User Interviews & Analytics

I executed extensive, multi-industry User Research and Analytics (Voice-of-Customer) to diagnose critical engagement and data quality friction points within our users-facing product suite. My research focused on understanding users reluctance, usability flaws, and unmet needs across core modules like Assessments, Risk Scoring, Disruption Management, and AI agents.

The Vision

My vision was to strategically transform the user experience by eliminating known usability barriers and automating painful data management processes. I aimed to dramatically increase user self-service adoption and response rates, thereby achieving a step-change in the quality and completeness of supply chain risk data used by our customers.

The PM Approach & Analytics

My PM approach for this research project was rooted in a rigorous, data-driven validation framework that I designed to translate user pain points into an actionable strategy. I first conducted high-volume qualitative interviews to surface latent issues, which I immediately cross-referenced with quantitative platform analytics (like drop-off rates and completion percentages) to validate and quantify the severity of every problem. This systematic analysis, categorized across the three core themes of Usability, Engagement, and Data Friction, allowed me to isolate universal friction points versus industry-specific anomalies. Ultimately, this comprehensive process delivered a prioritized, data-backed roadmap, ensuring resource allocation was exclusively focused on correcting the highest-impact UX/UI flaws and addressing the critical need for data automation (such as automated document parsing). A summary of the findings are as below:

Key Stakeholder Collaborations & Discussions

I utilized these severe usability and engagement findings to drive high-impact discussions with Executive Leadership, Customer Success, and Engineering.

Executive Leadership Impact & Discussion: I translated the users usability issues into clear financial and strategic risk, aligning the need for feature investment with core business goals.
- Risk to Data Quality: I presented the findings showing that low user engagement (e.g., reluctance to complete assessments, attachment issues) directly compromises the core data quality promise of our platform, posing a direct threat to customer renewal rates and satisfaction.
- Strategic Investment Justification: I justified the prioritization of data automation projects (like the AI Parser) by quantifying the manual labor savings for user. My pitch was: "Investing in this AI will solve the users pain and unlock our customers' value, ensuring our platform remains competitive."
- Pivot to Incentivization: I secured executive buy-in to move R&D efforts away from solely demanding data to building user incentives (e.g., lite version of the core product post-submission ratings and feedback) to ensure the long-term viability of our user network strategy.

Customer Success & Sales Impact & Discussion: I equipped these teams with context to manage customer expectations and defined new success metrics based on friction reduction.
- Proactive Customer Management: I informed Customer Success that the current visibility mapping latency and Assessment friction were directly contributing to low data completion rates. This enabled them to proactively address customer complaints about incomplete data and manage expectations regarding platform performance.
- Defining Success Metrics: I partnered with CS to redefine internal success. Instead of just counting completed assessments, the focus shifted to reducing time-on-task for users and increasing the percentage of fully automated responses (via AI), which directly proved value to the end customer.
- Sales Narrative Enhancement: I provided Sales with a new, powerful narrative focusing on "Frictionless Compliance." This allowed them to sell the future state of the product—one that eliminates user pain points like document upload security and notification overload—rather than selling the painful status quo.
- Engineering & UX/UI Team Discussion: I translated the qualitative user feedback into precise, prioritized technical requirements to ensure a robust and user-centric solution.
- UX/UI Technical Debt Resolution: I held deep-dive sessions with Engineering to address the specific UX/UI failures reported by users . This validated the need to overhaul the front-end architecture and build simpler, faster input components.

Result

My research successfully validated and prioritized the subsequent product roadmap, directly guiding R&D investment away from speculation toward verifiable user pain points. The findings led directly to the design and implementation of key projects focused on UX/UI efficiency, process friction elimination, and data automation (like the AI Parser), resulting in targeted improvements to user engagement and a measurable increase in supply chain data quality.