Persona to patterns

Unlocked a unified understanding of warehouse managers’ workflows, influencing 3 roadmap priorities.

Project type

Persona System Development + AI Tooling

Project type

Persona System Development + AI Tooling

Project type

Persona System Development + AI Tooling

Contribution

Led persona research and GPT integration, conducted field visits, synthesized findings, developed frameworks, and enabled cross-team adoption.

Contribution

Led persona research and GPT integration, conducted field visits, synthesized findings, developed frameworks, and enabled cross-team adoption.

Contribution

Led persona research and GPT integration, conducted field visits, synthesized findings, developed frameworks, and enabled cross-team adoption.

Timeline

10 months

Timeline

10 months

Timeline

10 months

Stakeholders involved

Product Managers, Customer Success Managers, TAMs, UX Designer, Analytics Team, Leadership

Stakeholders involved

Product Managers, Customer Success Managers, TAMs, UX Designer, Analytics Team, Leadership

Stakeholders involved

Product Managers, Customer Success Managers, TAMs, UX Designer, Analytics Team, Leadership

Target User

Warehouse managers, riders, logistics ops managers, cold-chain supervisors

Target User

Warehouse managers, riders, logistics ops managers, cold-chain supervisors

Target User

Warehouse managers, riders, logistics ops managers, cold-chain supervisors

Problem Statement

The company relied on fragmented tribal knowledge and siloed personas, with no shared view of cross-product flows. Insights were undocumented, inconsistent across markets, and rarely shaped product decisions. In logistics, a generic “user” doesn’t work as the workflows shift with culture, infrastructure, and regulations. Without a scalable, evolving system of user understanding, products missed context, causing failed features, low adoption, frustrated clients, and costly rework.

Approach & Aim

Hypothesis

A structured persona system, enhanced with AI, can centralize tribal knowledge, surface gaps, and drive more equitable and effective product decisions.

Goal

To move from individual empathy to shared empathy across teams, ensuring personas reflect regional, cultural, and regulatory diversity and prevent costly misalignment caused by “universal personas.”

Scope

In scope: Persona creation, validation, tooling, adoption. Out of scope: Direct usability testing of product features.

Key Insights

  • 8 user personas documented, based on interviews + field visits, enriched with GPT-driven solutions for emerging use cases. Shifted company from fragmented tribal knowledge to shared, documented understanding.

  • Personas mapped into quadrants showing commonality, autonomy, and edge-case triggers. Introduced contextual disabilities (low signal, no desk, weather barriers).

  • Focus sessions with TAMs, CSMs, PMs validated personas in design critiques. Personas integrated into GPT tooling for document validation and use-case simulation.

  • Created Slack bot to distribute persona nudges and structured a persona catalog. Built persona-centric A/B testing framework → increased relevance of experiment learnings.

Impact

Business Outcome

Personas informed product restructuring for warehouse roles. Defined metrics enabled PMs/Analytics teams to prioritize high-impact enhancements. Created foundation for persona-index dashboard and AI persona generator.

Team Outcome

Designers used personas in early-stage critiques. PMs integrated persona insights into PRDs. Analytics aligned test design to persona buckets.

Stakeholder Outcome

Leadership gained visibility into underserved personas → guided roadmap prioritization. CSMs/TAMs used personas to frame customer conversations.

Methodologies

Research Category

Mixed Methods (Generative + Evaluative + Internal Validation)

Research Category

Mixed Methods (Generative + Evaluative + Internal Validation)

Research Category

Mixed Methods (Generative + Evaluative + Internal Validation)

Methodologies

Generative: 12 months of field visits, interviews, environmental/contextual inquiry. Evaluative: Focus groups, internal validation sessions with PMs/CSMs/TAMs, A/B testing framework. AI/Tooling: GPT-based persona creation, gap analysis, PRD embed mode, journey map generator.

Methodologies

Generative: 12 months of field visits, interviews, environmental/contextual inquiry. Evaluative: Focus groups, internal validation sessions with PMs/CSMs/TAMs, A/B testing framework. AI/Tooling: GPT-based persona creation, gap analysis, PRD embed mode, journey map generator.

Methodologies

Generative: 12 months of field visits, interviews, environmental/contextual inquiry. Evaluative: Focus groups, internal validation sessions with PMs/CSMs/TAMs, A/B testing framework. AI/Tooling: GPT-based persona creation, gap analysis, PRD embed mode, journey map generator.

Participants

Warehouse managers, logistics ops managers, riders, supervisors across multiple markets.

Participants

Warehouse managers, logistics ops managers, riders, supervisors across multiple markets.

Participants

Warehouse managers, logistics ops managers, riders, supervisors across multiple markets.

Geography / Languages

Multi-region (India, Southeast Asia, Latin America); multilingual interviews (English + local languages).

Geography / Languages

Multi-region (India, Southeast Asia, Latin America); multilingual interviews (English + local languages).

Geography / Languages

Multi-region (India, Southeast Asia, Latin America); multilingual interviews (English + local languages).

Data Analysis

Archetype grouping, quadrant mapping, thematic coding, AI-assisted summarization of field visits, synthesis workshops.

Data Analysis

Archetype grouping, quadrant mapping, thematic coding, AI-assisted summarization of field visits, synthesis workshops.

Data Analysis

Archetype grouping, quadrant mapping, thematic coding, AI-assisted summarization of field visits, synthesis workshops.