Competitive Win and Loss Stories

The Competitive Win and Loss Stories feature transforms the manual, time-intensive process of documenting deal outcomes into an automated system that generates contextual insights at scale. By leveraging existing call recordings, CRM data, and competitive intelligence, this capability delivers actionable win/loss narratives that equip sales teams with peer-validated strategies for handling objections and positioning in competitive scenarios.

My Role

This project was handed over to me as a takeover when the original Product Manager departed, requiring me to step in mid-flight during the technical breakdown and scope definition phase. While we already knew that win and loss stories existed as manual deliverables that clients and sales teams created regularly, no systematic analysis had been conducted to understand what made these stories effective.

To properly scope the automation opportunity, I conducted supplementary research analyzing existing user behaviour within the application. I systematically analyzed every piece of content published by current clients, distilling the key structural components that constituted successful win/loss stories based on recurring themes in highly trafficked content. Focusing specifically on the qualities of cards with the highest engagement levels, I translated these insights into default templates that would drive the automated generation process. From there, I collaborated with the engineering team to craft templates and supporting prompts, productized the end-to-end experience, and established criteria for content publishing, tagging, and notifications while managing the beta and initial client adoption phases.

Objectives

Our primary objective was to automate insight generation at scale by identifying existing manual, time-consuming user behaviours and creating end-to-end automation that would deliver incremental value while freeing clients to focus on higher-value activities. We centred our approach around a core seller problem hypothesis:

  • Seller Problem Validation: Sellers frequently encounter unfamiliar situations and objections, typically relying on inconsistent peer consultation while lacking systematic access to historical knowledge and proven positioning strategies.

  • Value Creation: Rather than simply telling sellers why deals were won, we focused on connecting the dots between buyer needs and successful positioning strategies, providing actionable guidance for narrative development with prospects.

  • Systematic Knowledge Capture: Transform ad-hoc, tribal knowledge into systematically captured, easily accessible insights that leverage the peer validation that sellers value most.

Outcomes

The feature exceeded expectations and generated significant client excitement within the first two months of launch:

  • Generated such strong market interest that we eventually had to limit access to alpha and beta cohorts, indicating significant product-market fit and demand for automated competitive intelligence.

  • Achieved adoption across 37 companies in the initial two-month period, with an overwhelming positive response, creating a substantial backlog of beta trial requests.

  • Published over 600 new stories to client spaces within two months, demonstrating the scale of automated content generation capabilities while maintaining high-quality standards with a 90% customer satisfaction rate and 86% accuracy rate out of the box.

  • Successfully integrated content into our RAG-powered in-app research chatbot, extending the value of generated stories across the broader platform experience.

  • Accomplished these results using minimal resources—a small 'ant' team of two AI engineers and me over two quarters, demonstrating efficient execution and strong ROI on development investment.

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