Call Coaching product was launched, capable of ingesting and analyzing phone call recordings. It provided managers and sales reps with actionable insights and event tags for performance review.
​
Product Iteration & Growth
Building on the success of Call Coaching, the team extended the platform to support video conferencing and dashboard of insights across teams.
My last involvement with Conversation Intelligence enabling it to surface across the ecosystem, such as in Slack and Starter Suite.
​
